Double-mechanical-arm cooperative trajectory planning method and device based on improved artificial potential field method

Abstract

The invention relates to the technical field of mechanical arm trajectory planning, in particular to a double-mechanical-arm cooperative trajectory planning method and device based on an improved artificial potential field method.The collision distance between double mechanical arms and the collision distance between the double mechanical arms and environmental obstacles are detected in real time; an improved potential field function is constructed, wherein the function is formed by weighting a gravitation term jointly influenced by the tail end position and the joint angle deviation and a repulsive force term based on the real-time collision distance; and with the minimum total potential energy as a target, master-slave alternating collaborative trajectory search and optimization are carried out in a joint space. And when searching is trapped in a local minimum value, a virtual target pose is dynamically generated to guide the mechanical arm to escape, and then the mechanical arm restores to move towards the final target. According to the method, the local minimum problem is effectively solved, the planned joint track is smooth, the speed is continuous, and the motion performance of safe, stable and collaborative obstacle avoidance of the double mechanical arms in the shared space is remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of robotic arm trajectory planning technology, and in particular to a method and apparatus for collaborative trajectory planning of two robotic arms based on an improved artificial potential field method. Background Technology

[0002] With the continuous development of intelligent manufacturing and special operations, multi-robot collaborative operation has become a key technology for overcoming bottlenecks in complex operations due to its significant advantages in task capability, efficiency, and flexibility. Among them, the dual-arm system, as the most common collaborative unit, is widely used in assembly, handling, welding, and other scenarios. However, in actual dynamic and unstructured environments, achieving safe, efficient, and stable collaborative motion planning for dual arms still faces a series of fundamental challenges.

[0003] Traditional artificial potential field methods are widely used for real-time obstacle avoidance in robots due to their simple mathematical models and fast computational response. However, this method has inherent limitations when applied to collaborative robotic arm operation: First, traditional potential fields rely solely on the Cartesian spatial positional relationship between the end effector and the target point / obstacle, failing to consider the joint kinematics of the robotic arm itself. This can lead to abrupt changes in joint velocity, near-singular configurations, or low energy efficiency in the planned path. Second, the method is prone to getting trapped in local minima, especially when the two robotic arms are close to each other or interact with complex obstacles, leading to planning failure. Furthermore, the collision detection models of traditional potential fields are often coarse or computationally intensive, making it difficult to accurately describe the complex interference between robotic arm links while ensuring real-time performance, thus affecting safety.

[0004] On the other hand, existing research on collaborative planning for dual robotic arms often treats sub-problems such as motion planning, internal force control, and load balancing in isolation. For example, separating collision-free path planning and joint torque optimization into two independent stages results in planned trajectories that are dynamically infeasible or have poor performance. Furthermore, when collaboratively manipulating unknown objects, the lack of robust internal force tracking capabilities for uncertainties in multiple parameters such as object stiffness and contact point motion leads to insufficient stability in collaborative operations. These problems reflect the current lack of an integrated framework that can uniformly handle kinematic constraints, dynamic coupling, real-time obstacle avoidance, and uncertainty compensation. Summary of the Invention

[0005] In view of this, the purpose of this invention is to propose a dual-manipulator cooperative trajectory planning method and device based on an improved artificial potential field method, so as to solve the problems of local minima traps, lack of consideration for joint kinematic performance optimization, and insufficient collision detection efficiency and accuracy in traditional dual-manipulator cooperative obstacle avoidance trajectory planning.

[0006] To achieve the above objectives, this invention provides a dual-manipulator cooperative trajectory planning method based on an improved artificial potential field method, comprising the following steps:

[0007] Step S1: Real-time detection of collision risks between the two robotic arms and between the robotic arms and obstacles, and acquisition of collision distance information;

[0008] Step S2: Based on the collision distance information, construct a total potential energy function for the dual robotic arm system, which is a weighted sum of gravitational potential energy and repulsive potential energy. The value of the repulsive potential energy is calculated based on the collision distance information, and the gravitational potential energy is configured to be affected by both the position deviation of the robotic arm end and the joint space angle deviation.

[0009] Step S3: With the goal of minimizing the total potential energy function, perform trajectory search in joint space and output the optimized trajectory;

[0010] Step S4: If the trajectory search process gets stuck in a local minimum due to potential field balance, a virtual target pose is dynamically generated based on the current relative pose relationship between the two robotic arms and the obstacle. The virtual target pose is used as the new search target, and the trajectory search process in step S3 is re-executed to generate a trajectory segment leading to the virtual target pose. After the virtual target pose is achieved, the final target pose is resumed as the search target, and the trajectory search process in step S3 is continued until the final target pose is reached, and the optimized trajectory is output.

[0011] Preferably, the formula for calculating gravitational potential energy is:

[0012] ;

[0013] in, Represents gravitational potential energy. X is the gravitational gain coefficient, and X is the current coordinate of the robotic arm's end effector. The coordinates of the target at the end of the robotic arm. Let be the current angle of the i-th joint. Let be the target angle of the i-th joint, and n be the number of joints in the robotic arm.

[0014] Preferably, the formula for calculating repulsive potential energy is:

[0015] ;

[0016] in, Let n represent the repulsive potential energy, and n represent the number of joints in the robotic arm. The repulsive gain coefficient is... This represents the distance from the obstacle to the i-th joint of the robotic arm. This represents the radius of the repulsive field.

[0017] Preferably, in step S4, dynamically generating a virtual target pose includes:

[0018] Based on the geometric relationship of the two robotic arms projected onto a preset plane, a virtual target direction for escape is determined;

[0019] Based on the direction of the virtual target, a set of temporary joint angles are calculated as the pose of the virtual target.

[0020] Preferably, determining a virtual target direction for escape based on the geometric relationship of the dual robotic arms projected onto a preset plane includes:

[0021] Make the projection line from the robotic arm tangent to the virtual circular boundary defined by a specific point on the main robotic arm to determine the virtual target angle from the rotary joints closest to the robotic arm.

[0022] Based on the relative positional relationship of the two robotic arms on the projection plane, it is determined whether the slave robotic arm should be located above or below the master robotic arm.

[0023] Preferably, calculating a set of temporary joint angles as the virtual target pose based on the virtual target orientation includes:

[0024] Based on the judgment result, the virtual target angle from the remaining joints of the robotic arm is increased or decreased accordingly to guide it to move toward the judged orientation;

[0025] The virtual target pose is formed by the determined virtual target angles of each joint.

[0026] Preferably, step S1 specifically includes: performing collision detection by establishing a cylindrical-hemispherical bounding box model for the links of the robotic arm, and obtaining the collision distance information by calculating the shortest distance between the center lines of the two bounding boxes.

[0027] This invention also provides a dual-manipulator cooperative trajectory planning system based on an improved artificial potential field method, comprising:

[0028] The collision detection module is used to acquire collision distance information between the two robotic arms and between the robotic arm and obstacles in real time;

[0029] The trajectory search module is used to construct a total potential energy function consisting of a weighted sum of gravitational potential energy and repulsive potential energy for the dual robotic arm system based on the collision distance information, and to perform trajectory search in the joint space with the goal of minimizing the total potential energy function. The value of the repulsive potential energy is calculated based on the collision distance information, and the gravitational potential energy is configured to be affected by both the position deviation of the robotic arm end and the angle deviation of the joint space.

[0030] The local minimum value processing module is used to dynamically generate a virtual target pose based on the current relative pose relationship between the dual robotic arms and the obstacle when the trajectory search process gets stuck in a local minimum due to potential field balance. This virtual target pose is then used as the new search target, triggering the trajectory search module to re-perform the trajectory search to generate a trajectory segment leading to the virtual target pose. After the virtual target pose is achieved, the trajectory search module is triggered to resume using the final target pose as the search target and continue the trajectory search until the final target pose is reached.

[0031] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the above-described method.

[0032] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method.

[0033] The beneficial effects of this invention are:

[0034] 1. This invention introduces a dynamic virtual target point generation and guidance strategy. When planning is detected to be stuck, it can quickly calculate the effective escape direction and temporary target pose based on the real-time relative geometric relationship between the dual robotic arms and obstacles. By guiding the robotic arms to actively jump out of the potential field trap and then resume moving towards the final target, the problem of planning failure caused by local minima is fundamentally solved, significantly improving the success rate and reliability of path search in complex scenarios.

[0035] 2. The joint trajectory, velocity, and acceleration curves planned by this invention have been experimentally verified to remain smooth and continuous, effectively avoiding sudden changes in joint velocity and acceleration impacts caused by obstacle avoidance maneuvers. This not only improves trajectory tracking accuracy but also reduces mechanical vibration and wear.

[0036] 3. This invention employs a simplified collision model based on a cylindrical-hemispherical envelope box, enabling real-time and accurate calculation of the minimum distances between robotic arm links and between the robotic arm and obstacles with low computational overhead. This distance information is directly used to construct the repulsive force field. Combined with real-time collision detection and candidate solution screening steps embedded in the master-slave collaborative planning process, collision risks are actively eliminated at each trajectory generation step, thus providing a reliable guarantee for safe and conflict-free collaborative operation of two robotic arms in a shared workspace.

[0037] 4. This invention, through a task allocation mechanism based on real-time utility and leader-follower formation control, can coordinate the base movement and robotic arm operation of multiple mobile robots. When completing complex tasks such as collaborative handling, it can optimize the overall system performance under multiple constraints, such as significantly reducing system-level joint torque, thereby improving the working efficiency, stability and load-bearing capacity of the entire system.

[0038] 5. This invention does not rely on a globally accurate map of the environment; it only needs to perceive local obstacle information to perform online planning, and has good environmental adaptability and robustness. Attached Figure Description

[0039] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only for this invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0040] Figure 1 This is a schematic diagram of the process of a dual-manipulator cooperative trajectory planning method based on an improved artificial potential field method according to an embodiment of the present invention.

[0041] Figure 2 This is a simulation diagram of the collaborative workspace of two robotic arms according to an embodiment of the present invention;

[0042] Figure 3 This is a diagram showing the experimental results of the joint angle trajectory of the dual robotic arms according to an embodiment of the present invention;

[0043] Figure 4 The figure shows the experimental results of the joint angular velocity of the dual robotic arms according to an embodiment of the present invention;

[0044] Figure 5 The figure shows the experimental results of the joint angular acceleration of the dual robotic arms according to an embodiment of the present invention. Detailed Implementation

[0045] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0046] It should be noted that, unless otherwise defined, the technical or scientific terms used in this invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0047] Example 1:

[0048] like Figure 1 As shown, this embodiment provides a dual-manipulator cooperative trajectory planning method based on the improved artificial potential field method (R-APF), including the following steps:

[0049] Step S1: Real-time detection of collision risks between the two robotic arms and between the robotic arms and obstacles, and acquisition of collision distance information;

[0050] To achieve efficient and accurate collision detection, a reasonable simplified model of the complex three-dimensional structure of the robotic arm is required. In this embodiment, a "cylinder-hemispherical bounding box" model is used to approximate each link of the robotic arm and obstacles are modeled as capsules or other geometric shapes. Specifically, cylinders are used to simulate the body of the links, and hemispheres are used to simulate the shape of the connecting joints. The specific parameters of the model (such as the diameter and length of the cylinders) are directly derived from the design drawings or physical measurement data of the robotic arm. For example, for a six-degree-of-freedom robotic arm, different diameters and lengths of cylinders can be set for the base, upper arm, and lower arm, respectively, based on the actual link dimensions. After establishing the simplified model, the core of collision detection is transformed into calculating the shortest distance between any two such bounding boxes (i.e., capsules).

[0051] The method for calculating the shortest distance between two capsules is as follows: Each capsule is defined by its central axis (a line segment) and radius. Assuming the coordinates of the endpoints of the two central line segments are known, a function of the square of the distance between these two points with respect to the parameters can be constructed by introducing parameters to represent the points on the line segments. By solving the partial derivative of this function with respect to the parameters and analyzing its extrema within the parameter domain, the shortest distance between the two central line segments can be accurately calculated. Finally, subtracting the sum of the radii of the two capsules from this shortest distance yields the proximity between different parts of the robotic arm or between the robotic arm and external obstacles, resulting in the shortest gap distance between the model surfaces, thus obtaining collision distance information. If this gap distance is less than or equal to zero, a collision is considered imminent or has already occurred, indicating a collision risk has been detected.

[0052] Step S2: Based on the collision distance information, construct a total potential energy function for the dual robotic arm system, which is a weighted sum of gravitational potential energy and repulsive potential energy. The value of the repulsive potential energy is calculated based on the collision distance information, and the gravitational potential energy is configured to be affected by both the position deviation of the robotic arm end and the joint space angle deviation.

[0053] Specifically, the total potential energy function is ,in , All of these represent adjustable weighting coefficients used to balance the priorities of target attraction and obstacle avoidance. Their specific values ​​need to be determined through experimental debugging based on the relative requirements of the mission for trajectory accuracy and safety.

[0054] Gravitational potential energy It guides the robotic arm toward the target. Unlike traditional methods that establish gravity only in Cartesian space (end-effector position), this invention uses gravitational potential energy to simultaneously optimize the end-effector task and joint motion state. The specific calculation formula is as follows:

[0055] ;

[0056] in, Represents gravitational potential energy. is the gravitational gain coefficient, whose magnitude directly affects the speed at which the robotic arm approaches the target and needs to be adjusted according to the robotic arm's dynamic characteristics and the desired motion speed. X is the current coordinate of the robotic arm's end effector. Let be the coordinates of the target at the end of the robotic arm, and n be the number of joints in the robotic arm. Let be the current angle of the i-th joint. Let be the target angle of the i-th joint. The target angle is obtained by considering the coordinates of the end effector. It is obtained by performing inverse kinematics. Inverse kinematics is a well-known method that uses the robot's geometric model to solve for all possible combinations of joint angles from the end effector pose. This directly prompts the planning algorithm to consider not only how to get the end to the destination when finding a path, but also how to make each joint move in a way that is closer to the ideal target configuration, thereby indirectly optimizing motion performance and avoiding singular regions.

[0057] Repulsive potential energy It is responsible for keeping the robotic arm away from obstacles and other robotic arms. It calculates this distance based on the real-time distance information provided by the aforementioned collision detection, specifically as follows:

[0058] ;

[0059] in, Let n represent the repulsive potential energy, and n represent the number of joints in the robotic arm. The repulsive force gain coefficient determines the "hardness" of the obstacle avoidance behavior and needs to be set according to the degree of environmental congestion and the sensitivity of the robotic arm. This represents the distance from the obstacle to the i-th joint of the robotic arm. This represents the radius of the repulsive field. Repulsive force is only generated when the distance between the obstacle and the joint falls within this threshold range, and the closer the distance, the faster the repulsive force increases. This ensures that the robotic arm avoids obstacles only when necessary and reacts strongly to close-range threats.

[0060] Step S3: With the goal of minimizing the total potential energy function, perform trajectory search in the joint space;

[0061] The trajectory search process is performed entirely in joint space. The system starts with the current joint angle vector of the robotic arm and aims to minimize the total potential energy. To optimize the target, the adjustment direction and step size of the joint angles are calculated using numerical iterative methods (such as gradient descent or other optimization algorithms). Each iteration attempts to find a set of tiny joint angle changes that reduce the total potential energy, thus gradually "rolling down" to a potential energy depression, ultimately generating a continuous sequence of joint angles from the starting point to the target point, i.e., the desired trajectory. The step size is a key parameter that needs to be preset, as it affects the accuracy and speed of the planning. An excessively large step size may cause oscillations, while an excessively small step size reduces efficiency; a compromise must be made. Joint space refers to the space formed by the angles of the robotic arm's joints. The space that is formed.

[0062] For example, in one specific implementation, starting from the current joint angle of the main robotic arm, within a preset step length... A set of candidate joint angles is generated within a defined neighborhood. The end-effector pose corresponding to each candidate is calculated using forward kinematics, and the joint angle combination that minimizes the gravitational potential energy is selected as the pose of the main robotic arm at the next moment. If the new pose found multiple times in a row is the same as the current position, it is considered that the main robotic arm has reached the target or is in trouble.

[0063] After determining the next pose of the main robotic arm, a set of candidate joint angles is generated within a neighborhood defined by the current joint angle of the robotic arm. For each candidate solution, collision detection is performed using the next pose of the main robotic arm, eliminating candidate solutions that would cause a collision. The total potential energy of the candidate solutions that pass the collision detection is calculated, and the joint angle combination that minimizes the total potential energy is selected as the next pose of the robotic arm. The main and slave robotic arms are then driven sequentially to their respective newly searched poses. Then, the trajectory search of the main robotic arm and the trajectory search of the slave robotic arm are repeated until both robotic arms reach the target pose.

[0064] During the trajectory search guided by the potential field, the algorithm may encounter a local minimum point where the attractive and repulsive forces cancel each other out, causing the algorithm to stall. In this case, our method will activate the virtual target point escape strategy.

[0065] S4. This strategy first determines which robotic arm should be considered the master arm and which should be considered the slave arm based on the current projection geometry of the two robotic arms on the horizontal plane (e.g., the XY plane of the ground coordinate system). Next, the strategy dynamically calculates a temporary "virtual target pose" for the slave arm to track. The generation of this pose follows these rules:

[0066] Select a key point on the projection of the main robotic arm (e.g., the lowest point of one of its links).

[0067] Using this key point as the center and a safe distance as the radius, define a virtual circle on the projection plane. The setting of this safe distance needs to take into account the physical dimensions and motion uncertainties of the robotic arm.

[0068] Adjust the angle of the rotary joint (joint 1) closest to the base of the robotic arm so that the outline of the entire robotic arm on the projection plane is tangent to the virtual circle. This angle is set as the virtual target angle of that joint.

[0069] Further analysis of the intersection point or the highest point of the projections of the two robotic arms is needed to determine whether the best escape direction for the robotic arm to avoid interference is to move above or below the main robotic arm.

[0070] Based on the orientation determination (above or below) from the previous step, the angles of several key joints of the robotic arm (such as joints 2, 3, and 4) are gradually increased or decreased until the desired orientation relationship (above or below) is achieved from a specific reference point on the robotic arm relative to a specific point on the main robotic arm. The angles of these joints obtained at this point are then set as their respective virtual target angles.

[0071] Ultimately, the joint angles of this series of virtual targets together constitute a complete, temporary virtual target pose.

[0072] After obtaining the virtual target pose, this method temporarily replaces the endpoint of the trajectory search with this virtual target pose and immediately invokes the aforementioned trajectory search process to plan and execute a trajectory to this virtual pose. Once the robotic arm reaches the virtual pose, the system switches the search endpoint back to the actual final target pose and resumes the main search loop.

[0073] Example 2:

[0074] To verify the trajectory planning method proposed in Example 1, the following experiments and analyses were conducted on a self-built dual-manipulator experimental platform.

[0075] Before planning, a simulation analysis was first conducted on the collaborative operation space of the dual robotic arm system to determine their commonly accessible safe working area.

[0076] Experimental method: Based on the established kinematic model of the dual robotic arms and the calibration results of the base coordinate system, a large number of joint angle combinations were randomly generated using the Monte Carlo method, and the corresponding end effector position point cloud was obtained through forward kinematics calculation.

[0077] Experimental results: Figure 2 Simulation results of the collaborative workspace of two robotic arms are presented. In the figure, the red point cloud represents the reachable space of the master robotic arm, the blue point cloud represents the reachable space of the slave robotic arm, and the overlapping area of ​​red and blue points represents the collaborative workspace of the two robotic arms. Simulation results show that the established mathematical model can clearly define the physical range within which the two robotic arms can safely collaborate, providing basic spatial constraints for collision-free trajectory planning.

[0078] Subsequently, a physical verification experiment was conducted on a self-built experimental platform, demonstrating how dual robotic arms collaboratively grasped a box lid and autonomously bypassed static obstacles to place it.

[0079] The host computer recorded and processed the joint motion data of the two robotic arms throughout the entire task execution process, and the results are as follows: Figures 3-5 As shown in the figure These represent different joint angles.

[0080] like Figure 3 The joint angle trajectory of the robotic arm is smooth and continuous. The joint angle curve of the main robotic arm shows a phase jump during obstacle avoidance and turning, but analysis shows that this is a natural kinematic mapping result caused by the change in the end-effector trajectory, and does not cause a sudden change in joint velocity or acceleration.

[0081] like Figure 4 As shown, the joint angular velocity curves of the master and slave robotic arms remain smooth and continuous throughout the entire mission, including the obstacle avoidance phase, without any sudden changes in speed.

[0082] like Figure 5As shown, the joint angular acceleration curves of the master and slave robotic arms are also smooth and continuous, without any impact acceleration peaks.

[0083] No collisions occurred between the two robotic arms or between the robotic arm and the toolbox throughout the experiment, successfully achieving safe collaborative operation in a shared space.

[0084] The above simulation and physical experiments jointly verified the superiority of the method of the present invention. This method can generate safe and collision-free cooperative motion trajectories for dual robotic arm systems in complex obstacle environments in real time.

[0085] The trajectory planned by this method has high smoothness at the joint space level, and the joint velocity and acceleration are continuous without abrupt changes, which ensures the stability of the system operation and low mechanical shock.

[0086] Example 3:

[0087] This embodiment provides a dual-manipulator cooperative trajectory planning system based on an improved artificial potential field method, used to implement the dual-manipulator cooperative trajectory planning method of Embodiment 1. The system includes:

[0088] The collision detection module is used to acquire collision distance information between the two robotic arms and between the robotic arm and obstacles in real time;

[0089] The trajectory search module is used to construct a total potential energy function consisting of a weighted sum of gravitational potential energy and repulsive potential energy for the dual robotic arm system based on the collision distance information, and to perform trajectory search in the joint space with the goal of minimizing the total potential energy function. The value of the repulsive potential energy is calculated based on the collision distance information, and the gravitational potential energy is configured to be affected by both the position deviation of the robotic arm end and the angle deviation of the joint space.

[0090] The local minimum value processing module is used to dynamically generate a virtual target pose based on the current relative pose relationship between the dual robotic arms and the obstacle when the trajectory search process gets stuck in a local minimum due to potential field balance. This virtual target pose is then used as the new search target, triggering the trajectory search module to re-perform the trajectory search to generate a trajectory segment leading to the virtual target pose. After the virtual target pose is achieved, the trajectory search module is triggered to resume using the final target pose as the search target and continue the trajectory search until the final target pose is reached.

[0091] Example 4:

[0092] This embodiment provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps of the dual-manipulator cooperative trajectory planning method of Embodiment 1.

[0093] Example 5:

[0094] This embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the dual-manipulator cooperative trajectory planning method of Embodiment 1.

[0095] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this application can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0096] In the embodiments provided in this application, it should be understood that the disclosed devices / terminal equipment and methods can be implemented in other ways. For example, the device / terminal equipment embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling or direct coupling or communication connection may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0097] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0098] If the integrated module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0099] The implementation of all or part of the processes in the methods of the above embodiments can also be accomplished by a computer program product. When the computer program product is run on a terminal device, the terminal device can implement the steps in the various method embodiments described above.

[0100] The embodiments described above are only used to illustrate the technical solutions of this application, and are not intended to limit it. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A collaborative trajectory planning method for two robotic arms based on an improved artificial potential field method, characterized in that, The method includes the following steps: Step S1: Real-time detection of collision risks between the two robotic arms and between the robotic arms and obstacles, and acquisition of collision distance information; Step S2: Based on the collision distance information, construct a total potential energy function for the dual robotic arm system, which is a weighted sum of gravitational potential energy and repulsive potential energy. The value of the repulsive potential energy is calculated based on the collision distance information, and the gravitational potential energy is configured to be affected by both the position deviation of the robotic arm end and the joint space angle deviation. Step S3: With the goal of minimizing the total potential energy function, perform trajectory search in joint space and output the optimized trajectory; Step S4: If the trajectory search process gets stuck in a local minimum due to potential field balance, a virtual target pose is dynamically generated based on the current relative pose relationship between the two robotic arms and the obstacle. The virtual target pose is used as the new search target, and the trajectory search process in step S3 is re-executed to generate a trajectory segment leading to the virtual target pose. After the virtual target pose is achieved, the final target pose is resumed as the search target, and the trajectory search process in step S3 is continued until the final target pose is reached, and the optimized trajectory is output.

2. The dual-manipulator cooperative trajectory planning method based on the improved artificial potential field method according to claim 1, characterized in that, The formula for calculating the gravitational potential energy is: ； in, Represents gravitational potential energy. X is the gravitational gain coefficient, and X is the current coordinate of the robotic arm's end effector. The coordinates of the target at the end of the robotic arm. Let be the current angle of the i-th joint. Let be the target angle of the i-th joint, and n be the number of joints in the robotic arm.

3. The dual-manipulator cooperative trajectory planning method based on the improved artificial potential field method according to claim 1, characterized in that, The formula for calculating the repulsive potential energy is: ； in, Let n represent the repulsive potential energy, and n represent the number of joints in the robotic arm. The repulsive gain coefficient is... This represents the distance from the obstacle to the i-th joint of the robotic arm. This represents the radius of the repulsive field.

4. The dual-manipulator cooperative trajectory planning method based on the improved artificial potential field method according to claim 1, characterized in that, In step S4, dynamically generating a virtual target pose includes: Based on the geometric relationship of the two robotic arms projected onto a preset plane, a virtual target direction for escape is determined; Based on the direction of the virtual target, a set of temporary joint angles are calculated as the pose of the virtual target.

5. The dual-manipulator cooperative trajectory planning method based on the improved artificial potential field method according to claim 4, characterized in that, The process of determining a virtual target direction for escape based on the geometric relationship of the dual robotic arms projected onto a preset plane includes: Make the projection line from the robotic arm tangent to the virtual circular boundary defined by a specific point on the main robotic arm to determine the virtual target angle from the rotary joints closest to the robotic arm. Based on the relative positional relationship of the two robotic arms on the projection plane, it is determined whether the slave robotic arm should be located above or below the master robotic arm.

6. The dual-manipulator cooperative trajectory planning method based on the improved artificial potential field method according to claim 5, characterized in that, The step of calculating a set of temporary joint angles as the virtual target pose based on the virtual target orientation includes: Based on the judgment result, the virtual target angle from the remaining joints of the robotic arm is increased or decreased accordingly to guide it to move toward the judged orientation; The virtual target pose is formed by the determined virtual target angles of each joint.

7. The dual-manipulator cooperative trajectory planning method based on the improved artificial potential field method according to claim 1, characterized in that, Step S1 specifically includes: performing collision detection by establishing a cylindrical-hemispherical bounding box model for the links of the robotic arm, and obtaining the collision distance information by calculating the shortest distance between the center lines of the two bounding boxes.

8. A dual-manipulator cooperative trajectory planning system based on an improved artificial potential field method, characterized in that, include: The collision detection module is used to acquire collision distance information between the two robotic arms and between the robotic arm and obstacles in real time; The trajectory search module is used to construct a total potential energy function consisting of a weighted sum of gravitational potential energy and repulsive potential energy for the dual robotic arm system based on the collision distance information, and to perform trajectory search in the joint space with the goal of minimizing the total potential energy function. The value of the repulsive potential energy is calculated based on the collision distance information, and the gravitational potential energy is configured to be affected by both the position deviation of the robotic arm end and the angle deviation of the joint space. The local minimum value processing module is used to dynamically generate a virtual target pose based on the current relative pose relationship between the dual robotic arms and the obstacle when the trajectory search process gets stuck in a local minimum due to potential field balance. This virtual target pose is then used as the new search target, triggering the trajectory search module to re-perform the trajectory search to generate a trajectory segment leading to the virtual target pose. After the virtual target pose is achieved, the trajectory search module is triggered to resume using the final target pose as the search target and continue the trajectory search until the final target pose is reached.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of the method as described in any one of claims 1 to 7.

Images