Reconstruction method for dynamic obstacle avoidance and performance optimization of cable-driven parallel robot

Abstract

The invention discloses a reconstruction method for dynamic obstacle avoidance and performance optimization of a cable-driven parallel robot, and relates to the technical field of cable-driven parallel robots, and the method comprises the steps: discretizing a collision-free path of an end effector into n sampling points; on the basis of geometric constraints of obstacles, an adjustable range of a cable point is stipulated, and an optimization objective function is constructed; solving a system stiffness matrix and a flexible cable tension sum of different cable outlet point coordinate combinations under each path point; and an optimization algorithm is adopted to solve an optimal cable point coordinate satisfying the constraint, and a dynamic reconstruction pose sequence is generated. By integrating the dynamic obstacle avoidance and multi-objective optimization technology, interference between the flexible cable and the obstacle is remarkably prevented, the operation reliability and overall performance of the system in a complex environment are improved on the premise of meeting movement continuity and mechanical constraints, and the dynamic response and working capacity of the robot are optimized.

Description

Technical Field

[0001] The present application relates to the field of cable-driven parallel robots, and specifically designs a reconstruction method for dynamic obstacle avoidance and performance optimization of a cable-driven parallel robot. Background Art

[0002] Cable-driven parallel robots show great potential in complex operating environments, but their flexible cables easily interfere with obstacles, creating a reliability bottleneck. Existing reconstruction technologies, such as the dynamic obstacle avoidance method described in patent CN112659233A, can expand the workspace by adjusting the cable dropout point, but they suffer from significant shortcomings: discrete reconstruction requires manual intervention and interruption of operations, while continuous reconstruction, while capable of dynamic adjustment, lacks an online optimization mechanism. This disconnects obstacle avoidance decisions from multi-objective optimization objectives such as system stiffness and energy consumption, making it difficult to adapt to dynamic environments in real time and find the optimal configuration that balances obstacle avoidance, stiffness, and energy consumption. This is especially true given the strong nonlinear correlation between cable force and dropout point location, leading to an increasingly urgent need for efficient collaborative solutions.

[0003] Therefore, in order to meet practical needs, a method for dynamic reconstruction and multi-objective trajectory collaborative optimization of a cable-driven parallel robot is provided. Summary of the Invention

[0004] In response to the defects in the existing technology, the purpose of this application is to provide a reconstruction method for dynamic obstacle avoidance and performance optimization of a cable-driven parallel robot, aiming to effectively prevent interference between flexible cables and obstacles, and improve the operating reliability and overall performance of the system in complex environments by optimizing the cable force distribution.

[0005] In order to achieve the above objectives, the technical solution adopted by this application is:

[0006] The present application provides a method for reconfiguring dynamic obstacle avoidance and performance optimization of a cable-driven parallel robot, the method comprising the following steps:

[0007] Discretize the collision-free path of the end effector into n sampling points;

[0008] Based on the geometric constraints of the obstacles, the adjustable range of the cable points is specified and the optimization objective function is constructed: K represents static stiffness, T i Indicates the tension of each cable;

[0009] Solve the system stiffness matrix K and the total cable tension ∑T for different cable exit point coordinate combinations at each path point i , where the stiffness matrix includes the attitude stiffness K1 and the configuration stiffness K2, and the static stiffness matrix K is calculated as: Where S=(x T Ω T ), x=(x,y,z) Tis the coordinate of the point in the moving coordinate system P-XYZ, is the attitude angle vector of the mobile platform;

[0010] According to the number of robot degrees of freedom n and the number of ropes m≥n, the robot is divided into planar cable-driven parallel robots and spatial cable-driven parallel robots. The optimization algorithm is used to solve the optimal cable-exit point coordinates that meet the constraints and generate a dynamic reconstructed pose sequence.

[0011] Based on the above technical solution, the method can analyze a cable-driven parallel robot with 3 to 6 degrees of freedom n and m≥n and m≥3 rope numbers. When n=3, it is a planar cable-driven parallel robot with a working space located in a two-dimensional plane. When n=4 to 6, it is a spatial cable-driven parallel robot with a working space located in a three-dimensional area. The moving platform is a non-point rigid body structure.

[0012] On the basis of the above technical solution, the planar cable-driven parallel robot uses two-dimensional coordinates to describe the cable-exit point position, and the coordinates of each cable-exit point are discretized into a two-dimensional grid point set; the spatial cable-driven parallel robot, when only adjusting the horizontal cable-exit point, uses the same discretization method as the two-dimensional planar robot. When adjusting in three dimensions, the cable-exit point coordinates are discretized into a three-dimensional voxel lattice that satisfies the extreme position constraints.

[0013] On the basis of the above technical solution, the optimization objective is constructed according to the static stiffness performance of the system and the energy consumption performance of the flexible cable. Specifically, when the stability of the robot under external disturbances needs to be emphasized, the optimization objective is the Frobenius norm of the stiffness matrix. When focusing on reducing the robot's energy consumption, the optimization target is the total tension of the flexible cable. When stiffness and energy consumption performance need to be considered comprehensively, the optimization objective function is the weighted sum of the two, that is:

[0014] where λ1+λ2=1

[0015] The optimization objective function is solved under the following constraints:

[0016]

[0017] Where T min and T max They represent the lower and upper limits of the cable tension, D and D respectively. min are the distance and lower limit of the flexible rope and obstacle respectively, X i 、X imin and X imax are the X coordinate, X lower limit and X upper limit of the i-th rope-out point in the static coordinate system, respectively. i 、Y imin and Y imaxare the Y coordinate, Y lower limit and Y upper limit of the i-th rope-out point in the static coordinate system, J is the Jacobian matrix, T is the rope tension vector, W e is the end load;

[0018] Based on the above technical solution, the optimal extraction point configuration can be obtained by any of the following methods: when the adjustable dimension of the extraction point is ≤2, the global traversal method is used to calculate the target value F of all candidate configurations in the discrete point set (applicable to low-dimensional cases); the intelligent optimization algorithm uses the extraction point coordinate range as a constraint and F as the optimization target to search for the optimal solution.

[0019] On the basis of the above technical solution, the intelligent optimization algorithm is preferably a particle swarm optimization algorithm (PSO), or can be replaced by a sequential quadratic programming (SQP) algorithm, a genetic algorithm (GA), or a simulated annealing algorithm (SA).

[0020] Compared with the prior art, the advantages of this application are:

[0021] 1. This application proposes a method for adjusting the configuration of a cable-driven parallel robot. By constructing a multi-objective optimization model that maximizes structural rigidity and minimizes cable forces, this method achieves a comprehensive analysis and optimization of the robot's stability, interference resistance, and system energy consumption. Dynamically adjusting the cable-exit point position based on the optimization results effectively selects the optimal configuration for each position in the robot's workspace, significantly improving the system's operational stability and resistance to external interference while simultaneously reducing cable energy consumption.

[0022] 2. Through the optimization and dynamic correction methods proposed in this invention, the robot's core operational performance (stability, interference immunity, and energy consumption) in complex environments is optimized, and real-time correction of the flexible cable trajectory and active obstacle avoidance control are achieved. Compared to traditional configuration adjustment methods that focus solely on static performance optimization or single-dimensional adjustments (such as stiffness or force distribution), this invention not only effectively prevents interference between the flexible cable and obstacles, but also significantly improves the overall operational reliability and overall performance of the system in complex environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0024] Figure 1 This is a flowchart of the steps of a method for reconfiguring dynamic obstacle avoidance and performance optimization of a cable-driven parallel robot according to an embodiment of the present application;

[0025] Figure 2This is a schematic structural diagram of a spatial four-cable three-degree-of-freedom cable-driven parallel robot according to an embodiment of the present application;

[0026] Figure 3 This is a schematic structural diagram of a spatial four-cable three-degree-of-freedom cable-driven parallel robot before reconstruction according to an embodiment of the present application;

[0027] Figure 4 This is a schematic diagram of the structure after triggering reconstruction when a spatial four-cable three-degree-of-freedom cable-driven parallel robot is used in an embodiment of the present application.

[0028] Figure 5 This is the distance between the flexible cable and the obstacle during reconstruction when used in a spatial four-cable three-degree-of-freedom cable-driven parallel robot in an embodiment of the present application. DETAILED DESCRIPTION

[0029] To make the purpose, technical solutions, and advantages of the embodiments of this application more clear, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0030] The embodiments of the present application are further described in detail below with reference to the accompanying drawings.

[0031] The embodiments of the present application provide a reconstruction method for dynamic obstacle avoidance and performance optimization of a cable-driven parallel robot, aiming to effectively prevent interference between flexible cables and obstacles, and to improve the operational reliability and overall performance of the system in complex environments by optimizing the cable force distribution.

[0032] To achieve the above technical effects, the overall idea of ​​this application is as follows:

[0033] A reconfiguration method for dynamic obstacle avoidance and performance optimization of a cable-driven parallel robot, the method comprising the following steps:

[0034] S1, discretize the collision-free path of the end effector into n sampling points;

[0035] S2. Based on the geometric constraints of the obstacle, the adjustable range of the cable point is specified and the optimization objective function is constructed;

[0036] S3. Calculate the system stiffness matrix and the total cable tension for different cable-out point coordinate combinations at each path point;

[0037] S4. Use the optimization algorithm to solve the optimal extraction point coordinates that meet the constraints and generate a dynamic reconstructed pose sequence.

[0038] The embodiments of the present application are further described in detail below with reference to the accompanying drawings.

[0039] See also Figures 1 to 5 As shown, an embodiment of the present application provides a method for reconfiguring dynamic obstacle avoidance and performance optimization of a cable-driven parallel robot, the method comprising the following steps:

[0040] S1, discretize the collision-free path of the end effector into n sampling points;

[0041] S2. Based on the geometric constraints of the obstacle, the adjustable range of the cable point is specified and the optimization objective function is constructed;

[0042] S3. Calculate the system stiffness matrix and the total cable tension for different cable-out point coordinate combinations at each path point;

[0043] S4. Use the optimization algorithm to solve the optimal extraction point coordinates that meet the constraints and generate a dynamic reconstructed pose sequence.

[0044] It should be noted that the core of the technical solution of the embodiment of the present application is as follows:

[0045] Discretize the end-effector trajectory based on path planning and specify the adjustable range of the cable-out point;

[0046] Construct an optimization objective function based on obstacle avoidance and performance requirements;

[0047] Set the coordinates of the cable-out point as design variables, and configure the stiffness and energy consumption optimization targets based on functional requirements;

[0048] The optimal extraction point coordinates are solved through the optimization algorithm to generate a dynamic reconstructed pose sequence.

[0049] This application uses multi-objective optimization technology to effectively prevent interference between flexible cables and obstacles while meeting international standards (such as EN13445) and functional requirements, thereby improving the operational reliability and overall performance of the system in complex environments.

[0050] In the embodiment of the present application, multi-objective optimization technology is used to effectively prevent interference between the flexible rope and obstacles while meeting standard requirements and functional needs, thereby improving the operational reliability and overall performance of the system in complex environments.

[0051] Furthermore, the method can analyze a cable-driven parallel robot with 3 to 6 degrees of freedom n and m≥n and m≥3 rope numbers, wherein when n=3, it is a planar cable-driven parallel robot with a working space located in a two-dimensional plane; when n=4 to 6, it is a spatial cable-driven parallel robot with a working space located in a three-dimensional area, and the moving platform is a non-point rigid structure.

[0052] Furthermore, for the planar cable-driven parallel robot, the position of the cable-exit point is described by two-dimensional coordinates, and the coordinates of each cable-exit point are discretized into a two-dimensional grid point set; for the spatial cable-driven parallel robot, when only the horizontal cable-exit point is adjusted, the discretization method is the same as that of the two-dimensional planar robot. When three-dimensional adjustment is performed, the coordinates of the cable-exit point are discretized into a three-dimensional voxel lattice that meets the extreme position constraints.

[0053] Furthermore, the optimization objective is constructed based on the static stiffness performance of the system and the energy consumption performance of the flexible cable. Specifically, when the robot's stability under external disturbances needs to be emphasized, the optimization objective is the Frobenius norm of the stiffness matrix. When focusing on reducing the robot's energy consumption, the optimization target is the total tension of the flexible cable. When stiffness and energy consumption performance need to be considered comprehensively, the optimization objective function is the weighted sum of the two, that is:

[0054] where λ1+λ2=1

[0055] The optimization objective function is solved under the following constraints:

[0056]

[0057] Where T min and T max They represent the lower and upper limits of the cable tension, D and D respectively. min are the distance and lower limit of the flexible rope and obstacle respectively, X i 、X imin and X imax are the X coordinate, X lower limit and X upper limit of the i-th rope-out point in the static coordinate system, respectively. i 、Y imin and Y imax are the Y coordinate, Y lower limit and Y upper limit of the i-th rope-out point in the static coordinate system, J is the Jacobian matrix, T is the rope tension vector, W e is the end load;

[0058] Furthermore, the optimal extraction point configuration can be obtained by any of the following methods: when the adjustable dimension of the extraction point is ≤2, the global traversal method is used to calculate the target value F of all candidate configurations in the discrete point set (applicable to low-dimensional cases); the intelligent optimization algorithm uses the extraction point coordinate range as a constraint and F as the optimization target to search for the optimal solution.

[0059] Furthermore, the intelligent optimization algorithm is preferably a particle swarm optimization algorithm (PSO), or can be replaced by a sequential quadratic programming (SQP) algorithm, a genetic algorithm (GA), or a simulated annealing algorithm (SA).

[0060] Based on the technical solution of the embodiment of this application, the specific process is as follows during implementation:

[0061] Step 1: Select a collision-free motion path for the end effector of the cable-connected parallel robot, discretize the path into n key sampling points according to preset intervals, and form a motion trajectory in a discretized workspace.

[0062] Step 2: Based on the positions of each path point of the cable-driven parallel robot in the workspace, the adjustable range of the cable point is specified based on the geometric constraints of the obstacles, and an optimization function is constructed with the goal of maximizing the structural stiffness and minimizing the total cable tension as the optimal configuration selection principle. The objective function is: K represents static stiffness, T i represents the tension of each cable, and its constraint conditions are: Where T min and T max They represent the lower and upper limits of the cable tension, D and D respectively. min are the distance and lower limit of the flexible rope and obstacle respectively, X i 、X imin and X imax are the X coordinate, X lower limit and X upper limit of the i-th rope-out point in the static coordinate system, respectively. i 、Y imin and Y imax are the Y coordinate, Y lower limit and Y upper limit of the i-th rope-out point in the static coordinate system, J is the Jacobian matrix, T is the rope tension vector, W e For the end load.

[0063] Step 3: Calculate the system stiffness matrix and the total cable tension of the cable-driven parallel robot at different adjustable coordinate combinations at each path point in the workspace. The stiffness calculation includes posture stiffness and configuration stiffness. The calculation process of the static stiffness matrix K at the end effector posture is as follows:

[0064] Step 3.1: Construct the static stiffness equation:

[0065] Where S=(x T Ω T ), x=(x,y,z) T is the coordinate of the point in the moving coordinate system P-XYZ, is the attitude angle vector of the mobile platform.

[0066] Step 3.2: Calculate the attitude stiffness K1, which is calculated as follows:

[0067]

[0068] In the formula

[0069] Step 3.3: Derive the matrices in the K1 calculation formula:

[0070] When a certain cable force point on the end effector undergoes a small displacement dx, the original cable direction vector T is updated to T', and at the same time, the direction unit vector u also changes to a new unit vector The increment is recorded as du.

[0071]

[0072] Right now

[0073] L i represents the length of the rope. When the end effector rotates around point P, the force position of the rope also produces a corresponding displacement change dr=dx=dΩ×r. The following formula can be obtained:

[0074]

[0075] Step 3.4: Calculate the cable stiffness as follows:

[0076]

[0077] Where E represents the elastic coefficient of the cable, and A represents the cross-sectional area of ​​the cable.

[0078] Step 4: Use the particle swarm optimization algorithm to solve the optimal cable-exit point coordinates of the cable-driven parallel robot at each path point in the workspace that meets the mechanical constraints, and then obtain the dynamic reconstructed pose distribution map on the continuous path of the workspace. The particle velocity update formula of the particle swarm optimization algorithm is:

[0079]

[0080] In the formula, vector x i represents the position of each particle i in the solution space, w represents the inertia weight, which is used to adjust the balance between global search and local search, c1 and c2 are learning factors, which represent the learning ability of individuals and groups respectively, and r1 and r2 are values ​​generated from the random interval [0,1] to add randomness to the search process. The position update formula is:

[0081]

[0082] According to the above formula, the number of iterations in this embodiment is 100.

[0083] Figure 2This is a schematic diagram of the model structure of a spatial four-cable three-degree-of-freedom cable-driven parallel robot. The robot can perform translation along the X and Z directions and rotation around the Y axis in the XOZ plane, with a total of three degrees of freedom.

[0084] The base of this cable-driven parallel robot measures 200cm × 200cm × 180cm. The moving platform is a 20cm × 20cm × 10cm cube weighing 1kg. Four cable dropout points are located at the four corners of the base, with the first, second, third, and fourth dropout points accessible. The cable tension is limited to a minimum of 1N and a maximum of 300N.

[0085] To validate the path planning method, a simulation experiment was conducted. A cylindrical obstacle with a radius of 25 cm and a length of 100 cm was set up, with the coordinates of its central axis endpoints at (50, 100, 100) and (150, 100, 100). The safe distance between the end effector and the obstacle was set at 1-2 cm.

[0086] For the flexible cable, when the end effector moves to the sixth path point, flexible cables 1 and 2 will collide with the obstacle. When the end effector moves to the fifth path point, reconstruction is triggered. After the rope exit point is reconstructed, the motion platform will continue to move along the remaining path points until it reaches the target position. Figure 3 and Figure 4 , are the positions of the rope-exiting points before and after reconstruction, respectively. Figure 5 is the distance between the four flexible ropes and the obstacles in the path from the starting point to the target point, and the thin line is the set safety distance threshold. Figure 5 As can be seen, the four cables remained at a safe distance throughout the entire path. In summary, the reconfigured robot successfully avoided obstacles and maintained a safe distance at the remaining path points. This demonstrates that the reconfigured robot structure is more stable and better able to withstand external interference.

[0087] The details of this application are as follows:

[0088] First, a collision-free motion path of the end effector is selected and the path is discretized to form a discretized motion trajectory.

[0089] Secondly, according to the actual working path point positions and based on the geometric constraints of obstacles, the adjustable range of the cable points is specified, and a multi-objective optimization function is constructed to maximize the structural stiffness and minimize the total cable tension.

[0090] Then the system stiffness matrix and the total cable tension are solved for different combinations of adjustable coordinates of the cable points. The stiffness calculation includes attitude stiffness and configuration stiffness.

[0091] Finally, the particle swarm optimization algorithm is used to solve the optimal cable-exit point of the robot under the mechanical constraints and obtain the reconstructed pose graph.

[0092] This method can be applied to cable-driven parallel robots, effectively preventing interference between the flexible cables and obstacles, and improving the operational reliability and overall performance of the system in complex environments.

[0093] In the description of this application, it should be noted that the terms "upper" and "lower" etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on this application.

[0094] It is worth noting that although the technical solutions and preferred embodiments of the present invention have been described in detail above in conjunction with the accompanying drawings, the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned implementation methods are merely illustrative. Relevant technical personnel in this field can be inspired by the present invention and make many forms without departing from the purpose of the present invention and the scope of protection of the claims. These all fall within the scope of protection of the present invention.

Claims

1. A reconstruction method for dynamic obstacle avoidance and performance optimization of a cable-driven parallel robot, characterized in that: The method comprises the following steps: Discretize the collision-free path of the end effector into n sampling points; Based on the geometric constraints of the obstacles, the adjustable range of the cable points is specified and the optimization objective function is constructed: K represents static stiffness, T i Indicates the tension of each cable; Solve the system stiffness matrix K and the total cable tension ∑T for different cable exit point coordinate combinations at each path point i , where the stiffness matrix includes the attitude stiffness K1 and the configuration stiffness K2, and the static stiffness matrix K is calculated as: Where S=(x T Ω T ), x=(x,y,z) T is the coordinate of the point in the moving coordinate system P-XYZ, is the attitude angle vector of the mobile platform; According to the number of robot degrees of freedom n and the number of ropes m≥n, the robot is divided into planar cable-driven parallel robots and spatial cable-driven parallel robots. The optimization algorithm is used to solve the optimal cable exit point position coordinates that meet the constraints and generate the corresponding dynamic reconstructed pose sequence.

2. The method for reconfiguring dynamic obstacle avoidance and performance optimization of a cable-driven parallel robot according to claim 1, wherein: The method can analyze a cable-driven parallel robot with 3 to 6 degrees of freedom n and m≥n and m≥3 rope numbers. When n=3, it is a planar cable-driven parallel robot with a workspace located in a two-dimensional plane. When n=4 to 6, it is a spatial cable-driven parallel robot with a workspace located in a three-dimensional area. The moving platform is a non-point rigid body structure.

3. The method for reconfiguring dynamic obstacle avoidance and performance optimization of a cable-driven parallel robot according to claim 1, wherein: For the planar cable-driven parallel robot, the position of the cable-exit point is described by two-dimensional coordinates, and the coordinates of each cable-exit point are discretized into a two-dimensional grid point set; for the spatial cable-driven parallel robot, when only the horizontal cable-exit point is adjusted, the discretization method is the same as that of the two-dimensional planar robot. When three-dimensional adjustment is performed, the coordinates of the cable-exit point are discretized into a three-dimensional voxel lattice that meets the extreme position constraints.

4. The method for reconfiguring dynamic obstacle avoidance and performance optimization of a cable-driven parallel robot according to claim 1, wherein: The optimization objective is constructed based on the static stiffness performance of the system and the energy consumption performance of the flexible cable. Specifically, when the robot's stability under external disturbances needs to be emphasized, the optimization objective is the Frobenius norm of the stiffness matrix. When focusing on reducing the robot's energy consumption, the optimization target is the total tension of the flexible cable. When stiffness and energy consumption performance need to be considered comprehensively, the optimization objective function is the weighted sum of the two, that is: where λ1+λ2=1 The optimization objective function is solved under the following constraints: Where T min and T max They represent the lower and upper limits of the cable tension, D and D respectively. min are the distance and lower limit of the flexible rope and obstacle respectively, X i 、X imin and X imax are the X coordinate, X lower limit and X upper limit of the i-th rope-out point in the static coordinate system, respectively. i 、Y imin and Y imax are the Y coordinate, Y lower limit and Y upper limit of the i-th rope-out point in the static coordinate system, J is the Jacobian matrix, T is the rope tension vector, W e For the end load.

5. The method for reconfiguring dynamic obstacle avoidance and performance optimization of a cable-driven parallel robot according to claim 1, wherein: The optimal extraction point configuration can be obtained by any of the following methods: when the adjustable dimension of the extraction point is ≤2, a global traversal method is used to calculate the target value F of all candidate configurations in the discrete point set (applicable to low-dimensional cases); an intelligent optimization algorithm is used to search for the optimal solution with the extraction point coordinate range as a constraint and F as the optimization target.

6. The method for reconfiguring dynamic obstacle avoidance and performance optimization of a cable-driven parallel robot according to claim 1, wherein: The intelligent optimization algorithm is preferably a particle swarm optimization algorithm (PSO), or can be replaced by a sequential quadratic programming (SQP) algorithm, a genetic algorithm (GA), or a simulated annealing algorithm (SA).

Images