Patent Application
20240385601
The disclosure herein relates to the technical field of multi-robot collaborative planning, and in particular, to a multi-robot collaborative planning method for machining of large spacecraft cabin components.
The structural components of the large spacecraft cabin have the characteristics such as large size and low rigidity, and the number of brackets to be machined is large, which are unevenly distributed on the surface of the cabin. At present, such components are mainly machined by using a gantry type multi-axis numerically controlled machine tool. However, the numerically controlled machine tool has the problems such as large size, high manufacturing cost, single machining object, and limited machining travel. In this machining mode, the brackets need to be repeatedly assembled and disassembled, which affects the machining accuracy and efficiency of the structural components of the large spacecraft cabin. An in-situ manufacturing mode based on a multi-robot system has emerged to address the manufacturing problems, which involves fixing the cabin and moving robots to different positions for machining. This machining mode has high adaptability, can machine parts with large size spans, and simultaneously can avoid repeated disassembly and assembly of cabin brackets. The use of multi-robot collaborative operation can significantly improve the production efficiency of the large spacecraft cabin components.
However, due to the large size of the cabin components and the restriction of the robot executed machining range, the robots cannot machine all the brackets in the circumferential direction of the cabin without changing the cabin rotation angle. Often, it is necessary to use a positioner to rotate the cabin so that the robots can cover all bracket tasks. Cabin rotation planning is attributed to a zoned machining and manufacturing problem. According to the literature “Ma J W, Lu X, Li G L, et al. Toolpath topology design based on vector field of tool feeding direction in sub-regional processing for complex curved surface [J]. Journal of Manufacturing Processes, 2020, 52:44-57”, a complex curved surface is divided into sub-regions and subdivision criteria are determined based on the geometric features or machining techniques of the curved surface, thus achieving machining with variable process parameters in different sub-regions. This method is mainly aimed at the machining mode of machine tools, and mainly focuses on the zoning research of local areas of large components with determined shapes and single machining feature. It is difficult to apply to spacecraft cabin structures with changes in the orientation and distribution of the brackets to be machined as the cabin rotates. Moreover, for the structural components of the large spacecraft cabin, their large size and wide machining range often require the robots to change their base positions during machining, firstly to cover all tasks, and secondly to adjust the posture during machining tasks, so as to improve the efficiency and quality of the machining tasks of the robot team. Literature “Wei T, Dai J, Zhou W, et al. Process Planning and Control Technology on Multi-station Working Mode of Robot Drilling and Riveting System with Auxiliary Axis [J]. China Mechanical Engineering, 2014, 25 (01): 23-27” proposes a station-based planning strategy for industrial robots, which models and optimizes the position shifting mechanism of robots. This method fully considers the movement and position shifting problem of a single robot, but ignores the space competition problem in the multi-robot collaborative operation process, and cannot be directly applied to the situation where there is workspace competition in the multi-robot collaborative machining of the structural components of the large spacecraft cabin. The balance of task assignment plays a crucial role in the operation efficiency of the multi-robot team. Literature “Wang Z, Gombolay M. Learning Scheduling Policies for Multi-Robot Coordination With Graph Attention Networks [J]. IEEE Robotics and Automation Letters, 2020, 5 (3): 4509-4516” proposes a network-based multi-robot coordinated learning scheduling strategy to address this problem. This method can quickly schedule and optimize robot teams of various sizes, and find high-quality solutions from multiple tasks. However, this method is only suitable for operations with fixed tasks and machining information that does not change over time, and cannot handle complex task situations where the orientation of the brackets outside the cabin changes with the rotation of the cabin. Generally speaking, the machining and manufacturing of existing large cabin components have the following shortcomings: 1) most of the existing large cabin components are machined by adopting a CNC machine tool machining mode, which has the problems such as high manufacturing cost, limited machining travel and long manufacturing cycle, and cannot meet the high-efficiency and high-precision manufacturing needs of large cabin components; and 2) the existing inventions are applicable to machining objects with a single feature and unchanged machining state information, while there is little mention of the double weak rigid structure with multiple robots as machining equipment and large weak rigid thin-walled components as machining objects in the existing inventions.
Aiming at the above technical problem, this application provides a multi-robot collaborative planning method for machining of large spacecraft cabin components, and the method includes:
according to the optimal scheme Rot={Ra, Rb, . . . , Rc}, where for any robot base position scheme Posi={Posga, Poshb, . . . Poslc}, g, h, l≤j and g, h, l␣N*, Posga represents a robot base position scheme Posga adopted at the cabin rotation position Ra, Poshb represents a robot base position scheme Posghb adopted at the cabin rotation position Rb, and Poslc represents a robot base position scheme Poslc adopted at the cabin rotation position Rc, calculating stiffness values {Kga, Khb, . . . , Klc} under the robot base position schemes {Posga, Poshb, . . . Poslc}, adopting a robot base position performance evaluation function Pos_fun=Kga+Kgb+ . . . +Klc, and selecting a scheme Pos={Posga, Poshb, . . . Poslc} with the optimal result of Pos_fun as a final robot base position scheme; where “a scheme with the optimal result” refers to a robot base position scheme with the largest stiffness value selected by using the Pos_fun function to calculate the average robot operation stiffness under different robot base position schemes, and comparing the average robot operation stiffness value under each scheme according to that the larger the stiffness value, the better the robot base position scheme;
Further, in step 1, the poses of the brackets to be machined change with the rotation of the cabin, further influencing the reachability of the robots to the brackets to be machined.
Further, in step 2, the unit composition of the multi-robot operation system includes a robot model, an AGV model, the number m of robots, end effector construction, and tool model and specification, where the size of the cabin of the spacecraft determines the robot model, the AGV model and the number m of robots, and the process requirement of the brackets to be machined on the surface of the cabin determines the end effector construction and the tool model and specification.
Further, in step 3, the space layout scheme of the multi-robot system includes the distribution of the robots on two sides of the cabin, and a relative position relationship between each robot and the cabin; the distribution of the robots on the two sides of the cabin influences relative position relationships among the robots, further influencing task assignment and task timing scheduling links; the relative position relationship between each robot and the cabin refers to a horizontal distance between each robot and an axis of the cabin and the height of the axis of the cabin from the ground, and the distance between each robot and the cabin influences the machining posture of each robot, further influencing the machining quality of each robot.
Further, in step 4, the number k of the cabin rotation positions is calculated according to the following formula:
k=360/α.
The number k of the cabin rotation positions is related to the step size α (unit: °) of the cabin rotation angle. An equation relationship k=360/α exists, where α represents the step size of the cabin rotation angle, in unit of “◯”.
Further, in step 5, the minimum number of rotation of the cabin kmin refers to the minimum number of rotation of the cabin on a premise of covering all tasks in the total processable task set XR.
Further, in step 6, the number j of base positions selectable for the robots is calculated according to the following formula:
j=2l/s.
The number j of the base positions selectable for the robots is related to the length l (unit: m) of the cabin and the movement step size s (unit: m) of the robots along the axial direction of the cabin. An equation relationship j=2l/s exists. The arrangement of the selectable base positions is required to ensure the spacing between base positions and ensure the safety of two or more robots during simultaneous machining.
Further, in step 8, assigning machining tasks to each robot refers to removing “overlapping tasks” and assigning “unique tasks” to a corresponding robot; the “overlapping tasks” refer to tasks processable for robots on the same side and processable at different cabin rotation angles, and the “unique tasks” refer to tasks only processable at a certain robot base position at a certain cabin rotation angle; the “unique tasks” are assigned to the corresponding robot, the current workload of the robot is calculated, and then the “overlapping tasks” are sequentially assigned to robots that are able to complete the overlapping tasks and have the least workload, completing a task assignment process for the multi-robot team.
Further, in step 9, the machining task timing scheduling table for each robot refers to a sequence that each robot ri completes the tasks in the corresponding task set Ari={Ariag, Aribh, . . . , Aricl}; the machining scheduling scheme for the multi-robot team refers to a scheme in which cabin rotation angles and robot base positions during machining at different time nodes, and a list and sequence of robot executed machining tasks at different robot base positions are determined according to a time axis.
An example of this application further provides a storage medium, which stores a computer program or instruction, when the computer program or instruction being executed, implementing the multi-robot collaborative planning method for the machining of the large cabin components of the spacecraft.
An example of this application further provides a multi-robot collaborative planning system for machining of large spacecraft cabin components, which includes a cabin rotation planning system, a robot base position planning system and a machining task timing scheduling planning system. The cabin rotation planning system is configured to determine the number of rotation of the cabin and the angle of rotation at each time. The robot base position planning system is configured to plan the number of position shifting and space positions of each robot under a determined cabin rotation scheme. The machining task timing scheduling planning system is configured to plan the number and sequence of robot executed machining tasks under a determined cabin rotation scheme and a determined robot position shifting scheme.
Compared with the existing methods, the multi-robot collaborative planning method provided in this application has the following technical effects:
The disclosure will be further specifically described below in combination with the embodiments with reference to the drawings. The advantages of the above and/or other aspects of the disclosure will become clearer.
in the figure: 1—cabin, 2—positioner rotating wheel, 3—auxiliary supporting mechanism, 4—AGV, 5—robot; and the cabin arrow direction is the rotation direction.
in the figure: 6—fixture table, 7—cabin wall plate, and 8—bracket to be machined.
This example relates to a simulated cabin machining test. Referring to
Further, an evaluation indicator for the cabin rotation scheme is set. Taking the cabin rotation scheme Rot={Ra, Rb, . . . , Rc} as an example, a comprehensive evaluation function Rot_fun=(Ka+Kb+ . . . +Kc)/ki of the number of rotation of the cabin ki and the machining stiffness {Ka, Kb, . . . , Kc} of the robot is used as an evaluation indicator. The smaller the number of rotation of the cabin and the higher the stiffness of robot executed machining tasks, that is, the larger the cabin for all the above cabin rotation schemes is 3, that is, ki=3. A genetic algorithm is adopted to solve a cabin rotation scheme with the largest stiffness of robot executed machining tasks in the above schemes. The scheme with the largest stiffness of robot executed machining tasks in the above schemes is (4, 6, 8). According to the comprehensive evaluation function Rot_fun=(Ka+Kb+ . . . +Kc)/ki, (4, 6, 8) is considered as the optimal cabin rotation scheme.
According to the above cabin rotation evaluation indicator, the optimal cabin rotation combination serial number is (4, 6, 8), that is, the cabin rotation angle combination is (90°, 150°, 210°). The cabin rotation angles at each time and the tasks processable at each angle under this scheme are as shown in Table 2.
Further, a robot base position scheme evaluation indicator is set. Taking the robot base position scheme {Posga, Poshb, . . . , Poslc} as an example, the comprehensive evaluation function Pos_fun=Kga+Khb+ . . . +Klc of the number of position shifting and the robot executed machining stiffness {Kga, Khb, . . . , Klc} is used as the evaluation indicator. The smaller the number of position shifting and the larger the operation stiffness value during robot executed machining tasks, that is, the larger the result of the function Pos_fun, the better the robot base position scheme. The number of position shifting for all the above robot base position schemes is 0, that is, under a fixed cabin rotation angle, each robot adopts a fixed machining base to complete the task. Only after the cabin is rotated for a specific angle can the base position of the robot be changed. A genetic algorithm is adopted to solve the robot base position scheme with the highest robot executed machining stiffness in the above schemes. The scheme with the highest robot executed machining stiffness in the above schemes is {(1, 3, 6, 10)90, (1, 4, 6, 9)150, (2, 3, 8, 9)210}. That is, at the cabin rotation angle of 90°, the base positions of the four robots are respectively Base Position 1, Base Position 3, Base Position 6, and Base Position 10; at the cabin rotation angle of 150°, the base positions of the four robots are respectively Base Position 1, Base Position 4, Base Position 6, and Base Position 9; and at the cabin rotation angle of 210°, the base positions of the four robots are respectively Base Position 2, Base Position 3, Base Position 8, and Base Position 9. According to the comprehensive evaluation function Pos_fun=Kga+Khb+ . . . +Klc, {(1, 3, 6, 10)90, (1, 4, 6, 9)150, (2, 3, 8, 9)210} is considered as the optimal robot base position scheme. The cabin rotation angles at each time and the tasks processable at each angle under this scheme are as shown in Table 4.
The distribution of “overlapping tasks” under this machining scheme is as shown in Table 6, where each task is processable at this robot base position at this cabin rotation angle.
Further, the workload of each robot after obtaining the initial task is calculated. Through a task reassignment process, the “overlapping tasks” are sequentially assigned to robots that are able to complete the tasks and have the least workload. The final task assignment result is as shown in Table 7.
The machining scheduling scheme for the multi-robot team refers to a scheme in which the cabin rotation angles and the robot base positions during machining at different time nodes, and a list and sequence of robot executed machining tasks at different robot base positions are determined according to a time axis. A Gantt chart of machining task timing scheduling planning of a multi-robot system is as illustrated in
This example relates to an actual cabin wall plate machining test. The number of rotation of the cabin is 1 (k=1). A multi-robot collaborative planning method involved in a machining process specifically includes the following steps (see
Further, a robot base position scheme evaluation indicator is set. The number of position shifting and the operation stiffness during robot executed machining tasks are used as evaluation indicators. The smaller the number of position shifting and the larger the operation stiffness value during robot executed machining tasks, the better the robot base position scheme. A genetic algorithm is adopted to optimize the selection process of the robot base position scheme to obtain an optimal scheme among all schemes.
A robot base position combination selected according to the evaluation indicators of the robot base position scheme is {(1, 3) 0}, that is, at the cabin wall plate rotation angle of 0°, the base positions of the robots are respectively Base Position 1 and Base Position 3. The tasks processable at the cabin wall plate rotation angle of 0° under this scheme are as shown in Table 11. Due to the constraint between robot base positions, that is, Robot 1 must be located on a left side of Robot 2, it is defaulted in the table that Robot 1 is located at Base Position 1 and Robot 2 is located at Base Position 3.
The distribution of “overlapping tasks” under this machining scheme is as shown in Table 13, where each task is processable at this robot base position at this cabin wall plate rotation angle.
Further, the workload of each robot after obtaining the initial task is calculated. Through a task reassignment process, the “overlapping tasks” are sequentially assigned to robots that are able to complete the tasks and have the least workload. The final task assignment result is as shown in Table 14.
The machining scheduling scheme for the multi-robot team refers to a scheme in which the cabin wall plate rotation angles and the robot base positions during machining at different time nodes, and a list and sequence of robot executed machining tasks at different robot base positions are determined according to a time axis. A Gantt chart of machining task timing scheduling planning of a multi-robot system for the machining of the cabin wall plate is as illustrated in
This example relates to an actual cabin machining test (see Example 1 for the simulated test). A multi-robot collaborative planning method involved in a machining process specifically includes the following steps (see
Further, an evaluation indicator for the cabin rotation scheme is set. Taking the cabin rotation scheme Rot={Ra, Rb, . . . , Rc} as an example, a comprehensive evaluation function Rot_fun=(Ka+Kb+ . . . +Kc)/ki of the number of rotation of the cabin k, and the machining stiffness {Ka, Kb, . . . , Kc} of the robot is used as an evaluation indicator. The smaller the number of rotation of the cabin and the higher the stiffness of robot executed machining tasks, that is, the larger the cabin for all the above cabin rotation schemes is 3, that is, ki/3. A genetic algorithm is adopted to solve a cabin rotation scheme with the largest stiffness of robot executed machining tasks in the above schemes. The scheme with the largest stiffness of robot executed machining tasks in the above schemes is (4, 6, 8). According to the comprehensive evaluation function Rot_fun=(Ka+Kb+ . . . +Kc)/ki, (4, 6, 8) is considered as the optimal cabin rotation scheme.
According to the above cabin rotation evaluation indicator, the optimal cabin rotation combination serial number is (4, 6, 8), that is, the cabin rotation angle combination is (90°, 150°, 210°). The cabin rotation angles at each time and the tasks processable at each angle under this scheme are as shown in Table 16.
Further, a robot base position scheme evaluation indicator is set. Taking the robot base position scheme {Posga, Poshb, . . . Poslc} as an example, the comprehensive evaluation function Pos_fun=KKga+Kgb+ . . . +Klc of the number of position shifting and the robot executed machining stiffness {Kga, Kgb, . . . , Klc} is used as the evaluation indicator. The smaller the number of position shifting and the larger the operation stiffness value during robot executed machining tasks, that is, the larger the result of the function Pos_fun, the better the robot base position scheme. The number of position shifting for all the above robot base position schemes is 0, that is, under a fixed cabin rotation angle, each robot adopts a fixed machining base to complete the task. Only after the cabin is rotated for a specific angle can the base position of the robot be changed. A genetic algorithm is adopted to solve the robot base position scheme with the highest robot executed machining stiffness in the above schemes. The scheme with the highest robot executed machining stiffness in the above schemes is {(1, 3, 6, 10)90, (1, 4, 6, 9)150, (2, 3, 8, 9)210}. That is, at the cabin rotation angle of 90°, the base positions of the four robots are respectively Base Position 1, Base Position 3, Base Position 6, and Base Position 10; at the cabin rotation angle of 150°, the base positions of the four robots are respectively Base Position 1, Base Position 4, Base Position 6, and Base Position 9; and at the cabin rotation angle of 210°, the base positions of the four robots are respectively Base Position 2, Base Position 3, Base Position 8, and Base Position 9. According to the comprehensive evaluation function Pos_fun=Kga+Kgb+ . . . +Klc, {(1, 3, 6, 10)90, (1, 4, 6, 9)150, (2, 3, 8, 9)210} is considered as the optimal robot base position scheme. The cabin rotation angles at each time and the tasks processable at each angle under this scheme are as shown in Table 18.
The distribution of “overlapping tasks” under this machining scheme is as shown in Table 20, where each task is processable at this robot base position at this cabin rotation angle.
Further, the workload of each robot after obtaining the initial task is calculated. Through a task reassignment process, the “overlapping tasks” are sequentially assigned to robots that are able to complete the tasks and have the least workload. The final task assignment result is as shown in Table 21.
The machining scheduling scheme for the multi-robot team refers to a scheme in which the cabin rotation angles and the robot base positions during machining at different time nodes, and a list and sequence of robot executed machining tasks at different robot base positions are determined according to a time axis. A Gantt chart of machining task timing scheduling planning of a multi-robot system is as illustrated in
In specific implementation, this application provides a computer storage medium and a corresponding data processing unit. The computer storage medium can store a computer program, when executed by the data processing unit, the computer program can implement the multi-robot collaborative planning method for the machining of the large cabin components of the spacecraft provided in the disclosure and partial or all steps in each example. The storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM), a random access memory (RAM), or the like.
Those skilled in the art can clearly understand that the technical solutions in the above examples may be implemented by means of computer programs and corresponding general hardware platforms thereof. Based on such understanding, the technical solutions in the above examples or the parts that contribute to the existing technology may be essentially reflected in the form of computer programs, i.e., software products. The computer programs, i.e., software products, may be stored in a storage medium, including several instructions to enable a device containing a data processing unit (which may be a personal computer, a server, a single-chip computer, an MUU or a network device) to execute the methods described in each example or certain parts of the examples of the disclosure.
The disclosure provides a multi-robot collaborative planning method for machining of large spacecraft cabin components. There are many specific methods and approaches to implement this technical solution, and the above are only exemplary embodiments of the disclosure. It should be pointed out that for those skilled in the art, several improvements and modifications may be made without departing from the principles of the disclosure. These improvements and modifications should also be considered as the scope of protection of the disclosure. All components not clearly defined in the above examples may be implemented by adopting the existing technology.
| Number | Date | Country | Kind |
|---|---|---|---|
| 202310555238.X | May 2023 | CN | national |
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/CN2023/109023 | 7/25/2023 | WO |
