CONTINUUM ROBOTS WITH MULTI-SCALE MOTION THROUGH EQUILIBRIUM MODULATION

Abstract

A robotic system, such as a continuum robot, that includes at least one hollow tube backbone and an equilibrium modulation wire at least partially positioned within the backbone. The robotic system is configured to adjust a position of an end effector by bending the hollow tube and to further adjust the position of the robotic device by adjusting a linear insertion position of the equilibrium modulation wire inside the hollow tube, wherein adjusting the linear insertion position of the equilibrium modulation wire changes a flexural rigidity of the hollow tube resulting in a change in the resulting bending angle of the robotic device.

Description

BACKGROUND

The present invention relates to systems and methods for controlling the movement and positioning of a robotic device. In some particular implementations, the present invention relates to systems and methods for controlling the movement and positioning of a bending robotic device such as, for example, a continuum robot.

SUMMARY

In one embodiment, the invention provides a robotic system that includes at least one hollow tube backbone and an equilibrium modulation wire at least partially positioned within the backbone. The robotic system is configured to adjust a position of an end effector by bending the hollow tube and to further adjust the position of the robotic device by adjusting a linear insertion position of the equilibrium modulation wire inside the hollow tube, wherein adjusting the linear insertion position of the equilibrium modulation wire changes a flexural rigidity of the hollow tube resulting in a change in the resulting bending angle of the robotic device.

In other embodiments, the invention provides a robotic system configured to adjust a position of a robotic device by bending a backbone structure of a dexterous arm of the robotic device and to further adjust the position of the robotic device by adjusting a linear insertion position of an equilibrium modulation wire. The equilibrium modulation wire is coupled to the backbone structure of the dexterous arm at least partially along a length of the backbone structure such that adjusting the linear insertion position of the equilibrium modulation wire changes the flexural rigidity along the length of the backbone structure resulting in a change in the resulting bending angle of the robotic device. In some embodiments, the equilibrium modulation wire is coupled to the backbone structure by positioning the equilibrium modulation wire at least partially within an internal channel of the backbone structure. In other embodiments, the equilibrium modulation wire is coupled to the backbone structure by slidably constraining the equilibrium modulation wire along a side of the backbone structure.

In another embodiments, the invention provides a continuum robot system including a plurality of segments, each segment including an end disk, a base disk, a central backbone fixedly attached to the end disk, and a plurality of secondary backbones fixedly attached to the end disk. A bending angle and a bending plane of a first segment is controlled by adjusting a set of linear forces applied to the secondary backbones of the first segment and further modulated by adjusting an internal stored energy of at least one of the secondary backbones that causes a change in the flexural rigidity of the secondary backbone resulting in a change in the resulting bending angle of the first segment achieved by the set of linear forces applied to the secondary backbones of the first segment. In some embodiments, the central backbone is also fixedly attached to the base disk of the segment while, in other embodiments, the central backbone is slidably coupled to the base disk.

Other aspects of the invention will become apparent by consideration of the detailed description and accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a perspective view of a two-segment continuum robot according to one embodiment.

FIG. 2 is another perspective view of a three segment continuum robot of similar design as in FIG. 1 with a cross-sectional detailed view inset illustrating the macro- and micro-scale motion control mechanisms.

FIG. 3A is a schematic view of the continuum robot of FIG. 2 showing equilibrium modulation according to one embodiment.

FIG. 3B is a detailed schematic view of the effect of equilibrium modulation in movement control of one secondary backbone of the continuum robot of FIG. 3A.

FIG. 4 is a block diagram of a control system for the continuum robot of FIG. 2 according to one embodiment.

FIG. 5 is an elevation view of a continuum robot and an actuator system for the continuum robot utilizing the control system of FIG. 4.

FIG. 6 is a flowchart of a method for adjusting the position and controlling movement of a continuum robot using macro-scale and micro-scale movement control.

FIG. 7A is a schematic diagram of a bent snake segment of a continuum robot with an equilibrium modulating wire retracted from the backbone.

FIG. 7B is a schematic diagram of the bent snake segment of FIG. 7A with the equilibrium modulating wire inserted to a depth q_s.

FIG. 8 is a perspective view of a single segment of a continuum robot illustrated as two concatenated subsegments for a given micro-motion wire insertion depth.

FIG. 9 is a flowchart of an algorithm for a nonlinear LS estimation.

FIG. 10A is a schematic illustration of an entire segment of a continuum robot when an EMB is being inserted.

FIG. 10B is a schematic illustration of the tip of the continuum robot of FIG. 10A during micro motion under ideal conditions.

FIG. 10C is a schematic illustration of the tip of the continuum robot of FIG. 10A subject to a linear uncertainty function.

FIG. 11 is a graph illustrating a macro motion simulation and the micro motion Jacobian computed during the simulation.

FIG. 12A is a graph illustrating a simulation verifying derivations of the Jacobians under micro motion under ideal conditions.

FIG. 12B is a graph illustrating a simulation verifying derivations of the Jacobians under micro motion subject to a linear uncertainty function.

FIG. 12C is a graph illustrating a simulation verifying derivations of the Jacobians under micro motion with error propagation where perturbations were overlapped in simulated trajectory.

FIG. 13A is a perspective view of a single-segment continuum robot whose motion is captured by two cameras.

FIG. 13B is a side view of the experimental setup of FIG. 13A.

FIG. 13C is a microscope view of a segmented marker used in the experimental setup of FIG. 13A.

FIG. 14 is a graph of image-segmented tip positions under microscope.

FIG. 15A is a series of graphs illustrating experimental data and model iterations during parameter estimation for iterations of calibration that use full micro motion trajectory.

FIG. 15B is a series of graphs illustrating experimental data and model iterations during parameter estimation for iterations of calibration that use partial micro motion trajectory.

DETAILED DESCRIPTION

Before any embodiments of the invention are explained in detail, it is to be understood that the invention is not limited in its application to the details of construction and the arrangement of components set forth in the following description or illustrated in the following drawings. The invention is capable of other embodiments and of being practiced or of being carried out in various ways.

FIG. 1 illustrates an example of a continuum robot that includes a controllably bendable, snake-like body. The pose and position of the snake-like body of the continuum robot is controllably adjusted, for example, to position and operate a working tool mounted on the distal end of the snake-like body and can be controlled to reach target locations inside a cavity. For example, in surgical robot systems, a continuum robot may be used to move a working tool through a body cavity, orifice, or incision to a target site and to perform a surgical procedure at the target site. For example, a gripper, a camera-system, or, as described in further detail below, an optical coherence tomography (OCT) probe may be coupled to the distal end of the continuum robot's snake-like body for use in a medical procedure.

Examples of continuum robots and systems and methods for the control and operation of a continuum robot are described in detail in U.S. Publication No. 2014/0316434 to Simaan et al. and U.S. Publication No. 2014/0330432 to Simaan et al., the entire contents of which are incorporated herein by reference.

FIG. 1 shows an example of a continuum robot with multiple controllable segments. Although the example of FIG. 1 shows only two segments of a continuum robot (segment 101 and segment 102), a continuum robot can be implemented with any number of segments. The bending of each segment is controlled by a plurality of secondary backbones which control the bending angle of the segment and the plane in which the segment bends.

FIG. 2 illustrates the structure and control mechanism of the continuum robot 201 in further detail. As shown in the inset in FIG. 2, each individual segment 203 of the continuum robot 201 includes a base disk 1, a central backbone 2, a plurality of secondary backbones 3, an end disk 4, an equilibrium modulation wire 5 for each secondary backbone, and a plurality of spacer disks 6. The ends of the central backbone 2 are affixed to the base disk 1 and the end disk 4, respectively. In some implementations, the base disk 1 of one segment also serves as the end disk 4 of an adjacent segment. Furthermore, in some implementations, a single central backbone 2 runs through the entire length of the continuum robot 201 with a plurality of end disks/base disks affixed at locations along the central backbone 2. In some implementations, the spacer disks 6 are also fixed to locations along the central backbone 2.

Although, in the perspective of the inset illustrated in FIG. 2, only two secondary backbones 3 are visible, the example of FIG. 2 includes three secondary backbones 3 for each segment and other implementations may include more or fewer secondary backbones in each segment. The distal end of each secondary backbone 3 for the segment is affixed to the end disk 4 for that segment, but is not affixed to the spacer disks 6 or the base disk 1. Accordingly, the bending angle and the bending plane of the segment can be controlled by coordinated linear movement of the secondary backbones 3 for the segment. As shown in the inset of FIG. 2, when the secondary backbone 3 on the right side is pulled toward the proximal end of the continuum robot and the secondary backbone 3 on the left side is pushed toward the distal end of the continuum robot, the segment 203 bends to the right. By pulling and pushing on the secondary backbones, the continuum robot segment can be controllably bent to orient the end disk in two degrees of freedom. In this example, the central backbone 2 and all of the secondary backbones 3 are constructed of superelastic nickel titanium (NiTi) and the base disk and spacer disk maintain a constant spacing distance between the central backbone 2 and the secondary backbones 3.

In the example of FIG. 2, each secondary backbone 3 is formed as a hollow tube with an equilibrium modulation wire 5 positioned inside each secondary backbone 3. The equilibrium modulation wire 5 in this example is also constructed of superelastic NiTi and are controlled to slide inside the tubular secondary backbones 3. By changing the inserted length of these equilibrium wires 5, the distribution of flexural rigidity along the length of the continuum robot segment is altered—thereby resulting in indirect actuation of the end effector by a resulting change in its static equilibrium pose.

FIGS. 1, 3A, and 3B further illustrate and explain how rigidity and equilibrium of the backbones affects the pose and control of the continuum robot. As discussed above, the bending of each segment of the continuum robot is controlled by a plurality of secondary backbones as described further below. For each segment, two angles Θ and δ characterize the bending angle and the plane in which the segment bends. The vector Ψ=[Θ, δ]^T is used to designate the configuration of the segment. The pose (position orientation) of the end effector is represented by the vector x.

In the following sections we will use q∈Rⁿ to designate the active joint values responsible for macro motion control. The vector q⁻∈R^p is used to designate the micro-motion joint values responsible for equilibrium modulation. The mechanical energy of the continuum segment is given predominantly by its elastic energy because gravitational energy effects are negligible for small robots of the size in this example. Assuming n backbones in a continuum segment and using index i=0 to refer to its central backbone, the elastic potential energy is given by:

u=½Σ_i=0ⁿ∫₀^LiE_iI_iK_i(s)²ds  (1)

where K_i, E_i, I_i, and L_i designate curvature, Young's modulus, bending moment of inertia, and the length of the i'th backbone, respectively. The variable s stand for the arc-length parameter s∈[0, L_i]. We also note that the length of the i'th secondary backbones is given as a function of the joint variables L_i=L₀+q, i∈[1, n].

The shape of the continuum segment is given by k_o(s) since the shapes of the secondary backbones as given by k_i(s), i∈[1, n] are geometrically constrained to obey constant distance from the primary backbone. This shape is given as a solution to a variational problem minimizing the total elastic energy of the segment. In general, this shape is very close to circular (i.e., k₀ is approximately constant). An exact shape can be obtained using an elliptic-integral constrained optimization formulation.

The macro-motion direct kinematics (DK) of the robot illustrates in the examples of FIGS. 1 through 3B relates the active joint values with the corresponding end effector pose according to the equation:

x=f(ψ(q))  (2)

The instantaneous direct kinematics (IDK) relates the macro motion joint speeds with end effector twist according to the equation:

{dot over (x)}=g({dot over (ψ)}(q,{dot over (q)}))  (3)

In both cases the transition from joint space to task space variables is achieved by using the configuration space coordinates ψ and the configuration space speeds {dot over (ψ)}. Specifically, two Jacobian matrices are calculated in closed-form such that:

{dot over (x)}=J _xψ{dot over (ψ)}  (4)

{dot over (q)}=J _qψ{dot over (ψ)}  (5)

In macro actuation mode, the macro-motion actuator control q while the micro-motion actuators maintain {tilde over (q)} fixed. For a given value of {tilde over (q)}, the minimum energy bending shape of the continuum segment is given by minimizing the following equation:

u=½[Σ_i=0ⁿ∫₀^L0+qiE_iI_iK_i(s_i)ds_i+Σ_i=0ⁿ∫₀^{{tilde over (q)}i}{tilde over (E)}_iĨ_i{tilde over (K)}_i(s_i)²ds_i]  (6)

where k_i(s) designates the local curvature of the i'th backbone of the bent segment, E_i, I_i, and {tilde over (E)}_i, Ĩ_i are the Young's moduli and bending moments of inertia of the outer tube (macro-motion backbone) and the equivalent Young's modulus of the ñ micro-motion backbones in the portions where they overlap (s_i=0 . . . {tilde over (q)}_i).

To solve the problem of finding the equilibrium shape we will initialize our solution using the circular shape determined by the nominal solution of the inverse kinematics mapping. In addition, the solution must satisfy geometric and static equilibrium constraints leading to a constrained nonlinear minimization formulation:

minimize u=½[Σ_i=0ⁿ∫₀^L0+qiE_iI_iK_i(s_i)ds_i+Σ_i=0ⁿ∫₀^{{tilde over (q)}i}{tilde over (E)}_iĨ_i{tilde over (K)}_i(s_i)²ds_i]  (7)

Subject to: g(k)=0, where k=[k₁, . . . k_n]^T. where the constraint g(k) is the boundary constraints specific to the robot architecture and materials. A constrained nonlinear minimization algorithm can be used to find the approximate solution for k(s). The Augmented Lagrangian method or Active Set sequential programming method might also be used for the numerical solution by assuming a modal representation of k(s) such that:

$\begin{array}{cc}{K}_i\left(s_i\right)=\{\begin{array}{c}{\tilde{c}}_i^T\eta \left(s_i\right)\mathrm{for}s_i\le {\tilde{q}}_i\\ c_i^T\eta \left(s_i\right)\mathrm{for}{\tilde{q}}_i\le s_i\le L_i\end{array}& \left(8\right)\end{array}$

The outcome optimization algorithm will result in the modal coefficients {tilde over (c)}_i and c_i which define the curvature k_i(s_i). Given the curvature k_i(s_i), we will use differential geometry to define the Jacobians which will be used for resolved rates control according to equations (4) and (5) above.

In micro-motion control mode, the macro-motion joints q are locked while the micro-motion joints q⁻ are used to change the elastic energy and thereby change the equilibrium pose of the robot. Equation (6) shows that u=u (q, {tilde over (q)}). The complete differential u becomes:

du=(∇_qu)^Tdq+(∇_{{tilde over (q)}}u)^Td{tilde over (q)}=(∇_{{tilde over (q)}}u)^Td{tilde over (q)}  (9)

Equation (9) shows that, in micro motion mode, the change in elastic energy depends on the gradient of the energy with respect to the micro-motion degrees of freedom.

To capture the change in the shape of the backbones when {tilde over (q)} is changed, we use the modal representation in equation (8) and we express the unknown coefficients c_i and {tilde over (c)}_i as a function of q using the modal representation c_i=Λ_iϕ({tilde over (q)}) where Λ∈^x×y and ϕ({tilde over (q)})∈^y. This representation results in expressing the curvature as:

$\begin{array}{cc}{K}_i\left(s_i\right)=\{\begin{array}{c}{\tilde{\Phi }}^T{\tilde{\Lambda }}_i^T\eta \left(s_i\right)\mathrm{for}s\le {\tilde{q}}_i\\ {\Phi }^T{\Lambda }_i^T\eta \left(s_i\right)\mathrm{for}{\tilde{q}}_i\le s\le L_i\end{array}& \left(10\right)\end{array}$

The solution to the constrained energy minimization problem will give the modal coefficients A and k_i(s_i). Given the curvature k_i(s_i), the Jacobians that define the micro-motion kinematics can be defined using differential geometry.

To summarize, the degree of bending of the segment caused by linear movement is related to the rigidity of the central backbone, the secondary backbones, and the equilibrium modulation wires positioned inside the secondary backbones. As illustrated in FIG. 3A, when forces are applied to the secondary backbones 301, the segment and its corresponding end disk 303 bend until reaching an equilibrium position under the given set of applied forces. However, the degree to which the equilibrium modulation wire 305 is inserted and/or withdrawn from the secondary backbone affects the rigidity of the secondary backbone and, accordingly, the equilibrium pose of the segment. As illustrated in FIG. 3B, as the equilibrium modulation wire 305 is inserted or withdrawn into the secondary backbone 301, the equilibrium pose for the segment under the same set of applied forces will also change.

In the example of FIG. 3B, when the equilibrium modulation wire 305 is inserted into the secondary backbone at a depth indicated by the solid line, the applied forces cause the secondary backbone to be positioned at a first equilibrium pose 301a. However, as the equilibrium modulation wire is further inserted into the secondary backbone (as indicated by the dotted line), the rigidity of the secondary backbone increases causing the secondary backbone (and the segment of the continuum robot) to move from the first equilibrium pose 301a to a second equilibrium pose 301b.

Accordingly, in some implementations, macro-motion control is used as a primary mechanism for adjusting the pose of the continuum robot and/or an individual continuum robot segment by applying a linear force to push and/or pull the secondary backbones of a given segment. However, macro-motion control is limited in terms of resolution and precision. Therefore, micro-motion control is used to adjust the rigidity of one or more of the secondary backbones by advancing/retracting the equilibrium modulation wires causing the segment of the continuum robot to move from a first equilibrium pose into a second equilibrium pose. By adjusting the rigidity and the equilibrium of the continuum robot segment, the micro-motion control provides for fine tuning adjustments and control of the continuum robot and an increased level of position control and resolution for the continuum robot. Furthermore, just as the forces applied to the secondary backbones during macro-scale motion control are adjusted in coordination to achieve a target bending angle and bending plane for the segment, the linear positions of the equilibrium modulation wires in each of the secondary backbones are also controlled and adjusted in coordination to achieve a target bending angle adjustment and bending plane adjustment under micro-motion control.

FIG. 4 illustrates an example of a control system for implementing the macro/micro motion and pose control described above. The control system includes an electronic processor 401 and a memory 403 storing data and instructions that are executed by the electronic processor 401 to control the operation of the control system. The electronic processor 401 is also communicatively coupled to a continuum robot actuator 405 (i.e., the macro-scale motion actuator), a microscale manipulation (MSM) actuator 407, a user input control 409, an end effector 411, and, in some implementations, a camera system 413.

The macro-scale motion actuator 405 includes a plurality of linear slide actuators that each apply a linear force to a respective one of the secondary backbones based on an output instruction/signal from the electronic processor 401. The micro-scale manipulation actuator 407 also includes a series of linear slide actuators that are each coupled to a respective one of the equilibrium modulation wires based on an output instruction/signal from the electronic processor 401. The electronic processor 401 is configured to generate the output instruction/signals to the macro-scale motion actuator 405 and to the micro-scale manipulation actuator 407 based in part on a user adjustment of the user input control 409 (e.g., a joystick or other input device).

As discussed above, the continuum robot can be equipped with a working tool at its distal end. Accordingly, the end-effector 411 can include, for example, a gripper, a laser device, or an imaging device such as an OCT probe. In some implementations, the continuum robot may be equipped with a camera system positioned at its distal end in addition to or instead of an end-effector 411/working tool.

FIG. 5 illustrates an example of a continuum robot 501 equipped with the actuators and control system of FIG. 4. The continuum robot 501 is shown extending from an endoscope 503. The macro-scale motion actuator 405 is coupled to the continuum robot at the proximal end of the endoscope 503. The micro-scale manipulation actuator 407 is positioned on the opposite end of the macro-scale motion actuator 405 and, accordingly, is configured to retract or extend the equilibrium modulation wires into the secondary backbones of the continuum robot 501 through the macro-scale motion actuator 405, which is configured to adjust the linear forces applied to the secondary backbones.

In this example, the micro-scale manipulation actuator 407 includes three automated linear slides. The linear slides in this example are VELMEX A1506B-S1.5 linear slides equipped with a 4.5″ 20 turns/inch travel lead screw and are driven by a Maxon Motor RE16 4.5W equipped with planetary gearhead GP16A 19:1 and an MR encoder with 256 counts per turn.

FIG. 6 illustrates an example of a method implemented by the control system of FIG. 4 for controllably adjusting the pose and motion of the continuum robot of FIG. 2. The electronic processor 401 monitors an input signal from a user control 409 indicative of an input adjustment command (step 601). In response to receiving an input adjustment command, the control system determines a target end pose for the continuum robot based on the current pose of the continuum robot and the input adjustment command (step 603). The control system then determines target movements for the continuum robot to move from its current position to the target end position (step 605). In some implementations, this includes determining a range of fine-tuning adjustments of the pose that can be achieved by micro-scale manipulation (i.e., further inserting or retracting the equilibrium modulation wires). The system then determines a target pose for the continuum robot that can be achieved with macro-scale control and that is within the determined range of fine-tuning available through micro-scale manipulation. The system then determines (1) the linear movements of the secondary backbones that will cause the segment of the continuum robot to move into the target macro-scale pose and (2) the linear position of the equilibrium modulation wires that would then cause the segment of the continuum robot to move from its “macro-scale” target into its final target pose.

After the position adjustments for the secondary backbones and the linear position adjustments for the equilibrium modulation wires are determined, the electronic processor 401 actuates the macro-scale motion control of the continuum robot by causing the macro-scale motion actuator 405 to apply the determined set of forces to the secondary backbones (step 607). Once the macro-scale adjustment is complete, the electronic processor 401 actuates the micro-scale motion control by causing the micro-scale manipulation actuator 407 to adjust the linear positions of the equilibrium modulation wires (step 609).

In the example of FIG. 6, macro-scale control and micro-scale control are performed as two separate steps (first, macro-motion control and then micro-motion control only after the macro-scale adjustment). However, in other implementations, the control system may be configured to concurrently adjust the forces applied to the secondary backbones and the linear positioning of the equilibrium modulation wires.

In some implementations, the system is configured to move adjust the pose of the continuum robot using macro-scale motion control until the system determines that the pose of the continuum robot is close enough to the target pose that the target pose can be achieved by micro-scale control. In some such implementations, this check is performed between step 607 and step 609 in the example of FIG. 6.

In still other implementations, the control system may be configured to perform the micro-scale motion control based on feedback after the macro-scale motion adjustment is performed. For example, instead of determining target adjustments to the linear position of the equilibrium modulation wires before applied the macro-scale adjustment, the control system may be configured to first apply the macro-scale motion adjustment and then determine an error between the macro-scale adjusted pose and the actual target pose (using, for example, feedback from a camera system 413 or other position sensors). The control system would be configured to then further adjust the pose of the continuum robot using micro-scale control based on the determined position error until the error is reduced to below a defined acceptable threshold. In some implementations, the system is configured to use data collected by one or more sensors coupled to the distal end of the continuum robot for the feedback mechanism including, for example, image data from an OCT probe or other vision/camera sensor.

Finally, although the examples discussed above include equilibrium modulation wires in each of the secondary backbones, other implementations may include more or fewer equilibrium modulation wires. For example, a system that includes 6 secondary backbones for each segment may only include equilibrium modulation wires in three of the six secondary backbones. Conversely, some implementations may be configured to include an equilibrium modulation wire in the central backbone to adjust the rigidity of the continuum robot in addition to or instead of the equilibrium modulation wires in the secondary backbones.

Furthermore, although the examples described above achieve micro-scale motion control through equilibrium modulation by adjusting a linear position of an equilibrium modulation wire within an internal channel of a backbone structure, other mechanisms can be implemented to attain equilibrium modulation by adjusting the stored energy of the system in other implementations. For example, in some implementations, the equilibrium modulation wire can be slidably coupled to the side of a backbone and moved linearly to adjust the flexure rigidity. In other implementations, the system may be configured to apply thermal energy (e.g., heat) to the backbone structure to adjust its rigidity and the resulting equilibrium position. In still other implementations, the system may be configured to adjust a pressure of a fluid or gaseous medium within an internal chamber along the length of the backbone structure to adjust its rigidity and the resulting equilibrium position.

As discussed above, a continuum robot with macro/micro scale motion control can be integrated into a medical imaging system such as, for example, an OCT system. For example, a B-mode OCT probe may be coupled to the distal end of the continuum robot's “snake-like” body. For example, an OCT probe may be constructed with an outer diameter of 0.66 mm and configured to fit through the central backbone of the continuum robot. The OCT system may also include, for example, a spectral-domain optical coherence tomography system (e.g., a 870 nm system by Bioptigen, Inc., Morrisville, N.C.) coupled to the OCT probe and used to generate a synchronization signal, for image acquisition, and for generation of the B-mode images.

In some B-mode OCT systems, the probe is configured to perform lateral scanning by pivoting a mirror. However, lateral scanning can be performed without a pivoting mirror by instead using an OCT probe coupled to the distal end of a continuum robot with macro/micro scale motion control as described above. For example, lateral scanning for OCT can be achieved with sufficient speed and positioning control by bending a segment (e.g., the distal segment) of the continuum robot using macro-scale control and then adjusting the bend angle while capturing data with the OCT probe by adjusting the linear position of the equilibrium modulation wire(s). As discussed above, the change in equilibrium will cause small, precision-controlled movements of the distal end of the continuum robot that are suitable for OCT scanning.

In some implementations, the system may be configured to provide back-and-forth reciprocating movements of the distal end of the continuum robot (e.g., for OCT image data capture or other procedures using other end effectors/working tools) by alternatingly inserting and withdrawing one or more of the equilibrium modulation wires between two defined linear positions.

The examples described above present the concept of continuum robot equilibrium modulation (CREM) and provide a visual measurement solution to observe macro-motion. The examples below outline a modeling approach that can explain the experimental observations and that can be used for control and identification purposes. These examples present a simplified kinematic modeling framework that captures the micro-motion achieved by the equilibrium modulation of continuum robots and develop a calibration approach to capture the model parameters. The concept of “moment coupling effect” is presented as a simplified approach to describe the equilibrium modulation behavior and, therefore, both direct kinematics and instantaneous kinematics are formulated for control purposes. To account for errors potentially caused by the simplistic modeling assumptions, a modeling uncertainty term is introduced, and the identification Jacobian along with a calibration framework to capture the parameterization is developed. Using the multi-backbone continuum robot design as a validation platform, we validate the kinematic model and model calibration experimentally while augmenting these results with additional simulation validations.

This section presents the bending shape equilibrium modeling in the case where the Equilibrium Modulation Backbone (EMB) insertion is at a given depth q_s. When the EMBs are not inserted and for proper design parameters (e.g. small spacing between the spacer disks) the continuum segment bends in a constant curvature. We use this assumption to create a simplistic equilibrium model which lends itself to fast realtime computation. Since we have to account for modeling uncertainties due to friction and material parameter uncertainties, we later lump the error of the simplified model in an uncertainty term X, that will be used to produce an updated CREM model.

FIGS. 7A and 7B shows the free body diagram of a continuum segment with and without an inserted EMB. In this example, the continuum segment includes an end disk 701, an empty subsegment 702, a separation plane at EMB insertion depth q_s 703, an inserted subsegment 704, and a base disk 705.

In FIG. 7B, a separation plane 703 is defined at the insertion depth q_s, dividing the segment into two subsegment—Inserted and Empty. Though not accurate, the two subsegments are both assumed to have constant but different curvatures. The angles θ′ and θ_s denote the bending angles of the end-disk and at the insertion depth, respectively, when the EMB is inserted. The angle θ denotes the nominal bending angle when the EMB is not inserted. The angle θ₀=π/2 denotes the angle at the base of the segment.

We first consider the resultant moment m₁ that the backbones apply on any imaginary cross section of the continuum segment when no EMB is inserted (FIG. 7A):

$\begin{array}{cc}{m}_1=E_pI_p\frac{\theta -{\theta }_0}{L}+\sum _iE_iI_i\frac{\theta -{\theta }_0}{L_i}& \left(11\right)\end{array}$

where E_p, E_i and I_p, I_i denote the Young's moduli and cross-sectional bending moments of inertia of the central backbone and the i^th secondary backbone, respectively. Also L and Li denote the lengths of the central backbone and the i^th secondary backbone.

We also consider the moment m₁′ along the empty subsegment in the case of EMB being inserted (FIG. 7B):

$\begin{array}{cc}{m}_1^{\prime }=E_pI_p\frac{{\theta }^{\prime }-{\theta }_s}{L-q_s}+\sum _iE_iI_i\frac{{\theta }^{\prime }-{\theta }_s}{L_{{\varepsilon }_i}}& \left(12\right)\end{array}$

where L_εi denotes the i^th backbone length portion that belongs to the empty subsegment (this is the arc-length from the separation plane until the end-disk).

The radial distance between the secondary backbones and the primary backbone is denoted r. When r is projected onto the plane in which a segment bends, we obtain the projected radial distance Δ_i:

Δ_i=r cos(σ_i), σ_i=δ+(i−1)β  (13)

where σ_i designates the angular coordinate of the i^th backbone relative to the bending plane. The angular coordinate of the first backbone relative to the bending plane is δ and the angular separation between secondary backbones is

$\beta =\frac{2\pi }{n}$

where n is the number of secondary backbones.

The length of the i^th backbone, L is derived using the fixed radial offset between the backbones:

L _i =L+Δ _i(θ−θ_i)  (14)

Using similar rationale, we calculate the empty length portion L_εi and the inserted length portion of the i^th secondary backbone L_si:

L _{s i} =q _s+Δ_i(θ_s−θ₀)  (15)

L _{s i} =(L−q_s)+Δ_i(θ′−θ_s)=L_i−L_si  (16)

In both FIGS. 7A and 7B, the static equilibrium at the end-disk is determined by the geometric constraints and the backbone loading forces at the end-disk. For example, coordinated pulling and pushing on all secondary backbones are assumed to form a force couple that generates only a moment at the end-disk.

We next use a simplifying assumption that the effect of EMB wire insertion on changes in the bending curvatures of the un-inserted subsegment backbones is negligible, hence:

m ₁ =m ₁′  (17)

Next, we consider m₂ and m_s, the moments that the secondary backbones and the EMB apply on the separation plane as shown in FIG. 7B:

$\begin{array}{cc}{m}_2=-\left(E_pI_p\frac{{\theta }_s-{\theta }_0}{q_s}+\sum _iE_iI_i\frac{{\theta }^{\prime }-{\theta }_s}{L_{s_i}}\right)& \left(18\right)\\ m_s=-E_sI_s\frac{{\theta }_s-{\theta }_0}{q_s}& \left(19\right)\end{array}$

where E_s and I_s denote the Young's modulus and cross-sectional bending moment of inertia of the EMB.

Substituting equations (11, 12) into (17), results in one equation having two unknowns θ′ and θ_s as illustrated in FIG. 7B. To obtain the second equation necessary for solving for these two unknowns, we use the moment balance on the separation plane:

m ₁ ′+m ₂ +m _s=0(20)

To solve equations (20) and (17) for the unknowns θ′ and θ_s we explicitly express the backbone moments using the beam equation m=EIk where k designates the radius of curvature and EI designates the bending cross sectional stiffness of a beam. In doing so, we note that the curvature of a beam bent in a circular shape satisfies

$k=\frac{\theta }{L}$

where θ is the deflection angle and L is the beam length. Since the backbone lengths are a function of the unknowns, we will rewrite the moment equation for a beam as

$m=\frac{\mathrm{EI}}{L}\theta$

and, by defining the beam angular deflection stiffness

$k_{\theta }\stackrel{\Delta }{=}\frac{\mathrm{EI}}{L}$

we obtain a simple equation for the moment m=k_θθ.

Using the above definition for beam angular deflection stiffness, we rewrite the moment equations for each beam as:

$\begin{array}{cc}{m}_1=k_{{\theta }_0}\left(\theta -{\theta }_0\right),k_{{\theta }_0}=\frac{E_pI_p}{L}+\sum _i\frac{E_iI_i}{L_i}& \left(21\right)\\ m_1^{\prime }=k_{{\theta }_1}\left({\theta }^{\prime }-{\theta }_s\right),k_{{\theta }_1}=\frac{E_pI_p}{L-q_s}+\sum _i\frac{E_iI_i}{L_{{\varepsilon }_i}}& \left(22\right)\\ m_2=-k_{{\theta }_2}\left({\theta }_s-{\theta }_0\right),k_{{\theta }_2}=\frac{E_pI_p}{q_s}+\sum _i\frac{E_iI_i}{L_{s_i}}& \left(23\right)\\ m_s=-k_{{\theta }_s}\left({\theta }_s-{\theta }_0\right),k_{{\theta }_s}=\frac{E_sI_s}{q_s}& \left(24\right)\end{array}$

Substituting equation (22)-(24) in equation (20) results in:

$\begin{array}{cc}{\theta }_s=\frac{k_{{\theta }_1}{\theta }^{\prime }+k_{{\theta }_2}{\theta }_0+k_{{\theta }_s}{\theta }_0}{k_{{\theta }_1}+k_{{\theta }_2}+k_{{\theta }_s}}& \left(25\right)\end{array}$

Substituting equation (21) and (22) in equation (17) results in:

$\begin{array}{cc}{\theta }^{\prime }=\frac{k_0}{k_1}\left(\theta -{\theta }_0\right)+{\theta }_s=f_{{\theta }^{\prime }}\left({\theta }_s\right)& \left(26\right)\end{array}$

As a final step in the solution, we substitute the result in equation (26) in equation (25), thereby obtaining θ_s and subsequently θ′.

Equations (25) and (26) present the solution to the simplistic modeling approach that is fundamentally based on Eq. (17) and Eq. (20). In addition to the simplified assumption, the current model also neglects modeling uncertainties due to frictional effects and material property uncertainties. These uncertainties include friction and strain along the actuation lines, non-uniformly distributed load on backbones that causes shape deviations from constant-curvature bending, deviations in the cross section of the backbones during bending, and uncertainties in the properties of the NiTi backbones.

To account for the modeling uncertainties caused by friction, material uncertainty, and our simplistic model, we introduce an uncertainty term λ to equation (20):

m ₁ ′+m ₂ +m _s=λ(q_s,θ,k_λ)  (27)

The uncertainty term λ captures effects of EMB insertion offset, bending angle uncertainty and a fixed offset:

λ(q_s,θ,k_λ)=k_λ0+k_λθθ+k_λqq_s; k_λ≙[k_λ0,k_λq,k_λθ]^T  (28)

The solution in equation (25) is also updated to:

$\begin{array}{cc}{\theta }_s=\frac{k_1{\theta }^{\prime }+k_2{\theta }_0+k_s{\theta }_0-\lambda }{k_1+k_2+k_s}& \left(29\right)\end{array}$

Having obtained the solutions to the equilibrium tip bending angle θ′ and the bending angle at the separation plane θ_s, we introduce an equilibrium configuration space variable vector Φ to combine them. With the purpose of preparing for kinematic derivations in later sections when we break a single continuum segment down to two subsegments, the vector Φ is defined as:

$\begin{array}{cc}\phi \stackrel{\Delta }{=}{\left[{\theta }_s,{\theta }_{\varepsilon }\right]}^T,{\theta }_{\varepsilon }\stackrel{\Delta }{=}{\theta }^{\prime }+\left(\frac{\pi }{2}-{\theta }_s\right)& \left(30\right)\end{array}$

where θ_ε represents the bending angle of the empty subsegment.

We define the configuration space variable w as the nominal bending angle θ and the bending plane angle δ:

ψ≙[θ,δ]^T  (31)

Finally, the solution to equilibrium modeling problem is presented as a mapping ℑ_eqm which is used in deriving the Jacobian matrices in the following sections:

ϕ=ℑ_eqm(ψ,q_s,k_λ), ϕ∈²,ψ∈²,k_λ∈³  (32)

Equation (32) provides the end disk equilibrium angle for a combination of any given EMB insertion length q_s, nominal bending angle θ, and bending plane angle δ.

Kinematic modeling of CREM includes the mapping of configuration space to task space and its differential kinematics. The differential kinematics include the instantaneous kinematics and the error propagation.

The instantaneous kinematics is derived for control purpose, and it includes two motion Jacobian matrices that both relate actuation speeds to the robot tip velocity. The macro motion Jacobian J_M is associated with the joint velocities of push/pull on backbones (direct actuation) while the micro motion Jacobian J_μ is related to the EMB insertion velocity (indirect actuation).

The kinematic error propagation investigates how errors in parameters contribute to errors in kinematic measurements of task space (e.g. measured positions). In this work, we focus on the vector k_λ that parameterizes the modeling uncertainty. An identification Jacobian J_k is derived and used in the discussion below to estimate k_λ with experimental measurements.

With the mapping ℑ_eqm in equation (32) derived as the result of static equilibrium, the kinematic mapping can be formulated by considering a single continuum segment as two concatenated subsegments—the inserted and the empty, divided at the insertion depth q_s. FIG. 8 illustrates our approach to analyzing the two concatenated subsegments. The bending angles of both subsegments were introduced in equation (30), denoted as θ_s and θ_ε, for the inserted and the empty subsegment. Since the whole segment is assumed to bend in plane, both subsegments have the equal bending plane angles:

δ_s=δ_ε=δ  (33)

The kinematic nomenclature used in FIG. 8 is outlined in Table 1.

TABLE 1
Symbol       Description
Frame {F}    Designates a right-handed frame with unit vectors {circumflex over (x)}f, ŷf, {circumflex over (z)}f and point f as origin.
Frame {B}    The base disk frame with b located at the center of the base disk, {circumflex over (x)}b
             passing through the first secondary backbone and {circumflex over (z)}b perpendicular to the
             base disk.
Frame {1}    Frame of the bending plane having {circumflex over (z)}1 = {circumflex over (z)}b  and {circumflex over (x)}1 passing through with
             the project point of the end disk center. The angle δ is defined as from
             {circumflex over (x)}1 to {circumflex over (x)}b about {circumflex over (z)}b according to right hand rule.
Frames {E} & Frame {E} defined with {circumflex over (z)}e,as the normal to the end disk and {circumflex over (x)}e, is the
{G}          intersection of the bending plane and the end disk top surface. Frame {G}
             is the gripper frame that has the same z as {E}, i.e., {circumflex over (z)}g = {circumflex over (z)}e, but {circumflex over (x)}g is
             passing through the first secondary backbone. It can be obtained by a
             rotation angle (−σ1e) about {circumflex over (z)}e.
Frames{P} &  These frame are defined in a manner similar to the definition of frames
{C}          {E} & {G} but for a specific arc insertion length qs as opposed to the full
             length of the robot segment L. The {circumflex over (x)}c − ŷc plane is the insertion plane as
             shown in the planar case in FIGS. 7A and 7B.
Frame {I}    Designates the microscope image frame having the origin at the corner of
             the image and having its x-y axes aligned with the width and height
             directions (FIG. 13A and 13C).
Frame {M}    Designates the marker frame that is determined by segmentation of the
             microscope image (FIG. 13C).
Vector xpa/b Designates the position of point a relative to point b that is expressed in
             frame {X}.

Because the direct kinematics of a single segment have a length L_x and an end disk angle θ_x, the end disk pose (i.e., position and orientation) relative to the base are given by:

$\begin{array}{cc}p_{\mathrm{end}-\mathrm{disk}/\mathrm{base}}=\frac{L_x}{{\theta }_x-\pi /2}\left[\begin{array}{c}\cos {\delta }_x\left(\sin {\theta }_x-1\right)\\ -\sin {\delta }_x\left(\sin {\theta }_x-1\right)\\ -\cos {\theta }_x\end{array}\right]& \left(34\right)\\ R_{\mathrm{end}-\mathrm{disk}}=e^{-{\delta }_x\left[z^{^}\right]}e^{\left(\frac{\pi }{2}-{\theta }_x\right)\left[y^{^}\right]}e^{{\delta }_x\left[z^{^}\right]}& \left(35\right)\end{array}$

where δ_x designates the angle of the bending plane (analogous to δ in FIG. 8), [v{circumflex over ( )}] represents the cross-product matrix of vector v and the matrix exponential e^{α[v{circumflex over ( )}]} represents a rotation matrix about the axis v by an angle α.

To obtain the pose of the end disk of the inserted segment is given by ^bp_c/b and ^bR_c we substitute L_x=q_s, θ_x=θ_s, δ_x=δ in equations (34), (35). Similarly, the pose of the end disk of the empty segment relative to its base is obtained by substituting L_x=L−q_s, θ_x=θ_ε, δ_x=δ in equations (34), (35) to result in ^cp_g/c and ^cR_g.

The pose of the free subsegment end disk relative to the segment base is given by:

^b p _g/b=^bp_c/b=^bR_c^cp_g/c≙ℑ_g(ϕ,δ,q_s)  (36)

^b R _g=^bR_c^cR_b=R_g(ϕ,δ)  (37)

Casting the above two equations in a homogeneous transform format yields:

$\begin{array}{cc}T_g=\left[\begin{array}{cc}\hspace{0.17em}R_g& p_{g/b}\\ 0& 1\end{array}\right]\stackrel{\Delta }{=}{}_g\left(\phi ,\delta ,q_s\right)& \left(38\right)\end{array}$

With Φ expressed using mapping ℑ_eqm(ψ, q_s, k_λ), the forward kinematics is determined, which can be also written as:

^b T _g≙ℑ_T(ψ,q_s,k_λ)  (39)

The total differential of a homogenous transform T∈SE(3), may be represented as:

$\begin{array}{cc}d\xi \stackrel{\Delta }{=}{\left[{\left(dx\right)}^T,{\left(d\mu \right)}^T\right]}^T,d\xi \in {ℝ}^{6\times 1}& \left(40\right)\\ \mathrm{dx}\stackrel{\Delta }{=}d\left(p_{\frac{g}{b}}\right),d\mu \stackrel{\Delta }{=}{\left[d{\mu }_x,d{\mu }_y,d{\mu }_z\right]}^T& \left(41\right)\end{array}$

where dx and dμ represent translational and rotational differentials in the base frame. The vector μ∈^3×1 represents a chosen orientation parametrization (e.g., Euler angles).

The total differential of ^bT_g is obtained by considering differentials on all variables, i.e., dΦ, dδ, and dq_s:

$\begin{array}{cc}d\xi =\frac{d\xi }{d\phi }d\phi +\frac{d\xi }{d\delta }d\delta +\frac{d\xi }{{\mathrm{dq}}_s}{\mathrm{dq}}_s& \left(42\right)\end{array}$

Using the nomenclature of a Jacobian J_ab such that δ_a=J_abδ_b, we define the following Jacobian matrices:

$\begin{array}{cc}\frac{d\xi }{d\phi }\stackrel{\Delta }{=}J_{\mathrm{\xi \phi }}\in {ℝ}^{6\times 2},\frac{d\xi }{d\delta }\stackrel{\Delta }{=}J_{\mathrm{\xi \delta }}\in {ℝ}^6,\frac{d\xi }{{\mathrm{dq}}_s}\stackrel{\Delta }{=}J_{\xi q_s}\in {ℝ}^6& \left(43\right)\end{array}$

The Jacobian matrices J_ξϕ, J_ξδ, and J_ξqs, respectively, relate the differential on equilibrium configuration space variable dϕ=[dθ_s,dδ_ε]^T, the differential on bending plane dδ, and differential on EMB insertion depth dq_s, to the corresponding differential contributions on the pose vector dξ. Both J_ξϕ and J_ξδ can be obtained by treating the inserted and empty subsegments as a concatenated two-segment robot (as discussed further below).

The third Jacobian, J_ξqs, defined as the partial derivative, dξ/dq_s, is derived with the other variables (Φ and δ) held constant. The end-effector orientation, given by ^bR_g in equation (37) is not a function of q_s. Therefore, by considering only the translational differential due to dq_s, we have:

$\begin{array}{cc}{J}_{\xi q_s}=\left[\begin{array}{c}\frac{dp_{c/b}}{{\mathrm{dq}}_s}+R_c\frac{dp_{g/c}}{{\mathrm{dq}}_s}\\ 0_{3\times 1}\end{array}\right]& \left(44\right)\end{array}$

where

$\frac{dp_{c/b}}{{\mathrm{dq}}_s}\mathrm{and}\frac{dp_{g/c}}{{\mathrm{dq}}_s}$

are derived from equation (34). It is important to note that J_ξqs differs from the micro motion Jacobian J_μ derived later in that J_ξqs is a contributing part of J_μ—the length “tangential” contribution, while dq_s also propagates dΦ that also causes change on dξ.

Having derived the above three Jacobian matrices, J_ξϕ, J_ξδ, and J_ξqs, we obtain the pose total differential d expressed using differentials dΦ, dδ, and dq_s. Further, the differential dΦ is a result of multiple other differentials, which can be seen from mapping ℑ_eqm(ψ, q_s, k_λ). To fully investigate and decouple the contributions of direct (macro) and indirect (micro) actuation, we express dΦ using differentials on (ψ, q_s, k_λ). This differential is also motivated by equation (39), where the variables are decoupled as ψ for macro motion, q_s for micro motion, and k_λ for micro motion parameters. Such differentiation is derived as:

$\begin{array}{cc}d\phi =\frac{d\phi }{d\psi }d\psi +\frac{d\phi }{{\mathrm{dq}}_s}{\mathrm{dq}}_s+\frac{d\phi }{{\mathrm{dk}}_{\lambda }}{\mathrm{dk}}_{\lambda }& \left(45\right)\\ \frac{d\phi }{d\psi }=\left[\frac{d\phi }{d\theta },\frac{d\phi }{d\delta }\right],\frac{d\phi }{{\mathrm{dk}}_{\lambda }}=\left[\frac{d\phi }{{\mathrm{dk}}_{{\lambda }_0}},\frac{d\phi }{{\mathrm{dk}}_{{\lambda }_{\theta }}},\frac{d\phi }{{\mathrm{dk}}_{{\lambda }_q}}\right]& \left(46\right)\end{array}$

where the gradient terms are derived, as discussed below, as:

$\begin{array}{cc}\frac{d\phi }{d\theta }={\left({\mathrm{AS}}_0-{\Gamma }_{{\theta }_s}S_1\right)}^{-1}{\Gamma }_{\theta }\stackrel{\Delta }{=}J_{\mathrm{\phi \theta }}& \left(47\right)\\ \frac{d\phi }{d\delta }={\left({\mathrm{AS}}_0-{\Gamma }_{{\theta }_s}S_1\right)}^{-1}{\Gamma }_{\delta }\stackrel{\Delta }{=}J_{\mathrm{\phi \delta }}& \left(48\right)\\ \frac{d\phi }{{\mathrm{dq}}_s}={\left({\mathrm{AS}}_0-{\Gamma }_{{\theta }_s}S_1\right)}^{-1}{\Gamma }_{q_s}\stackrel{\Delta }{=}J_{\phi q_s}& \left(49\right)\\ \frac{d\phi }{{\mathrm{dk}}_{{\lambda }_i}}={\left({\mathrm{AS}}_0-{\Gamma }_{{\theta }_s}S_1\right)}^{-1}B_{k_{{\lambda }_i}}^{\prime }\stackrel{\Delta }{=}J_{\phi k_{{\lambda }_i}},\frac{d\phi }{{\mathrm{dk}}_{\lambda }}\stackrel{\Delta }{=}J_{\phi k_{\lambda }}& \left(50\right)\\ C_{\phi }\stackrel{\Delta }{=}S_0\phi -C_0,{\Gamma }_x=B_x^{\prime }-A_x^{\prime }C_{\phi }& \left(51\right)\end{array}$

Matrices A_x′ and B_x′ are partial derivative matrices with respect to variable “x”, and A, S₀, B, C₀, and S₁ are defined as:

$\begin{array}{cc}A=\left[\begin{array}{cc}{k}_1+k_2+k_s& -k_1\\ k_1& -k_1\end{array}\right],S_0=\left[\begin{array}{cc}1& 0\\ 1& 1\end{array}\right]& \left(52\right)\\ B=\left[\begin{array}{c}\left(k_2+k_s\right){\theta }_0-\lambda \\ k_0\left({\theta }_0-\theta \right)\end{array}\right],C_0=\left[\begin{array}{c}0\\ {\theta }_0\end{array}\right],S_1=\left[\begin{array}{cc}1& 0\end{array}\right]& \left(53\right)\end{array}$

Using equations (47)-(53), dΦ is fully expressed as equation (45). Substituting dΦ into the original differentiation in equation (42), we obtain the full differential kinematics that relates differentials on (ψ, q_s, k_λ) to the pose total differential d:

$\begin{array}{cc}d\xi =\frac{d\xi }{d\phi }\frac{d\phi }{d\theta }d\theta +\frac{d\xi }{d\phi }\frac{d\phi }{d\delta }d\delta +\frac{d\xi }{d\phi }\frac{d\phi }{{\mathrm{dq}}_s}{\mathrm{dq}}_s+\frac{d\xi }{d\phi }\frac{d\phi }{{\mathrm{dk}}_{\lambda }}{\mathrm{dk}}_{\lambda }+\frac{d\xi }{d\delta }d\delta +\frac{d\xi }{{\mathrm{dq}}_s}{\mathrm{dq}}_s& \left(54\right)\end{array}$

Rewriting equation (55) using the Jacobian definitions:

dξ=J _ξϕ J _ϕθ dθ+J _ξϕ J _ϕδ dδ+J _ξδ dδ+J _ξϕ J _{ϕq s} dq _s +J _{ξq s} dq _s +JξϕJ _{ϕk λ} dk _λ  (55)

Collecting like terms of dψ, dq_s, and dk_λ, we obtain a differentiation that decouples differentials of the macro motion, the micro motion, and the parameters:

$\begin{array}{cc}d\xi =\underset{\underset{\stackrel{\Delta }{=}J_{M\psi }}{}}{\left[J_{\mathrm{\xi \phi }}J_{\mathrm{\phi \theta }}J_{\mathrm{\xi \phi }}J_{\mathrm{\phi \delta }}+J_{\mathrm{\xi \delta }}\right]}d\psi +\underset{\underset{\stackrel{\Delta }{=}J_{\mu }}{}}{\left[J_{\mathrm{\xi \phi }}J_{\phi q_s}+J_{\xi q_s}\right]}{\mathrm{dq}}_s+\underset{\underset{\stackrel{\Delta }{=}J_k}{}}{J_{\mathrm{\xi \phi }}J_{\phi k_{\lambda }}}{\mathrm{dk}}_{\lambda }& \left(56\right)\end{array}$

The above result completes the mapping from configuration to task space. It clearly delineates the effects of EDM insertion and direct actuation to achieving macro and micro motion. For control purposes, a complete mapping from joint to task space is needed. We therefore consider next the mapping from direct (macro) actuation joint space q to task space ξ. Since three secondary backbones are used in our experimental setup as illustrated in FIG. 8, we will define q≙[q₁, q₂, q₃]^T where:

q _i ≙L _i −L  (57)

When obtaining this mapping, we consider the nominal segment kinematics for multi-backbone continuum robots.

The Jacobian that relates the differential dq to the differential dψ is:

$\begin{array}{cc}\mathrm{dq}\stackrel{\Delta }{=}J_{q\psi }d\psi ,J_{q\psi }=r\left[\begin{array}{cc}{c}_{\delta }& \left({\theta }_0-\theta \right)s_{\delta }\\ c_{\left(\delta +\beta \right)}& \left({\theta }_0-\theta \right)s_{\left(\delta +\beta \right)}\\ c_{\left(\delta +2\beta \right)}& \left({\theta }_0-\theta \right)s_{\left(\delta +2\beta \right)}\end{array}\right]& \left(58\right)\end{array}$

where r denotes the constant radial distance between the central and surrounding backbones, and β=2π/3 denotes the backbone separation angle. Using equation (58), we substitute div into equation (56), arriving at the final differential kinematics:

dξ=J _M dq+J _μ dq _s +J _k dk _λ  (59)

Equation (59) fully decouples the end-effector pose differential to contributions of the direct (macro) actuation dq, the indirect (micro) actuation dq_s, and the modeling uncertainty dk_λ. The three Jacobian matrices are obtained from equation (59): J_M defined as the macro motion Jacobian, J_μ defined as the micro motion Jacobian, and J_k defined as the Identification Jacobian.

J _M=[J_ξϕJ_ϕθJ_ξϕJ_ϕδ+J_ξδ](J_qψ)^†, J_M∈^6×2  (60)

J _μ =J _ξϕ J _{ϕq s} +J _{ξq s} , J _μ∈^6×1  (61)

J _k =J _ξϕ J _{ϕk λ} , J _k∈^6×nk  (62)

where (*)^† is the Moore-Penrose pseudo inverse.

The result in equation (59) builds on knowing the Jacobians J_ξϕ∈^6×2 and J_ξδ∈^6×1 as mentioned in equations (42) and (43). We now provide a derivation to these two Jacobians. Considering a single-segment CREM as two concatenated subsegments (inserted and empty), we apply the Jacobian formulation for the two-segment multi-backbone continuum robot (MBCR) while assuming that both subsegments share the bending plane angle δ. For ease of adapting formulations, we introduce a vector notation:

$\begin{array}{cc}{\delta }_v\stackrel{\Delta }{=}\left[\begin{array}{c}{\delta }_s\\ {\delta }_{\varepsilon }\end{array}\right]=\left[\begin{array}{c}1\\ 1\end{array}\right]\delta ,d{\delta }_v\stackrel{\Delta }{=}\left[\begin{array}{c}d{\delta }_s\\ d{\delta }_{\varepsilon }\end{array}\right]=\left[\begin{array}{c}1\\ 1\end{array}\right]d\delta & \left(63\right)\end{array}$

We next use the notation of ^i-1ξ_i/i-1 to denote the pose of the i^th subsegment relative to the (i−1)^th subsegment where i∈{s,ε}. Using v and ω to denote linear and angular velocities, we define the corresponding four Jacobian matrices corresponding with the contributions of dθ_i, dδ_i, where i∈{s,ε} to the end-effector twist:

$\begin{array}{cc}\frac{d^{i-1}{\xi }_{i/i-1}}{d\left({\left[{\theta }_i,{\delta }_i\right]}^T\right)}\stackrel{\Delta }{=}\left[\begin{array}{cc}{J}_{v{\theta }_i}& J_{v{\delta }_i}\\ J_{\omega {\theta }_i}& J_{\omega {\delta }_i}\end{array}\right]\in {ℝ}^{6\times 2},i\in \left\{s,\varepsilon \right\}& \left(64\right)\end{array}$

Details of the derivations of the Jacobians are provided below.

The serial composition of two subsegments using twist transformation results in the end effector twist:

$\begin{array}{cc}{J}_{\mathrm{\xi \phi }}=\frac{d\xi }{d\phi }=\left[\begin{array}{cc}{J}_{v{\theta }_s}-{\left[\begin{array}{cc}R_c& p_{g/c}\end{array}\right]}^{J_{{\mathrm{\omega \theta }}_s}}& R_cJ_{v{\theta }_{\varepsilon }}\\ J_{{\mathrm{\omega \theta }}_s}& R_cJ_{\omega {\theta }_{\varepsilon }}\end{array}\right]& \left(65\right)\\ J_{{\mathrm{\xi \delta }}_v}=\frac{d\xi }{d{\delta }_v}=\left[\begin{array}{cc}{J}_{v{\delta }_s}-{\left[\begin{array}{cc}R_c& p_{g/c}\end{array}\right]}^{J_{{\mathrm{\omega \delta }}_s}}& R_cJ_{v{\delta }_{\varepsilon }}\\ J_{{\mathrm{\omega \delta }}_s}& R_cJ_{\omega {\delta }_{\varepsilon }}\end{array}\right]& \left(66\right)\end{array}$

These definitions of J_ξϕ and J_ξδv complete the two missing terms needed in equation (59), but with a slight formulation modification. The Jacobian matrix J_ξδ is slightly different from J_ξδv in equation (66), and using the differentiation chain rule it becomes:

$\begin{array}{cc}{J}_{\xi \delta }\stackrel{\Delta }{=}\frac{d\xi }{d\delta }=\frac{d\xi }{d{\delta }_v}\frac{d{\delta }_v}{d\delta }=J_{\xi {\delta }_v}\left[\begin{array}{c}1\\ 1\end{array}\right]& \left(67\right)\end{array}$

To calibrate the model uncertainty parameters k_λ, we extract from equation (59) the following relation:

δξ(δk_λ)=J_kδk_λ  (68)

Using this error propagation model, we construct an estimation method to estimate k_λ. Let ξj↔[x_j,R_j] designate the measured end-effector pose at the j^th robot configuration (insertion depth) where x_j and R_j designate the measured position and orientation. Let x_j and R_j denote the modeled pose using the direct kinematics for a given k_λ. The error between the measured and modeled poses are then defined as:

c _j≙[(X_j−X_j)^T,(α_ej{circumflex over (m)}_ej)^T]^T, c_j∈⁶  (69)

where α_ej and {circumflex over (m)}_ej are the angle and axis parameterizing the orientation error R_ej. These parameters are given by:

$\begin{array}{cc}{R}_{e_j}\stackrel{\Delta }{=}{\stackrel{\_}{R}}_jR_j^T=e^{{{\alpha }_{e_j}\left[{\hat{m}}_{e_j}\right]}^{^}}& \left(70\right)\\ {\alpha }_{e_j}={\cos }^{-1}\left(\frac{\mathrm{Tr}\left(R_{e_j}\right)-1}{2}\right)& \left(71\right)\\ {\hat{m}}_{e_j}=\frac{1}{2\sin \left({\alpha }_e\right)}{\left(R_{e_j}-R_{e_j}^T\right)}^{\bigvee }& \left(72\right)\end{array}$

where the operator (*)^V designates the vector form of a skew-symmetric matrix.

An aggregated error vector {tilde over (c)}_λ is defined to include errors of all N robot configurations:

{tilde over (c)} _λ=[(c₁)^T, . . . ,(c_N)^T]^T  (73)

The optimization objection function MA is then defined as:

$\begin{array}{cc}{M}_{\lambda }\left(k_{\lambda }\right)=\frac{1}{2N}{\tilde{c}}_{\lambda }^TW{\tilde{c}}_{\lambda }& \left(74\right)\end{array}$

where W is a weight matrix encoding confidence in the measurements and the measurement unit scaling factors.

The first-order Taylor series approximation of Ma is given:

M _λ(k_κ+k_K)≈M_λ(k_λ)+J_Mλδk_λ  (75)

where the aggregated Jacobian J_Mλ∈^1×5 is given by:

$\begin{array}{cc}{J}_{M_{\lambda }}=\frac{1}{N}{\left({\tilde{c}}_{\lambda }\right)}^T{\mathrm{WJ}}_{c_{\lambda }}& \left(76\right)\\ J_{c_{\lambda }}=\frac{\partial {\tilde{c}}_{\lambda }}{\partial k_{\lambda }}=-{\left[{\left(J_{k_1}\right)}^T,\dots ,{\left(J_{k_N}\right)}^T\right]}^T& \left(77\right)\end{array}$

Equation (76) shows that minimizing M_λ, entails following the gradient descent direction along d{tilde over (c)}_λ/dk_λ. The parameter k_λ is then obtained using an iterative nonlinear least squares estimation shown in algorithm of FIG. 9. In the algorithm of FIG. 9, H is the parameter scaling matrix and the task space variable scaling is achieved by adjusting W.

In the examples below, we present simulations to demonstrate the direct kinematics and differential kinematics. We also verify the differential kinematics through finite-difference simulations. We also carry out simulations to verify the differential kinematics model. In all simulations, we assumed the robot points vertically down at its home (straight) configuration.

Using the model described above, we present the simulated position analysis of the micro motion created by the EMB insertion. In both simulations and the experimental model validations, we sue the parameters as in Table 2. They include the Young's modulus of the superelastic NiTi material used for backbone tubes and EMBs (E_p, E_i, E_s), the diameters of the backbones (d_p, d_i, d_s), and the cross-sectional moment of inertia (I_p, I_i, I_s).

	TABLE 2
	R	3	mm
	Ep, Ei, Es	41	GPa
	dp, di	0.90	mm
	ds	0.38	mm
	Ip, Ii	0.0312	mm4
	Is	0.0010	mm4

FIGS. 10A, 10B, and 10C show the simulation results of the micro motion creates by EMB insertion. FIG. 10A shows the continuum segment at its initial bending angle θ=30°. During simulation, the equilibrium bending angles {θ′, θ_s} were computed at different EMB insertion depths. The resulting tip micro motion is shown in FIG. 10B for the naïve kinematic model (i.e. λ=0). FIG. 10C shows the tip motion for an updated model assuming λ=0.2+0.06q_s. We note that, as expected, in both cases the robot straightens with EMB insertion since the robot body straightens. However, the updated model exhibits a turning point behavior which relates to the combined effect of straightening and change in the end effector angle θ′.

To verify the derivations of instantaneous kinematics and error propagation, we compute Jacobians as described above. Since the simulation case represents the robot motion within a bending plane, the columns of the Jacobians represent 2×1 vectors of induced velocities for unit change in the variables associated with each Jacobian. The following simulations verify the macro motion Jacobian JM, the micro motion Jacobian J_μ, and the identification Jacobian J_k by plotting the Jacobian columns. The verification is carried out visually by verifying that the Jacobian columns induce tip velocity tangent to the trajectory generated by direct kinematics. In addition, each Jacobian has been also verified numerically via finite difference computations.

To verify J_M, the EMB insertion depth q_s was fixed and direct actuation of backbones was assumed. Sample tip positions along the trajectory were obtained via direct kinematics and the corresponding Jacobian J_M was computed. FIG. 11 shows the simulation results. These results verify that the computed J_M is tangent to the macro motion trajectory.

To verify J_μ, the secondary backbones were assumed locked and the EMB insertion depth q_s was varied. The Jacobian J_μ was computed and plotted for each EMB depth. Two different scenarios of modeling uncertainty were considered: λ=0 and λ=0.2+0.06q_s. The results in FIGS. 12A and 12B verify that J_μ is tangent to the micro scale trajectory generated by direct kinematics.

FIG. 10C shows the plots of the identification Jacobian h for the simulation scenario where λ≠0, revealing how the parameter errors of modeling uncertainty affect the tip positions and hence the shape of the trajectory.

The following examples evaluate the ability of our simplified kinematic model to capture the micro-motion behavior, validate the calibration framework, and assess the accuracy of the updated kinematic model in reflecting experimental data. A single-segment continuum robot with EMB insertion actuation was used as the experimental platform as shown in FIGS. 13A, 13B, and 13C. To observe the robot tip motion at different scales, one HD camera (FLIR Dragonfly II®) was used to capture the macro motion and the bending shape while an identical camera mounted on a 22.5× microscope lens to capture the micro motion. Custom “multi-circled” marker was used to track the tip motion under microscope while multiple custom “X” markers were attached to the continuum robot body to observe the bending shape. The vision measurement methods exhibited a micro motion tracking accuracy of better than 2 μm.

FIGS. 13A, 13B, and 13C show the frames used and also previously referred to in FIGS. 10A, 10B, and 10C. The microscope is fixed at a known offset relative to the robot base, and such offset is represented as a constant transformation from the image frame {I} to the robot base frame {B}. The tracked marker frame {M} is placed at a known offset relative to the end disk (gripper frame {G}), and the transformation is represented as a constant transformation between {M} and {G}. The marker position and orientation in the image frame is obtained by the segmentation of the three circles that construct an asymmetric pattern as shown in FIG. 13C.

FIG. 14 shows a sample marker frame trajectory during EMB insertion. The marker positions were segmented from microscope images collected at 30 frames per second. Applying a butterworth infinite impulse response filter with the 3-dB frequency as 30 Hz, provided a smooth trajectory for calibration.

Using the calibration method described above, we calibrated the modeling uncertainty parameter vector k_λ. The parameter vector k_λ in equation (28) consists of three elements, a bias term k_λ0, a coefficient gain k_λθ that is associated with the nominal bending angle θ, and a coefficient gain

$k_{{\lambda }_q}$

that relates to the EMB insertion depth q_s. As a preliminary study, in this paper, we focus on investigating and calibrating k_λ0 and

$k_{{\lambda }_q}.$

Once the characterization of k_λ0 and

$k_{{\lambda }_q}$

is achieved, one can exhaust the choices of q to investigate the effect of k_λθ.

The algorithm of FIG. 9 was initialized with

$k_{{\lambda }_0}=0,k_{{\lambda }_q}=0.$

In each iteration, the modeled positions were computed using the current estimates of the parameters. The aggregated error vector was then calculated between the modeled and experimental positions. For each iteration, both of the current estimates of the parameters and the position root-mean squared errors (RMSE) of all insertion samples (382 in total) were reported. A relative convergence threshold of 0.1% was used to determine the convergence.

For the particular experimental data collection, the parameter estimation (model calibration) went through 46 iterations before converging, where a step size of 11=0.1 was selected. FIG. 15A shows selected iterations during the estimation, and the details of the iterations are reported in Table 3. The estimation started with an initial position RMSE of 44.27 and after its convergence, the position RMSE was reduced to 5.82 showing an improvement of 86.8% in model errors.

TABLE 3
Iteration	0	5	10	20	30	45	46
$\frac{k_{{\lambda }_0}}{100}$	0	4.22	6.72	9.06	9.88	10.23	10.24
$\frac{k_{{\lambda }_q}}{1000}$	0	2.7	4.3	5.7	6.3	6.5	6.5
RMSE	42.27	15.71	25.40	7.72	6.07	5.82	5.82
[μm]

By dividing the tip trajectory into two segments, we observe that the current simplistic modeling approach produced bigger errors after the turning point: the RMSEs were reported as 4.87 μm and 6.63 μm for the two segments before and after turning point that had the lengths of 48.11 μm and 38.82 μm, respectively. If one wishes to further improve the model accuracy, a model that only predicts the trajectory before the turning point may be considered. We therefore considered another calibration where only the partial micro motion trajectory before the turning point was used. With the same iteration step size and convergence criterion, the estimation went through 59 iterations to converge, and the updated results are reported in Table 4 and plotted in FIG. 15B. The position RMSE was then further improved to 4.76 μm.

TABLE 4
Iteration	0	5	10	20	30	58	59
$\frac{k_{{\lambda }_0}}{100}$	0	4.22	6.72	9.06	9.88	10.30	10.30
$\frac{k_{{\lambda }_q}}{1000}$	0	2.7	4.3	5.7	6.3	6.5	6.5
RMSE	19.51	13.01	9.30	6.12	5.17	4.76	4.76
[μm]

This work focused on creating a simplistic, yet fast model for equilibrium modulation control implementation. The kinematic model traded accurate mechanics modeling (which typically leads to solving nonlinear boundary value problems) with simplicity and speed of computation. Our experimental data showed an unexpected motion behavior manifested by a turning point along the micro-motion trajectory. The model presented in this paper does not offer a physical explanation to this behavior, but can capture this behavior for a given robot. The model calibration results indicate that there is still a potential to improve the model performance by further investigating alternative modeling assumptions and different descriptions of modeling uncertainties. One of the limitations of our approach can be inferred from the simulation shown in FIGS. 12A, 12B, and 12C, where both columns of the identification Jacobian are almost aligned with the tangent to the direct kinematics trajectory. The attainable correction directions that the column-space of J_k affords is therefore limited in reshaping the model trajectory. This was also observed from the experimental validation. Shown from the iterations in FIGS. 15A and 15B, it is difficult to reshape the modeled tip trajectory in the direction that is perpendicular to the trajectory. The other limitation is potentially caused by the choice of linearity in expressing the modeling uncertainty.

This work presented the first modeling attempt for a new class of continuum robots capable of multi-scale motion. These robots achieve macro-scale and micro-scale motions through direct and indirect actuation (equilibrium modulation). Instead of focusing on a high-fidelity mechanics-based model, which typically leads to non-linear boundary value problems not easy to adopt for real-time control or parameter identification. Instead the paper presented a simplified mechanics-based model utilizing moment coupling effects between sub-segments of the continuum robot. This approach generates a differential kinematics model that covers both macro and micro-motion. As a result of unavoidable parameter uncertainty, we presented a model-calibration approach that can compensate for parameter inaccuracy, friction effects and modeling inaccuracies due to the simplistic modeling assumptions. The modeling approach along with the calibration framework was validated experimentally on a multi-backbone continuum robot. The calibrated model reported a positional root-mean-squared error as 5.83 μm if one wishes to use the model for the entire motion profile with the turning point. If one chooses to exclude motions past the turning point, the calibrated model fit the experimental data with an accuracy of 4.76 μm. Future work will focus on investigations on a more sophisticated models capable of incorporating geometric constraints as well as minimizing mechanical energy for improved model accuracy. In addition, effects of direction reversal of EMB insertion can manifest in hysteresis.

As mentioned above, the gradient terms defined in equations (47)-(51) are derived as follows. Rewriting equations (29) and (26) in a matrix form yields:

$\begin{array}{cc}\underset{\stackrel{\Delta }{=}A\left(\psi ,q_s,{\theta }_s\right)}{\underset{}{\left[\begin{array}{cc}{k}_1+k_2+k_s& -k_1\\ k_1& -k_1\end{array}\right]}}\underset{\stackrel{\Delta }{=}B\left(\psi ,q_s,k_{\lambda },{\theta }_s\right)}{\underset{}{\left[\begin{array}{c}{\theta }_s\\ {\theta }^{\prime }\end{array}\right]=\left[\frac{\left(k_2+k_s\right){\theta }_0-\lambda }{k_0\left({\theta }_0-\theta \right)}\right]}}& \left(78\right)\end{array}$

where A and B are defined as functions of {ψ, q_s, θ_s} and {ψ, q_s, k_λ, θ_s} respectively. Using the definition of ϕ, yields:

$\begin{array}{cc}A\left(\underset{\underset{\stackrel{\Delta }{=}S_0}{}}{\left[\begin{array}{cc}1& 0\\ 1& 0\end{array}\right]}\underset{\underset{\phi }{}}{\left[\begin{array}{c}{\theta }_s\\ {\theta }_e\end{array}\right]}-\underset{\underset{\stackrel{\Delta }{=}C_0}{}}{\left[\begin{array}{c}0\\ {\theta }_0\end{array}\right]}\right)=B& \left(79\right)\end{array}$

By introducing two constant matrices in the above equation, S₀ and C₀, we have obtained the equation to differentiate:

A(S₀ϕ−C₀)=B, A∈^2×2, B∈², C₀∈²  (80)

The full differentiation may be expressed as:

(dA)(S₀ϕ−C₀)+(AS₀)dϕ=dB  (81)

Using X_a′ to denote the partial derivative of matrix X w.r.t the scalar variable a, i.e.

$X_a^{\prime }\stackrel{\Delta }{=}\frac{\partial X}{\partial a},$

then dA and dB may be written as:

dA=A _θ ′dθ+A′ _δ dδ+A _{q s} ′dq _s +A _{θ s} ′dθ _s  (82)

dB=B _θ ′dθ+B _δ ′dδ+B _{q s} ′dq _s +B _{θ s} ′dθ _s+Σ_i^nkB_kλi′dk_λi  (83)

Let us define C_ϕ and Γ_a to provide ease in the derivations:

C _ϕ S ₀ ϕ−C ₀, Γ_a=B_a′−A_a′C_ϕ  (84)

where the letter a∈{θ, δ, q_s, θ_s}.

By substituting equation (82) and equation (83) into equation 81, and by using the definitions of C_ϕ and Γ_a, we have:

$\begin{array}{cc}\left({\mathrm{AS}}_0\right)\left[\begin{array}{c}{d\theta }_s\\ {d\theta }_{\varepsilon }\end{array}\right]-\left[{\Gamma }_{{\theta }_s}\right]\left[\begin{array}{c}{d\theta }_s\\ {d\theta }_{\varepsilon }\end{array}\right]={\Gamma }_{\theta }d\theta +{\Gamma }_{\delta }d\delta +{\Gamma }_{q_s}{\mathrm{dq}}_s+{\sum }_i^{n_k}B_{k_{{\lambda }_i}}^{\prime }{\mathrm{dk}}_{{\lambda }_i}& \left(85\right)\end{array}$

This equation shows the full differentiation of equation (45) and all the Jacobians can be obtained directly by their definitions, i.e., the expressions of

$\left\{\frac{\partial \phi }{\partial \theta },\frac{\partial \phi }{\partial \delta },\frac{\partial \phi }{\partial q_s},\frac{\partial \phi }{\partial k_{{\lambda }_i}}\in {ℝ}^{2\times 1}\right\}$

may be written as described above in equations (47), (48), (49), and (50), which s₁=[1,0] is just a selection matrix.

Furthermore, as mentioned above, equations (64), (65), and (66) refer to the Jacobian matrix partitions for the two-segment case where the first segment is the inserted subsegment (indicated by superscript s) and the second segment is the empty subsegment (indicated by subscript c). The Jacobian partitions represent the effects of differentials on θ_i and δ_i that contribute to the end-effector's translational and rotational differential, labeled by subscripts ‘v’ and ‘ω’, indicating ‘velocity’ and ‘angular velocity’, respectively. The expressions of {J_vθi, J_ωθi, J_vδi, J_ωδi} are extracted as:

$\begin{array}{cc}{J}_{{v\theta }_i}=D_i\left[\begin{array}{c}{c}_{{\delta }_i}{\chi }_{a_i}\\ -s_{{\delta }_i}{\chi }_{a_i}\\ {\chi }_{b_i}\end{array}\right],J_{{\mathrm{\omega \theta }}_i}=\left[\begin{array}{c}-s_{{\delta }_i}\\ -c_{{\delta }_i}\\ 0\end{array}\right]& \left(86\right)\\ J_{{v\delta }_i}=D_i\left[\begin{array}{c}{s}_{{\delta }_i}{\chi }_{c_i}\\ c_{{\delta }_i}{\chi }_{c_i}\\ 0\end{array}\right],J_{{\mathrm{\omega \delta }}_i}=\left[\begin{array}{c}{c}_{{\delta }_i}s_{{\theta }_i}\\ -s_{{\delta }_i}c_{{\theta }_i}\\ -1+s_{{\theta }_i}\end{array}\right]& \left(87\right)\end{array}$

Where c(⋅) and s(⋅) denote the cosine and sine functions, and Di represents the length of the subsegment. For the inserted subsegment, D_s=q_s; and for the empty subsegment, D_r=L−q_s. In addition, the following shorthanded notations are used:

$\begin{array}{cc}{\chi }_{a_i}=\frac{\left({\theta }_i-{\theta }_0\right)c_{{\theta }_i}-s_{{\theta }_i}+1}{{\left({\theta }_i-{\theta }_0\right)}^2}& \left(88\right)\\ {\chi }_{b_i}=\frac{\left({\theta }_i-{\theta }_0\right)c_{{\theta }_i}+c_{{\theta }_i}}{{\left({\theta }_i-{\theta }_0\right)}^2}& \left(89\right)\\ {\chi }_{c_i}=\frac{s_{{\theta }_i}-1}{{\theta }_0-{\theta }_i}& \left(90\right)\end{array}$

Thus, the invention provides, among other things, a system for multiple-scale motion control of a bending robotic device using equilibrium modulation. Various features and advantages of the invention are set forth in the following claims.

Claims

1. A continuum robot comprising: a hollow tube, wherein the hollow tube is rigid and bendable, andwherein the continuum robot is operable to cause a controllable bending movement of the hollow tube to adjust a pose of the continuum robot; andan equilibrium modulating wire slidably positioned inside the hollow tube, wherein linear movement of the equilibrium modulating wire relative to the hollow tube controllably alters a flexural rigidity of the hollow tube.

2. The continuum robot of claim 1, wherein retracting the equilibrium modulating wire from the hollow tube reduces a flexural rigidity of the first secondary backbone.

3. The continuum robot of claim 1, wherein a bending force applied to the hollow tube causes a first bending movement when the equilibrium modulating wire is inserted inside the hollow tube, and wherein the same bending force applied to the hollow tube causes a second bending movement when the equilibrium modulating wire is at least partially retracted from the hollow tube, wherein the second bending movement has a greater bending magnitude than the first bending movement.

4. The continuum robot of claim 1, further comprising a robot actuator configured to controllably apply a bending force to the hollow tube, andcontrollably adjust linear movement of the equilibrium modulating wire.

5. The continuum robot of claim 4, further comprising an electronic controller configured to control movement of the hollow tube by providing control signals to the robot actuator, wherein the electronic controller is configured to: determine a target pose for the hollow tube;determine a target bending force and a target linear position of the equilibrium modulating wire relative to the hollow tube needed to move the hollow tube to the target pose; andoperate the robot actuator to move apply the target bending force to the hollow tube and to move the equilibrium modulating wire to the target linear position.

5. The continuum robot of claim 4, further comprising an electronic controller configured to control movement of the hollow tube by providing control signals to the robot actuator, wherein the electronic controller is configured to: operate the robot actuator under macro-scale control by adjusting the bending force applied to the hollow tube until the hollow tube bends to an intermediate position within a threshold distance of a target position; andoperate the robot actuator under micro-scale control by adjusting a linear position of the equilibrium modulating wire until the hollow tube bends from the intermediate position to the target position.

6. The continuum robot of claim 4, further comprising a bendably articulatable body including the hollow tube, wherein the hollow tube is positioned along at least part of a length of the bendably articulatable body.

7. The continuum robot of claim 6, wherein the bendably articulatable body further includes a bendable, rigid central backbone, and wherein the robotic actuator is configured to apply the bending force to the hollow tube by controllably adjusting a linear movement of the hollow tube relative to the central backbone.

8. The continuum robot of claim 7, wherein the bendably articulatable body further includes a control disk, wherein the central backbone is fixedly coupled to a center of the control disk, wherein the hollow tube is fixedly coupled to the control disk at a defined distance from the center of the control disk such that linear movement of the hollow tube relative to a base of the continuum robot causes a bending movement of the bendably articulatable body, and wherein the bendably articulatable body is configured such that the bending movement of the bendably articulable body causes corresponding bending movements of the central backbone and of the hollow tube.

9. A system comprising: a bendably articulatable body; anda movement actuator configured to controllably adjust a bending movement of the bendably articulatable body through direct actuation by adjustably applying a bending force to the bendably articulatable body; andcontrollably adjust the bending movement of the bendably articulatable body through indirect actuation by controllably adjusting a flexural rigidity of the bendably articulatable body.

10. The system of claim 9, wherein the bendably articulatable body includes a hollow tube, andan equilibrium modulating wire slidably positioned inside the hollow tube,wherein the movement actuator is configured to controllably adjust the flexural rigidity of the bendably articulatable body by controllably adjusting a linear position of the equilibrium modulating wire relative to the hollow tube.

11. The system of claim 9, wherein the movement actuator is configured to controllably adjust the flexural rigidity of the bendably articulatable body by adjustably applying thermal energy to at least a portion of the bendably articulatable body.

12. The system of claim 9, wherein the bendably articulatable body includes a hollow tube at least partially filled with a gaseous or fluid medium, and wherein the movement actuator is configured to controllably adjust the flexural rigidity of the bendably articulatable body by controllably adjusting an internal pressure of the gaseous or fluid medium inside the hollow tube.

13. The system of claim 9, wherein the bendably articulatable body includes a control disk,a bendable, rigid central backbone fixedly coupled to a center of the control disk, anda secondary backbone fixedly coupled to the control disk at a defined distance from the center of the control disk,wherein the movement actuator is configured to adjustably apply the bending force to the bendably articulatable body by controllably adjusting a linear position of a proximal end of the secondary backbone relative to a proximal end of the central backbone.

14. The system of claim 9, further comprising an electronic controller configured to control movement of the bendably articulatable body by providing control signals to the movement actuator, wherein the electronic controller is configured to: operate the movement actuator under macro-scale control by adjusting the bending force applied to the bendably articulatable body until the bendably articulatable body bends to an intermediate position within a threshold distance of a target position; andoperate the movement actuator under micro-scale control by adjusting the flexural rigidity of the bendably articulatable body until the bendably articulatable body bends from the intermediate position to the target position.

15. A method of adjustably controlling a pose of a bendably articulatable body, the method comprising: controllably adjusting a bending movement of the bendably articulatable body through direct actuation by adjustably applying a bending force to the bendably articulatable body; andcontrollably adjust the bending movement of the bendably articulatable body through indirect actuation by controllably adjusting a flexural rigidity of the bendably articulatable body.

16. The method of claim 15, wherein the bendably articulatable body includes a hollow tube and an equilibrium modulating wire slidably positioned inside the hollow tube, and wherein controllably adjusting the flexural rigidity of the bendably articulatable body includes controllably adjusting a linear position of the equilibrium modulating wire relative to the hollow tube.

17. The method of claim 15, wherein controllably adjusting the flexural rigidity of the bendably articulatable body includes adjustably applying thermal energy to at least a portion of the bendably articulatable body.

18. The method of claim 15, wherein the bendably articulatable body includes a hollow tube at least partially filled with a gaseous or fluid medium, and wherein controllably adjusting the flexural rigidity of the bendably articulatable body includes controllably adjusting an internal pressure of the gaseous or fluid medium inside the hollow tube.

19. The method of claim 15, wherein the bendably articulatable body includes a control disk, a bendable rigid central backbone fixedly coupled to a center of the control disk, and a secondary backbone fixedly coupled to the control disk at a defined distance from the center of the control disk, wherein adjustably applying the bending force to the bendably articulatable body includes controllably adjusting a linear position of a proximal end of the secondary backbone relative to a proximal end of the central backbone.

20. The method of claim 15, further comprising determining a target position for the bendably articulatable body, wherein controllably adjusting the bending movement of the bendably articulatable body through direct actuation includes adjusting the bending force applied to the bendably articulatable body until the bendably articulatable body bends to an intermediate position within a threshold distance of a target position, andwherein controllably adjusting the bending movement of the bendably articulatable body through indirect actuation includes adjusting the flexural rigidity of the bendably articulatable body until the bendably articulatable body bends from the intermediate position to the target position.