The present disclosure generally relates to quadcopter systems; and in particular, to a quadrotor with variable geometry and associated control model to accommodate improved navigation through constrained spaces.
Recent work on quadrotor systems demonstrated their applications for challenging tasks such as inspections of cluttered and occluded environments, aerial grasping and contact-based navigation, where a quadrotor interacts physically with the environment while navigating through it. Flying through such cluttered environments often requires the quadrotor to traverse through gaps smaller than its size, while simultaneously ensuring successful missions.
For a rigid quadrotor, flying through constrained spaces leads to research topics such as executing aggressive maneuvers or performing real-time trajectory re-planning to avoid cluttered areas using computer vision techniques. Alternatively, a quadrotor can have an adaptive morphology, where a folding mechanism helps the quadrotor fly through narrow apertures. In previous designs, additional actuators were needed to control the adaptive morphology, which increases the total weight of the system and decreases its power-to-weight ratio. In either case, different interactions bring significant challenges for vehicle stability when the quadrotor experiences unknown interactions with edges formed by passageways and gaps that the quadrotor may fly through.
It is with these observations in mind, among others, that various aspects of the present disclosure were conceived and developed.
Corresponding reference characters indicate corresponding elements among the view of the drawings. The headings used in the figures do not limit the scope of the claims.
Various embodiments of a novel quadrotor with variable geometry suitable which allows the quadrotor to physically interact with cluttered environments and fly through narrow gaps and passageways are disclosed herein. The compliant and variable geometry quadrotor with passive morphing capabilities disclosed is designed may include torsional springs at every arm hinge to allow for rotation driven by external forces. In one embodiment, the quadrotor includes a dynamic model for control of the quadrotor as well as an adaptive controller for trajectory tracking as further described herein. A corresponding Lyapunov stability proof of attitude tracking is also presented. Further, an admittance controller is designed to account for changes in yaw due to physical interactions with the environment. Finally, the disclosed design for the quadrotor has been validated in flight tests with two setups: a small gap and a passageway. The experimental results have demonstrated the unique capability of the quadrotor in navigating through constrained narrow spaces. Referring to the drawings, an embodiment of a quadrotor is illustrated and generally indicated as 100 in
Referring to
Each of the plurality of arms 104 includes a propeller (130) driven by at least one motor (160), and a propeller guard 132 protecting the propeller (130) from objects within the environment. A portion of each arm 104 is mounted over a connection member 124 defining a hinge or joint 112 extending between the base 122 and the top plate 121 accommodating in-plane rotary motion of the arm 104 along a plane defined by the hinge (rotary motion indicated and further described in
In addition, a torsional spring (106) is attached along and/or to each hinge. The torsional spring (106) deflects to accommodate the in-plane rotary motion of the arm 104 away from an original position as the arm engages an obstruction and provides an angular return force to urge the arm 104 back to the original position.
Compared to prior work on morphing quadrotors, the quadrotor 100 employing the torsional springs 106 as described results in negligible weight (<2% of the total weight) for achieving adaptive morphology. Hence, the power-to-weight ratio of the quadrotor 100 is not significantly affected. In addition, as indicated, the rotary motion of the arms 104 is passive and actuators are not required.
The rest of this disclosure discusses the details of the quadrotor 100 and its fabrication followed by a description of a model and system dynamics for varying geometry of the quadrotor 100. In addition, the present disclosure discusses an adaptive controller 210 (low-level controller,
In some embodiments, the body frame 102 and propeller guards 132 of the quadrotor 100 are 3D printed using a polymer such as polylactide or another suitable material, to ensure that the quadrotor 100 is lightweight and resilient to impact forces experienced during interactions with the environment. In some embodiments, the quadrotor 100 includes a base 122 and a top plate 121 operatively coupled to four arms 104. Each arm 104 of the quadrotor 100 includes an arm member 140 associated with a respective torsional spring 106 and a propeller guard 131. The torsional springs 106 are each attached to a respective joint 112 that connects a respective arm member 140 to the top plate 121. As shown, each torsional spring 106 defines a first end 161 and a second end 162, wherein the first end 161 being secured to the top plate 121 and the second end 162 being secured to the arm member 140 and/or a connection member 124, such as a screw. This assembled engagement between these components adds a rotational degree of freedom (DoF) about the connection member 124 located within each joint (
The notations used in this disclosure will be described in greater detail as well as the details of a simplified lumped mass model to calculate a position of center of gravity (CG) of the quadrotor 100 and a moment of inertia matrix of the quadrotor 100 at any instant in time. For an asymmetric and varying CG, the mapping of control inputs to individual motor thrusts changes, so a derivation of a control allocation matrix is further described herein. As shown in
For system dynamics and modeling, an inertial frame {i1, i2, i3} and a body frame {b1, b2, b3} are defined as shown in
Because the quadrotor 100 has a varying geometry while flying through passageways, it is necessary to compute the location of CG at any instant of time and the system's moment of inertia in order to compute correct control moments for trajectory tracking. The quadrotor 100 is modeled as an assemblage of a large sphere of mass M with diameter D and point masses of m units at a distance of l each from the center of the sphere. In addition, spheres 2 and 4 are at a height of h units below the motor pairs of 1 and 3. The inertia tensor of the whole body is calculated considering the {q1, q2, q3} frame with origin O as shown in
Without loss of generality, let arms 2 and 4 make an angle of β1 and β2 with the negative q1 axis respectively as shown in
cg=(−l(sin β1−sin β2),−l(cos β1+cos β2),2Ch), (1)
The moment of inertia calculated using (2) is used to design an adaptive controller 210 for trajectory tracking. This formulation is verified using a SolidWorks model as shown in Table III in Appendix A. The parameter values of the lumped mass model for the current design are: M=710 g, m=95 g, D=10 cm, I=12.5 cm, h=−3 cm.
Assuming that the thrust produced by each propeller 130 of the quadrotor 100 is directly controlled and that the direction of thrust generated is normal to the quadrotor plane, the total thrust is the sum of thrusts produced by each propeller 130, that is, f=Σi14 fi. Further, let τ1, τ2 and τ3 denote the pitch, roll and yaw moments respectively and γi, i=1, . . . , 4 denote the angles made by each arm 104 with the positive b2 axis as shown in
contributes to the total yaw moment and is equal to (−1)icτfi for a fixed constant cτ. The control allocation matrix (CAM) of the total thrust f and total moment T can be determined as a mapping to individual motor thrusts, f1, f2, f3 and f4, as
The determinant of this matrix is:
It is shown that when arms 104B-104D are at the same location, this matrix is singular, implying that the quadcopter 100 will lose some degrees of freedom for full attitude control and need to find solutions for a reduced order system. For this disclosure, it is ensured that CAM does not become singular at any instant of time.
In the rest of this disclosure, it is assumed that control inputs to the system are total thrust f∈ and torque τ∈3 and use (4) to calculate the individual thrust needed for each propeller 130.
The dynamics of the quadrotor 100 about the CG with control inputs f and T are given by:
m
t
{dot over (v)}=m
t
ge
3−Re3,
{dot over (x)}=v
{dot over (R)}=R{circumflex over (Ω)},
JΩ=τ−Ω×JΩ, (5)
Referring to
For trajectory tracking of a varying morphology, an adaptive attitude controller 210 is disclosed on the nonlinear configuration Lie group which accounts for the quadrotor's 100 varying moment of inertia. The desired b1d, b2d and b3d axes are chosen in a similar fashion as that of a standard quadrotor. Now, errors in R and Ω are defined as:
e
Ω
=Ω−R
T
R
dΩd,
e
R=½(RdTR−RTRd)v. (6)
The control moment T∈3 is selected as:
τ=J(−kReR−kΩeΩ−kΩ
Proof: The asymptotic stability for the attitude error is given in the appendix.
Simulations: The comparison results of simulations for a case where β1=30°, β2=30°, xd=[5 5−4]T and b1d=[1 0 0]T with the adaptive controller 210 and a standard controller which does not account for the varying J(β1(t), β2(t)) are shown in
In this section, an admittance controller 260 in yaw to account for the physical forces acting on the quadrotor 100 in relatively smaller gaps and tunnels is disclosed. It is critical to replan the yaw setpoint because as the quadrotor 100 approaches the passageway, unforeseen interactions can lead to unintended yaw moments. In such scenarios, if the yaw set-point is not updated, the quadrotor 100 tries to correct the yaw repeatedly during its flight and is prone to multiple collisions which may lead to unsuccessful flights. To this end, the yaw admittance controller 260 is included, where an outer loop is added to the low level controller 210 to modify the yaw reference trajectory.
M
ψ{umlaut over (ψ)}d+Dψ{dot over (ψ)}d+Kψψd=ψ, (8)
where ψ is the current yaw. The tuning parameters of Mψ, Dψ and Kψ are chosen such that the dynamics of ψd is critically damped to track changes in current yaw, to avoid delayed responses and oscillations in Ωd from over-damped and under-damped dynamics, respectively.
This section demonstrates the performance of the disclosed quadcopter 100 through experiments of i) flying through a gap and ii) flying through a passageway.
Experiments were conducted in an indoor drone studio at the Arizona State University. An Optitrack motion capture system with 17 high-speed cameras was utilized to obtain the position and heading of the vehicle. The 3-D position and current heading were transmitted to PIXHAWK, at 120 Hz for real-time feedback control. A high-level onboard computer 200 implementing was an Intel UP-board which ran the Robot Operating System (ROS) for communication with motion-capture system. A multi-threaded application was implemented for the admittance controller 260 and to enable the serial communication with the PIXHAWK. The quadrotor 100 was equipped with two additional IMUs, one underneath each arm, to obtain the arm bending angle.
The low-level attitude control algorithm 210 was implemented as described herein. A quaternion-based complementary filter was implemented for attitude estimation. A Kalman filter based algorithm was implemented for the low-level position estimation, and a cascaded P-PID control structure for the position control module. The admittance parameters (Mψ, Dψ, Kψ) were (0.01, 0.2, 1.0) for both the flight tests.
In a first flight test shown in
Flight through a Passageway
After successfully evaluating flight through a gap, the performance of the quadrotor 100 is evaluated using a passageway 20 where the vehicle continuously interacts with the environment. For the experimental setup, two acrylic sheets of 1 meter length are utilized to create the passageway with a width of 29 cm.
In this disclosure a novel quadrotor 100 with a passive folding mechanism which is capable of flying through gaps and passageways with dimensions smaller than its full body width while interacting with the environment. An adaptive controller 210 is included for trajectory tracking as well as a yaw admittance controller 260 in the outer loop to account for physical interactions during the flights. The mechanical complexity and added weight of the quadrotor 100 was low compared to existing morphing quadrotors. Finally, the quadrotor 100 was validated in flight tests through narrow apertures and tunnel-like environments.
It should be understood from the foregoing that, while particular embodiments have been illustrated and described, various modifications can be made thereto without departing from the spirit and scope of the invention as will be apparent to those skilled in the art. Such changes and modifications are within the scope and teachings of this invention as defined in the claims appended hereto.
The present document is a continuation application that claims benefit to U.S. patent application Ser. No. 17/500,720 filed Oct. 13, 2021 which application claims the benefit of U.S. Provisional Patent Application Ser. No. 63/091,114 filed on Oct. 13, 2020, which are herein incorporated by reference in their entireties.
Number | Date | Country | |
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63091114 | Oct 2020 | US |
Number | Date | Country | |
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Parent | 17500720 | Oct 2021 | US |
Child | 18241654 | US |