Coaxial rope-driven robot body obstacle avoidance method and system and computer storage medium

Abstract

The invention discloses a coaxial rope-driven robot body obstacle avoidance method and system and a computer storage medium, which are used for realizing movement obstacle avoidance of a coaxial rope-driven robot and are beneficial for the coaxial rope-driven robot to complete various tasks, such as oral examination and the like. The method comprises the following steps of: firstly, optimizing an artificial potential field function in combination with simulated annealing and an adjustment factor, and solving the problems of an artificial potential field local minimum value and an unreachable target point while realizing tail end track obstacle avoidance; secondly, on the basis of the established multi-node kinematics model, a gradient projection method is optimized, and the whole-arm obstacle avoidance problem with limited sensing information is solved while whole-arm obstacle avoidance is achieved. According to the method, it can be guaranteed that the tail end of the robot does not collide with the obstacle while reaching the target point, and it can also be guaranteed that the whole arm of the robot does not collide with the obstacle in the limited space.

Description

Technical Field

[0001] The present invention relates to the field of robot control, and in particular to a coaxial rope-driven robot embodied obstacle avoidance method and system, and a computer storage medium, which are applicable to the operation requirements of the coaxial rope-driven robot in a narrow space. Background Art

[0002] Due to the flexibility and agility of coaxial rope-driven robots, the inverse kinematics calculation of this type of robot is very complex. Therefore, motion obstacle avoidance has always been the focus and difficulty of research on this type of robot. Motion obstacle avoidance can enable the robot to complete its work tasks while avoiding obstacles, preventing unnecessary damage to the robot and the surrounding environment. For this research, previous scholars have made many contributions:

[0003] Wang et al. proposed a learning from demonstration method to plan the motion path of a manipulator based on ionic polymer-metal composites through a statistical machine learning algorithm. This method has the potential to be used for complex tasks. Chen et al. proposed a new three-dimensional neural dynamic model to obtain safer and more reliable reference trajectories. Li et al. proposed an online trajectory planning method for a flexible manipulator that can quickly generate trajectories and effectively suppress vibrations. Khan et al. proposed a model-free control framework for path planning of rigid and flexible robotic manipulators, which can effectively and accurately control the motion of the manipulator. Chen et al. proposed an obstacle avoidance and multi-target tracking method for a super-redundant modular manipulator based on Bezier curves and particle swarm optimization methods. This method can quickly and efficiently generate trajectories while achieving goals such as obstacle avoidance. Tian et al. proposed a general configuration planning method for a continuous super-redundant manipulator based on an improved artificial potential field method. This method shows high performance in terms of computational efficiency and environmental adaptability. Mu et al. proposed a dual-arc method for kinematics and configuration planning and a combined planning method based on dual-arc curves and Bezier curves, which provide guidance for the kinematics of coaxial rope-driven robots. Zhong et al. conducted kinematic analysis and trajectory planning of continuous manipulators and solved the bending coupling problem of such manipulators. Mishra et al. proposed an effective trajectory planning and dynamic tracking controller scheme for the material hysteresis effect of pneumatic continuum manipulators. Yang et al. proposed a combined kinematic motion planning method for cable-driven multi-segment continuum manipulators in confined spaces. This method can effectively solve the nonlinear motion planning problem of multi-segment continuum manipulators. Buonocore et al. proposed an obstacle avoidance path planning algorithm for virtual simulation of super-redundant manipulators, which promoted the application of such manipulators in practice. Yang et al. proposed a serpentine curve-based controller that can solve the obstacle avoidance problem of free-floating modular robots in space and the posture adjustment problem before landing.

[0004] According to the above research, so far, many articles have studied the trajectory planning of manipulators on a plane, and most of them are only applicable to single-segment rope-driven robots. There is still a lack of research on the trajectory planning algorithm of multi-segment coaxial rope-driven robots in space. In addition, there are certain deficiencies in completing embodied obstacle avoidance. Summary of the invention

[0005] The present invention aims to solve at least one of the technical problems in the related art to a certain extent. To this end, one object of the present invention is to provide a coaxial rope-driven robot embodied obstacle avoidance method and system, and a computer storage medium for realizing the coaxial rope-driven robot embodied obstacle avoidance control.

[0006] The technical solution adopted by the present invention is: a coaxial rope-driven robot embodied obstacle avoidance method, comprising the following steps:

[0007] Establish a multi-node kinematic model of the coaxial rope-driven robot;

[0008] By combining simulated annealing and adjustment factors, the artificial potential field function is optimized to achieve obstacle avoidance of the terminal trajectory of the coaxial rope-driven robot while solving the problems of local extreme points and unreachable target points of the artificial potential field.

[0009] Based on the established multi-node kinematic model, the gradient projection method is optimized to solve the obstacle avoidance problem of the whole arm of the coaxial rope-driven robot with limited sensor information.

[0010] Furthermore, for the multi-node kinematic model, if the coaxial cable-driven robot contains M segments of cable-driven mechanisms, the arm shape of the coaxial cable-driven robot is assumed to be M segments of arcs according to the equal bending assumption, and the bending angles of each joint of the same segment of the cable-driven mechanism are equal. Based on this assumption, the kinematic model of each segment of the cable-driven mechanism is established;

[0011] Set each joint as a node. If the rope-driven mechanism has N joints, divide the rope-driven mechanism into N segments and establish the kinematic relationship of each node.

[0012] According to the kinematic model of each section of the rope-driven mechanism, a multi-node kinematic model of the coaxial rope-driven robot is established.

[0013] Furthermore, for the obstacle avoidance of the coaxial rope-driven robot terminal trajectory, it is determined in real time whether the Euclidean distance (Euclidean distance) between the coaxial rope-driven robot terminal and the obstacle is less than the safety threshold. If the determination result is yes, the coaxial rope-driven robot terminal is moved away from the obstacle through the repulsive field of the artificial potential field.

[0014] It is judged in real time whether the Euclidean distance between the end of the coaxial rope-driven robot and the target point is less than the target distance. If the judgment result is no, the end of the coaxial rope-driven robot is brought close to the target point through the gravitational field of the artificial potential field.

[0015] It is determined in real time whether the coaxial rope-driven robot is trapped in the local minimum of the artificial potential field. If the judgment result is yes, it jumps out of the local minimum through simulated annealing.

[0016] Furthermore, for the whole-arm obstacle avoidance of the coaxial rope-driven robot, a limited number of landmark points are selected on the coaxial rope-driven robot as obstacle avoidance points, and it is judged in real time whether the distance between the obstacle avoidance point and the obstacle is less than the safety threshold. If the judgment result is yes, the arm shape is adjusted through gradient projection without affecting the end trajectory to achieve whole-arm obstacle avoidance.

[0017] Another technical solution adopted by the present invention is: a coaxial rope-driven robot embodied obstacle avoidance system, comprising:

[0018] Position detection unit: used to detect the current position of the coaxial rope-driven robot and the obstacle, so as to calculate the Euclidean distance between the end of the coaxial rope-driven robot and the obstacle, and the Euclidean distance between the obstacle avoidance point of the coaxial rope-driven robot and the obstacle;

[0019] Position determination unit: used to determine whether the Euclidean distance is less than the corresponding safety threshold, and whether the Euclidean distance is less than the target distance;

[0020] Obstacle avoidance planning unit: used to control the coaxial rope-driven robot to reach the end point without colliding with obstacles through the coaxial rope-driven robot's embodied obstacle avoidance algorithm;

[0021] Transmission unit: used to transmit the angular velocity and sensor information of the joint angle of the coaxial rope-driven robot to realize the motion control of the coaxial rope-driven robot.

[0022] Furthermore, the end trajectory problem of the coaxial rope-driven robot is realized through the artificial potential field, and the problems of local minimum and end inaccessibility existing in the artificial potential field are solved;

[0023] Gradient projection is used to realize obstacle avoidance of the whole arm of the coaxial rope-driven robot without affecting the end posture, and the problem of limited gradient projection sensor information is solved.

[0024] Another technical solution adopted by the present invention is: a computer storage medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the following steps:

[0025] Establish a multi-node kinematic model of the coaxial rope-driven robot;

[0026] By combining simulated annealing and adjustment factors, the artificial potential field function is optimized to achieve obstacle avoidance of the terminal trajectory of the coaxial rope-driven robot while solving the problems of local extreme points and unreachable target points of the artificial potential field.

[0027] Based on the established multi-node kinematic model, the gradient projection method is optimized to solve the obstacle avoidance problem of the whole arm of the coaxial rope-driven robot with limited sensor information.

[0028] The beneficial effects of the present invention are:

[0029] The present invention provides a coaxial rope-driven robot embodied obstacle avoidance method and system, and a computer storage medium, which are used to control the coaxial rope-driven robot to avoid obstacles while performing work tasks in a complex environment, so as to prevent unnecessary damage to the robot and the surrounding environment. Through the combination of artificial potential field and gradient projection, this method can not only ensure that the end of the robot reaches the target point without colliding with obstacles, but also ensure that the entire arm of the robot does not collide with obstacles in a confined space. The above characteristics are conducive to the coaxial rope-driven robot to complete tasks in various complex environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 It is a schematic diagram of kinematic parameters of a specific embodiment of the coaxial rope-driven robot embodied obstacle avoidance method in the present invention.

[0031] Figure 2 It is a schematic diagram of a simulated annealing algorithm for an embodied obstacle avoidance method of a coaxial rope-driven robot in the present invention.

[0032] Figure 3 It is a schematic diagram of whole-arm obstacle avoidance of a specific embodiment of a coaxial rope-driven robot embodied obstacle avoidance method in the present invention.

[0033] Figure 4 It is a schematic diagram of setting landmark points of a specific embodiment of the coaxial rope-driven robot embodied obstacle avoidance method in the present invention. DETAILED DESCRIPTION

[0034] In order to enable those skilled in the art to better understand the technical solution of the present invention, the specific technical solution of the present invention will be clearly and completely described in conjunction with the embodiments below to help those skilled in the art further understand the present invention. Obviously, the embodiments described in this case are only part of the embodiments of the present invention, not all of the embodiments. It should be pointed out that for those of ordinary skill in the art, the embodiments in this application and the features in the embodiments can be combined with each other without departing from the concept of the present invention and without conflicting with each other. Based on the embodiments in the present invention, all other embodiments obtained without creative work by those of ordinary skill in the art should belong to the disclosure and protection scope of the present invention.

[0035] In addition, the terms "first", "second", "further" and the like in the specification, claims and drawings of the present invention are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those described herein. At the same time, the terms "including" and "having" and any variations thereof in the present invention are intended to cover non-exclusive inclusions. In addition, for those of ordinary skill in the art, the specific meanings of the above terms in this case can be understood based on the specific circumstances and in combination with the prior art.

[0036] (1) Establishment of multi-node kinematic model

[0037] refer to Figure 1 Each coaxial rope-driven robot has three degrees of freedom, and the double-segment coaxial rope-driven robot has a total of 6 degrees of freedom. O ,S I ), bending angle (θ O ,θ I ) and the torsion angle around the central axis Indicates its spatial arm shape and direction.

[0038] If the joint length of the outer rope drive mechanism is H o +h o , the joint length of the inner rope drive mechanism is H I +h I , then the number of joints of the outer and inner rope drive mechanisms can be expressed by formula (1):

[0039]

[0040] The present invention introduces the node segmentation factor ξ(ξ O ,ξ I ) to establish a multi-node kinematic model. Then the multi-node transformation matrix of the outer rope-driven mechanism can be expressed by formula (2):

[0041]

[0042] in, Represents the position of the end. The position coordinate calculation method is shown in formula (3):

[0043]

[0044] Similarly, the multi-node transformation matrix of the inner rope-driven mechanism can be expressed as formula (4):

[0045]

[0046] In the present invention, the global coordinate system is defined as coinciding with the base coordinate system, and the transformation matrix can be used to obtain equation (5):

[0047]

[0048] In fact, due to structural limitations, the coaxial rope-driven robot can only rotate around the x-axis and the y-axis, and cannot rotate around the z-axis. We define the angles around the two axes as From the above positive kinematic relationship, we can know that:

[0049]

[0050] The angles α and β can be expressed as:

[0051]

[0052] in, express The corresponding elements in . Then the position and direction of the point on the coaxial rope-driven robot in the global coordinate system can be expressed by equation (8):

[0053]

[0054] With the position and direction in the global coordinate system, the velocity can be expressed as follows:

[0055]

[0056] Then the multi-node Jacobian matrix of the coaxial rope-driven robot can be expressed by (10):

[0057]

[0058] The Jacobian matrix of the coaxial rope-driven robot is not a full-rank matrix and cannot be directly inverted. Therefore, the present invention uses the Jacobian pseudo-inverse method to calculate the inverse solution of the coaxial rope-driven robot. The pseudo-inverse is defined as shown in formula (11).

[0059] J + =J T (JJ T ) -1 (11)

[0060] (2) Terminal trajectory obstacle avoidance

[0061] The present invention uses an artificial potential field to avoid obstacles on the terminal trajectory of a coaxial rope-driven robot. Simultaneously, the problem of local minimum and unreachable target point is solved by combining simulated annealing and adjustment factor. The artificial potential field is described as follows:

[0062] The gravitational potential field is positively correlated with the distance. The farther the distance between the robot and the target point, the greater the potential energy. Its expression is:

[0063]

[0064] Where k is the proportional gain coefficient of the gravitational potential field, P is the end position of the robot, and P g is the position of the target point, ||PP g || is the distance between the robot end and the target point. The gravitational force F acting on the robot attr It can be obtained from the negative gradient of the gravitational potential field:

[0065] F attr (P)=-▽U attr (P)=-k||PP g || (13)

[0066] The repulsive potential field is negatively correlated with the distance. The closer the distance between the robot and the obstacle, the greater the potential energy. Its expression is:

[0067]

[0068] in, is the distance between the robot and the obstacle, ρ0 is the influence distance of the obstacle, and η is the gain coefficient of the gravitational potential field. This shows that the robot will only be affected by U when the obstacle is within the influence distance. rep Similarly, the repulsive force F attr It can be obtained from the negative gradient of the repulsive potential field:

[0069]

[0070] When there are n obstacles, the total repulsive field is The total repulsive force is Then the total potential U of the artificial potential field is total With the combined force F total It can be expressed by the following formula:

[0071]

[0072] However, the above artificial potential field method has the following drawbacks.

[0073] ① It is easy to fall into the local minimum. total From the formula, we know that when the robot gravity F attr Total repulsion When the robots are on the same straight line, they will fall into a local minimum point, where they will oscillate or stop. The present invention solves this problem by combining simulated annealing.

[0074] ② The existence of obstacles near the target makes the target unreachable. When the target point is within the influence range of an obstacle, the attraction of the target point may be smaller than the repulsion of the obstacle, making the target unreachable. The present invention solves this problem by combining the adjustment factor.

[0075] ①Simulated annealing solves the local minimum problem:

[0076] refer to Figure 2 The basic idea of ​​the simulated annealing method is to start from a relatively high initial temperature, and as the temperature continues to drop, it combines certain probability jump characteristics to randomly search for the global optimal solution of the objective function in the solution space, that is, to probabilistically jump out of the local optimal solution and eventually tend to the global optimal solution.

[0077] The mechanism of updating the solution of the simulated annealing algorithm is based on the Metropolis criterion, which is usually expressed as:

[0078]

[0079] Among them, p represents the acceptance probability of the new solution, T represents the current temperature, and E(x new ) represents the system energy corresponding to the new solution, E(x old ) represents the system energy corresponding to the current solution. When the new solution is better, it is updated directly. When the energy of the new solution is higher than the current solution, whether to update depends on the current temperature and energy. The initial state is at point A. As the number of iterations increases, it is updated to the local optimal solution at point B. At this time, the energy of point B is lower than that of point A. After reaching point B, the update probability is calculated based on the current state, temperature and energy, and point B is jumped out. It stabilizes after reaching the global optimal point D.

[0080] The initial state is Figure 2 Point A in , as the number of iterations increases, is updated to Figure 4 The local minimum point of point B in the middle, at this time, the energy of point B is lower than point A. After reaching point B, the update probability is calculated according to the current state, temperature and energy. With the continuous decrease of the temperature parameter, combined with the probability jump characteristic, the global optimal solution of the objective function is randomly searched in the solution space, that is, the local optimal solution B can be probabilistically jumped out and eventually tend to the global optimal D.

[0081] ②Improve the potential field function to solve the problem of unreachable target point:

[0082] From the principle of artificial potential field method, we know that there are two main reasons why the robot cannot reach the target point when there are obstacles near the target point: first, the gravitational force of the target point gradually decreases as the robot approaches the target point; second, the repulsive force of the obstacle is not affected by the distance from the robot to the target point, and the repulsive force of the obstacle near the target point will be significantly greater than the gravitational force.

[0083] In order to solve the problem of unreachable target points, the present invention improves the gravity and repulsion functions by combining adjustment factors, so that gravity plays a leading role when the robot approaches the target point. An adjustment factor that increases as the distance decreases is added to the gravity function, and the improved gravitational potential field is as follows:

[0084]

[0085] Where, d S→G is the distance from the robot's starting point to the target point, ε is a positive number and satisfies ε<0.1×d S→G , calculate the negative gradient of the improved gravitational potential field and get the gravitational force as:

[0086]

[0087] Add an adjustment factor that decays with the distance from the robot to the target point to the repulsive potential field function, and the improved function is as follows:

[0088]

[0089] Taking the negative gradient, we can get the repulsive force as:

[0090]

[0091] The resultant force after improvement is:

[0092]

[0093] In the process of the robot end point gradually approaching the target point, the adjustment factor of the gravity function reduces the attenuation of gravity when approaching the target point, and the adjustment factor of the repulsion function reduces the repulsion of obstacles near the target point. The improved resultant potential field will overcome the problem that the artificial potential field is unreachable when there are obstacles near the target point.

[0094] (3) Whole-arm obstacle avoidance

[0095] For a rigid manipulator, the general formula of the obstacle avoidance algorithm based on gradient projection is given by (23):

[0096]

[0097] in, is the velocity of the degree of freedom in the configuration space, is the end velocity of the robot arm, I is the unit matrix, is the gradient vector in space used to adjust the arm shape. (IJ + J) is used to adjust the redundant degrees of freedom of the robot arm. This adjustment can achieve secondary goal reconstruction without affecting the main goal, so that the robot arm can achieve arm-type obstacle avoidance while meeting the terminal trajectory.

[0098] refer to Figure 3 This embodiment provides a general multi-segment coaxial rope-driven robot obstacle avoidance problem. Assume that the robot has N segments and is placed in a space with M obstacles. On this basis, the present invention improves the theoretical method of (23) to achieve the multi-obstacle obstacle avoidance problem of a multi-section flexible arm.

[0099] Assume that the end point of the robot is P and the target point is P g , the obstacle position is P Oi At this point, the primary goal is for the robot to successfully reach the end point, and the secondary goal is to avoid multiple obstacles. The kinematic relationship between these goals and the joint space is:

[0100]

[0101] in, is the moving speed of the end of the robot arm, is the repulsion speed of the kth obstacle on the robot’s obstacle avoidance point, J e With J k are the Jacobian matrices at the end of the robot and ξ(k), respectively. The speed of the end of the robot is planned by the artificial potential field in (10). By combining the priorities of the tasks in (24), the basic obstacle avoidance algorithm can be obtained as follows:

[0102]

[0103] The sum represents the total repulsion speed of all obstacles in the threshold area. In order to optimize the obstacle avoidance effect and make the motion smooth and effective, the adjustment coefficient α of the kth obstacle is introduced n|k With α 0|k .

[0104] refer to Figure 4 In previous applications, this algorithm usually assumes that there is a complete sensor field around the robot. Under this assumption, the closest point on the robot to the obstacle can be sensed. When the closest point enters the threshold area, the point is selected as the obstacle avoidance point for obstacle avoidance control. However, in practical applications, it is difficult for us to realize a complete sensor field. Therefore, the present invention selects fixed landmark points to realize the obstacle avoidance task, making the obstacle avoidance method easier to implement in practical applications.

[0105] Assuming that there are Q markers on the robot arm, when an obstacle enters the obstacle avoidance range, the repulsion velocity will be assigned to the direction away from the obstacle. Therefore, the obstacle avoidance algorithm can be changed to equation (26). Where s represents the sth marker of the robot.

[0106]

[0107] Formula (26) is solved iteratively until the robot reaches the specified position, and the robot's obstacle avoidance task can be achieved. and It is given by equation (27) and equation (28):

[0108]

[0109] in, represents the distance between the kth obstacle and the sth landmark of the robot, r c represents the safety threshold, r0 represents the critical distance for obstacle avoidance, and r c <r0, k2 represents the obstacle avoidance gain index when the robot approaches an obstacle.

[0110] Based on the above method, the present invention also provides a coaxial rope-driven robot embodied obstacle avoidance system, comprising:

[0111] Position detection unit: used to detect the current position of the coaxial rope-driven robot and the obstacle, so as to calculate the Euclidean distance between the end of the coaxial rope-driven robot and the obstacle, and the Euclidean distance between the obstacle avoidance point of the coaxial rope-driven robot and the obstacle;

[0112] Position determination unit: used to determine whether the Euclidean distance is less than the corresponding safety threshold, and whether the Euclidean distance is less than the target distance;

[0113] Obstacle avoidance planning unit: used to control the coaxial rope-driven robot to reach the end point without colliding with obstacles through the coaxial rope-driven robot's embodied obstacle avoidance algorithm;

[0114] Transmission unit: used to transmit the angular velocity and sensor information of the joint angle of the coaxial rope-driven robot to realize the motion control of the coaxial rope-driven robot.

[0115] In the embodied obstacle avoidance system, the terminal trajectory problem of the coaxial rope-driven robot is realized through the artificial potential field, and the problems of local minima and terminal inaccessibility of the artificial potential field are solved; the problem of whole-arm obstacle avoidance of the coaxial rope-driven robot is realized without affecting the terminal posture through gradient projection, and the problem of limited gradient projection sensor information is solved.

[0116] In addition, the present invention also provides a computer storage medium, on which a computer program is stored, and when the program is executed by a processor, the following steps are implemented:

[0117] Establish a multi-node kinematic model of the coaxial rope-driven robot;

[0118] By combining simulated annealing and adjustment factors, the artificial potential field function is optimized to achieve obstacle avoidance of the terminal trajectory of the coaxial rope-driven robot while solving the problems of local extreme points and unreachable target points of the artificial potential field.

[0119] Based on the established multi-node kinematic model, the gradient projection method is optimized to solve the obstacle avoidance problem of the whole arm of the coaxial rope-driven robot with limited sensor information.

[0120] The working process of the computer program stored on the computer storage medium can refer to the specific description of the above-mentioned coaxial rope-driven robot embodied obstacle avoidance method, which will not be repeated here.

[0121] At the same time, the method of the present invention is not only applicable to the coaxial rope-driven robot, but also applicable to various other super-redundant mechanical arms.

[0122] The above is a specific description of the preferred implementation of the present invention, but the invention is not limited to the embodiments. Those skilled in the art can make various equivalent modifications or substitutions without violating the spirit of the present invention. These equivalent modifications or substitutions are all included in the scope defined by the claims of this application.

Claims

1. A coaxial rope-driven robot obstacle avoidance method, characterized in that: The following steps are involved: Establish a multi-node kinematic model of the coaxial rope-driven robot; By combining simulated annealing and adjustment factors, the artificial potential field function is optimized to achieve obstacle avoidance of the terminal trajectory of the coaxial rope-driven robot while solving the problems of local extreme points and unreachable target points of the artificial potential field. Based on the established multi-node kinematic model, the gradient projection is optimized to solve the obstacle avoidance problem of the whole arm of the coaxial rope-driven robot with limited sensor information.

2. The coaxial rope-driven robot obstacle avoidance method according to claim 1, characterized in that: For the multi-node kinematic model, if the coaxial rope-driven robot contains M rope-driven mechanisms, the arm shape of the coaxial rope-driven robot is assumed to be M arc segments according to the equal bending assumption, and the bending angle of each joint of the rope-driven mechanism of the same segment is equal. Based on this assumption, the kinematic model of each rope-driven mechanism is established; Set each joint as a node. If the rope-driven mechanism has N joints, divide the rope-driven mechanism into N segments and establish the kinematic relationship of each node. According to the kinematic model of each section of the rope-driven mechanism, a multi-node kinematic model of the coaxial rope-driven robot is established.

3. The coaxial rope-driven robot obstacle avoidance method according to claim 1, characterized in that: For the coaxial rope-driven robot terminal trajectory obstacle avoidance, it is determined in real time whether the Euclidean distance between the coaxial rope-driven robot terminal and the obstacle is less than the safety threshold. If the judgment result is yes, the coaxial rope-driven robot terminal is moved away from the obstacle through the repulsive field of the artificial potential field; In real time, it is determined whether the Euclidean distance between the end of the coaxial rope-driven robot and the target point is less than the target distance. If the determination result is no, the end of the coaxial rope-driven robot is brought close to the target point through the gravitational field of the artificial potential field. If the determination result is yes, the coaxial rope-driven robot stops moving. It is determined in real time whether the coaxial rope-driven robot is trapped in the local minimum of the artificial potential field. If the judgment result is yes, it jumps out of the local minimum through simulated annealing.

4. The coaxial rope-driven robot obstacle avoidance method according to any one of claims 1 to 3, characterized in that: For the whole-arm obstacle avoidance of the coaxial rope-driven robot, a limited number of landmark points are selected on the coaxial rope-driven robot as obstacle avoidance points, and it is judged in real time whether the distance between the obstacle avoidance point and the obstacle is less than the safety threshold. If the judgment result is yes, the arm shape is adjusted through gradient projection without affecting the terminal trajectory to achieve whole-arm obstacle avoidance.

5. A coaxial rope-driven robot embodied obstacle avoidance system, characterized in that: include: Position detection unit: used to detect the current position of the coaxial rope-driven robot and the obstacle, so as to calculate the Euclidean distance between the end of the coaxial rope-driven robot and the obstacle, and the Euclidean distance between the landmark of the coaxial rope-driven robot and the obstacle; Position determination unit: used to determine whether the Euclidean distance is less than the corresponding safety threshold, and whether the determined Euclidean distance is less than the target distance; Obstacle avoidance planning unit: used to control the coaxial rope-driven robot to reach the end point without colliding with obstacles through the coaxial rope-driven robot's embodied obstacle avoidance algorithm; Transmission unit: used to transmit the angular velocity and sensor information of the joint angle of the coaxial rope-driven robot to realize the motion control of the coaxial rope-driven robot.

6. The coaxial rope-driven robot body obstacle avoidance system according to claim 5, characterized in that: The end trajectory of the coaxial rope-driven robot is realized through the artificial potential field, and the local minimum and end unreachable problems of the artificial potential field are solved; Gradient projection is used to realize obstacle avoidance of the whole arm of the coaxial rope-driven robot without affecting the end posture, and the problem of limited gradient projection sensor information is solved.

7. A computer storage medium, characterized in that: A computer program is stored thereon, and when the program is executed by a processor, the following steps are implemented: Establish a multi-node kinematic model of the coaxial rope-driven robot; By combining simulated annealing and adjustment factors, the artificial potential field function is optimized to achieve obstacle avoidance of the terminal trajectory of the coaxial rope-driven robot while solving the problems of local extreme points and unreachable target points of the artificial potential field. Based on the established multi-node kinematic model, the gradient projection method is optimized to solve the obstacle avoidance problem of the whole arm of the coaxial rope-driven robot with limited sensor information.

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