Industrial robot dynamic obstacle avoidance method oriented to man-machine cooperation safety

Abstract

The invention discloses an industrial robot dynamic obstacle avoidance method for man-machine cooperation safety, and the method specifically comprises the steps: obtaining the information of a human body collision body in real time through a depth camera, and updating and synchronizing the information to an industrial robot coordinate system; designing a probability field RRT-Connect algorithm, and rapidly planning a feasible initial path; after an initial path is generated, rapid path detection is carried out, and whether obstacle shielding and track shielding exist on the path is detected; if no shielding exists, a path optimization strategy is carried out; if it is detected that shielding exists, on the basis of the initial path, path regeneration is carried out on the basis of a probability field RRT-Connect algorithm and a path optimization strategy; and after the path is regenerated, the steps are repeated until the robot completes collision-free movement according to the path. According to the method, online dynamic obstacle avoidance in the movement process of the robot is achieved, man-machine safety and machining task continuity are guaranteed, and the method can be widely applied to man-machine cooperation safety obstacle avoidance tasks.

Description

Technical Field

[0001] The present invention belongs to the technical field of human-machine collaborative safety, and specifically relates to a dynamic obstacle avoidance method for an industrial robot oriented to human-machine collaborative safety. Background Art

[0002] With the continuous development of modern manufacturing, human-robot collaboration has gradually become the development direction of intelligent manufacturing. Human-robot collaboration places humans and robots in the same space and time, inevitably posing safety risks to humans during robot assembly and processing. Traditional safety measures that separate humans and robots in space and time are not applicable to human-robot collaboration scenarios. Existing safety assurance methods, such as emergency stops after collisions detected through torque monitoring and speed limits based on the distance between the human and the robot, prevent further harm to the human body. However, these measures interrupt the robot's processing tasks and reduce processing efficiency. Dynamic obstacle avoidance can plan a collision-free path based on current obstacle information while ensuring the safety of both humans and robots, driving the robot to the initial target position. It is an effective means to ensure human-robot safety and improve processing efficiency.

[0003] Currently, the main methods for robot path planning and obstacle avoidance include ant colony algorithms, genetic algorithms, A* algorithms, Dijkstra algorithms, RRT algorithms, artificial potential field methods, and DWA. Ant colony algorithms and genetic algorithms are computationally intensive and time-consuming, making them unsuitable for dynamic environments. A* and Dijkstra algorithms require a large amount of space to store environmental information, and while they have certain advantages for path planning in low-dimensional spaces, they are not suitable for industrial robots with six degrees of freedom. The DWA algorithm is commonly used for low-dimensional mobile robots, but the complexity of searching in the six-dimensional joint velocity space of industrial robots increases significantly. Artificial potential field methods are very effective for local path planning, but they suffer from local minima and unreachable targets. They are often used for single-target trajectory planning in dynamic environments. When applied to serial multi-joint industrial robots, potential field design and dynamics solutions are required for each joint, making them less suitable for trajectory avoidance involving coupled multi-joint targets. The RRT algorithm is a sampling-based path planning method that uses a collision detection module to determine path validity. It eliminates the need to explicitly construct the entire configuration space and boundaries. It is suitable for motion planning problems in high-dimensional spaces and complex constraints. However, the algorithm's randomness in high-dimensional spaces can reduce its efficiency. Existing RRT and its variants mostly focus on static environments. Dynamic planning algorithms, such as DRRT, while enhancing their adaptability to dynamic environments, also weaken their real-time response efficiency and path stability, making them difficult to meet the requirements of high-frequency dynamic obstacle avoidance and continuous human-machine obstacle avoidance in industrial collaboration scenarios. Summary of the Invention

[0004] In response to the problems existing in the above-mentioned prior art, the present invention proposes a dynamic obstacle avoidance method for industrial robots for human-machine collaborative safety, so as to realize dynamic obstacle avoidance of industrial robots, improve the real-time response efficiency and path stability of the algorithm, and meet the needs of high-frequency dynamic obstacles and continuous obstacle avoidance of humans and machines in industrial collaboration scenarios.

[0005] In order to achieve the above technical objectives, the present invention provides the following technical solutions: A dynamic obstacle avoidance method for industrial robots oriented to human-machine collaboration safety, which specifically includes the following steps: S1. Obtain human collision body information in real time through the depth camera and update it to the industrial robot coordinate system; S2. Design the probability field RRT-Connect algorithm to quickly plan a feasible initial path; S3. After generating the initial path, perform a quick path detection to check whether there are any obstacles or track obstructions on the path; if there are no obstacles, perform a path optimization strategy; S4. If occlusion is detected, the path is regenerated based on the probability field RRT-Connect algorithm and path optimization strategy on the basis of the initial path; S5. After the path is regenerated, steps S3-S4 are repeated until the robot moves along the path without collision.

[0006] Furthermore, step S1 specifically includes: S11. Based on the color image obtained by the depth camera, use the OpenPose human detection network to detect key points of the human body in the color image and obtain the 2D coordinates of the key points of the human body in the color image; S12, according to the depth map provided by the depth camera, the 2D coordinates of the key points of the human body are mapped to the depth map to obtain the 3D coordinates of the key points of the human body, which is expressed as follows: ; ; in, is the 2D coordinate of the key point, is the value of the depth map on the corresponding pixel, is the internal parameter corresponding to the camera, 3D coordinates of key points of the human body; S13, using the obtained 3D coordinates of the key points of the human body, assembling the human body collision body in a split manner, modeling the collision body by taking the joints of the human limbs, the torso, and the neck as cylinders, and modeling the collision body by taking the head as a sphere, and assembling the whole collision body of the human body; S14, looping through steps S11-S13, performing real-time detection on the real-time image captured by the depth camera, and obtaining real-time 3D coordinates of key points of the human body.

[0007] Furthermore, the probability field RRT-Connect algorithm in step S2 adds probability field sampling on the basis of the RRT-Connect algorithm architecture, changing the sampling strategy of the two trees in the original algorithm; the probability field sampling integrates the gravitational field idea of ​​the artificial potential field method, converting the attractive force and the repulsive force into the gravitational probability field and the repulsive probability field. For a single tree, a gravitational probability field is generated at the joint value in the joint space corresponding to its end point, guiding the tree to explore toward the end point, and the existing nodes in the tree generate a repulsive probability field, which promotes the divergent growth of the tree.

[0008] Furthermore, the sampling strategy of each tree in the probability field RRT-Connect algorithm is as follows: P1, record the end point of the initial path is the target point for tree growth, and the starting point of the initial path As the starting point of tree growth, record i Path nodes The first i There are nodes; each node is represented by: ; ; ; in, 、 、 The target point, the starting point, i Six joint angles of existing nodes; P2, taking the first joint angle as an example, Generate gravitational probability field, probability density function of gravitational probability field For normal distribution, the formula is: ; ; in, is the angle value of the first joint of the robot; is the mean value, and in the gravitational probability field its value is The first joint angle value of is the standard deviation; P3, also taking the first joint angle as an example, the starting point With existing nodes Generate a repulsive probability field, and the probability density function of the repulsive probability field is set to an inverted normal distribution, which is expressed as follows: ; ; ; ; in, is the probability density function of the repulsive force probability field, is the normal distribution function, which is used Substitute the calculation into ; is the mean, for , ;for , ; is the standard deviation; P4. Added a uniformly distributed probability density function within the range of joint angles. , increase the sampling probability of the end point and reduce the sampling probability of existing nodes. The formula is expressed as: ; in, is the minimum joint value within the reachable range of the first joint angle, is the maximum joint value within the reachable range of the first joint angle; P5, probability density function of weighted mixed normal distribution, inverted normal distribution and uniform distribution, to obtain the overall sampling probability density in the first joint angle , the formula is: ; in, is the weight of the normal distribution, is a uniformly distributed weight, 、 For the i , j The weights of an inverted normal distribution, is the number of inverted normal distributions; Normalize the overall sampling probability density to ensure that the integral is 1. The formula is: ; in, is the overall sampling probability density after normalization of the first joint angle; P6. Repeat steps P2-P5 to obtain the normalized overall adoption probability density of the remaining joint angles. Use the rejection sampling method to generate six randomly sampled joint values ​​according to the reachable range of the joints, merge them into the final sampling point, and use it as a node in the tree.

[0009] Furthermore, the fast path detection in step S3 includes forward path detection and reverse path detection, the forward path is the path of the robot moving from the starting point to the end point, and the reverse path is the path of the robot moving from the end point to the starting point.

[0010] More specifically, the fast path detection in step S3 is as follows: After obtaining the initial path, a virtual robot is built based on the CAD file using robot inverse solution, trajectory interpolation, and collision detection algorithms; First, perform forward path detection, drive the virtual robot to move at high speed from the starting point to the end point along the initial path, and record the angles of each joint of the robot in real time during the movement. According to the obstacle information, when it is determined that the initial path will be blocked by an obstacle and a collision will occur, record the last forward collision-free path point in the initial path. ; When the forward path detection determines that the robot will collide when moving along the initial path, the reverse path detection is triggered, and the virtual robot is driven to move at high speed from the end point to the starting point along the initial path. During the movement, the joint angles of the robot are also recorded in real time. According to the obstacle information, the last reverse collision-free path point in the initial path is recorded. ; Finally, the fast path detection determines that 、 The initial paths between them will collide and path replanning is required.

[0011] Furthermore, the path optimization strategy in step S3 is specifically as follows: First, for two separated path points 、 The path between them is quickly collided. If the robot can directly Exercise to If no collision occurs, delete the intermediate path points ; Afterwards This process is continued until all paths between the paths collide with each other. The path with the intermediate paths removed is retained to complete the optimization.

[0012] Further, step S4 is specifically as follows: Get the forward collision-free path point and reverse collision-free pathpoints After that, keep the initial path starting point arrive , the initial path end point arrive The original path of and As the new starting point and end point, RRT-Connect planning based on probability field arrive new trajectory; At the same time, during the path regeneration, the following mechanism is established: when the fast path detection determines that a collision will occur, but the real robot has not collided, on the one hand, the path regeneration is triggered, and on the other hand, the real robot is set to only press to The trajectory moves until a new path is generated. If the real robot has reached point, the real robot waits for the new path in place.

[0013] Based on the above technical solution, the present invention has at least the following beneficial effects: The present invention proposes a dynamic obstacle avoidance method for industrial robots aimed at human-machine collaborative safety, which realizes online dynamic obstacle avoidance during the robot's movement, ensuring human-machine safety and the continuity of processing tasks.

[0014] The present invention designs a probability field RRT-Connect method for the robot joint space. By applying the gravitational probability field of the target point and the repulsive probability field of the existing nodes, it guides the rapid exploration of the search tree and can quickly generate a feasible path between two points in space.

[0015] The present invention establishes a mechanism for rapid path detection and path regeneration, realizes dynamic path generation, establishes a path optimization strategy, shortens the robot's movement time, and ultimately achieves online obstacle avoidance for the robot under dynamic obstacles.

[0016] The dynamic obstacle avoidance method for industrial robots proposed in the present invention can ensure the safety of humans and machines while enabling the robot to avoid obstacles and reach the target task point. It can be applied to human-machine collaborative safety obstacle avoidance tasks. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a flow chart of a dynamic obstacle avoidance method for industrial robots oriented to human-machine collaboration safety proposed by the present invention; Figure 2 Schematic diagram of tree growth in the probability field RRT-Connect algorithm designed by the present invention; Figure 3 Schematic diagram of fast path detection and path regeneration in the method proposed by the present invention. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solutions and advantages of the present invention more clear, the following Figure 1-Figure 3 It should be understood that the specific embodiments described herein are only used to illustrate the present invention and are not intended to limit the present invention.

[0019] Although the steps in the present invention are arranged with numbers, they are not intended to limit the order of the steps. Unless the order of the steps is clearly stated or the execution of a step requires other steps as a basis, the relative order of the steps can be adjusted. It is understood that the term "and / or" used herein refers to and covers any and all possible combinations of one or more of the associated listed items.

[0020] like Figure 1 As shown, the present invention proposes a dynamic obstacle avoidance method for industrial robots oriented to human-machine collaboration safety, which specifically includes the following steps: S1. Obtain human collision body information in real time through the depth camera and update it to the industrial robot coordinate system; As a preferred embodiment, step S1 specifically includes: S11. Based on the color image obtained by the depth camera, use the OpenPose human detection network to detect key points of the human body in the color image and obtain the 2D coordinates of the key points of the human body in the color image; S12, according to the depth map provided by the depth camera, the 2D coordinates of the key points of the human body are mapped to the depth map to obtain the 3D coordinates of the key points of the human body, which is expressed as follows: ; ; in, is the 2D coordinate of the key point, is the value of the depth map on the corresponding pixel, is the internal parameter corresponding to the camera, 3D coordinates of key points of the human body; S13, using the obtained 3D coordinates of the key points of the human body, assembling the human body collision body in a split manner, modeling the collision body by taking the joints of the human limbs, the torso, and the neck as cylinders, and modeling the collision body by taking the head as a sphere, and assembling the whole collision body of the human body; S14, looping through steps S11-S13, performing real-time detection on the real-time image captured by the depth camera, and obtaining real-time 3D coordinates of key points of the human body.

[0021] S2. Design a probability field RRT-Connect algorithm to quickly plan a feasible initial path; in this application, the probability field RRT-Connect algorithm adds probability field sampling on the basis of the RRT-Connect algorithm architecture, changes the sampling strategy of the two trees in the original algorithm, and can quickly plan a collision-free path between two points; the probability field sampling integrates the gravitational field idea of ​​the artificial potential field method, converts the attraction and repulsion into gravitational probability field and repulsion probability field, and for a single tree, a gravitational probability field is generated at the joint value in the joint space corresponding to its end point, guiding the tree to explore the end point, and the existing nodes in the tree generate a repulsion probability field, which promotes the divergence of the tree's growth.

[0022] More specifically, the sampling strategy for each tree in the probability field RRT-Connect algorithm is as follows: P1, record the end point of the initial path is the target point for tree growth, and the starting point of the initial path As the starting point of tree growth, record i Path nodes The first i There are nodes; each node is represented by: ; ; ; in, 、 、 The target point, the starting point, i Six joint angles of existing nodes; P2, such as Figure 2 As shown, taking the first joint angle as an example, Generate gravitational probability field, probability density function of gravitational probability field For normal distribution, the formula is: ; ; in, is the angle value of the first joint of the robot; is the mean value, and in the gravitational probability field its value is The first joint angle value of is the standard deviation; P3, also taking the first joint angle as an example, the starting point With existing nodes Generate a repulsive probability field, and the probability density function of the repulsive probability field is set to an inverted normal distribution, which is expressed as follows: ; ; ; ; in, is the probability density function of the repulsive force probability field, is the normal distribution function, which is used Substitute the calculation into ; is the mean, for , ;for , ; is the standard deviation, in this application 、 According to the experience setting, the probability density is controlled to be dispersed or concentrated according to the needs.

[0023] P4. Added a uniformly distributed probability density function within the range of joint angles. , increase the sampling probability of the end point and reduce the sampling probability of existing nodes. The formula is expressed as: ; in, is the minimum joint value within the reachable range of the first joint angle, is the maximum joint value within the reachable range of the first joint angle; P5, probability density function of weighted mixed normal distribution, inverted normal distribution and uniform distribution, to obtain the overall sampling probability density in the first joint angle , the formula is: ; in, is the weight of the normal distribution, is a uniformly distributed weight, 、 For the i , j The weights of an inverted normal distribution, is the number of inverted normal distributions; It should be noted that in this application, the number of inverted normal distributions is equal to the number of existing nodes plus the number of nodes, that is, the i-th inverted normal distribution corresponds to the i-th existing node. At the same time, for the convenience of formula expression, setting i=0 represents the starting point. .

[0024] Normalize the overall sampling probability density to ensure that the integral is 1. The formula is: ; in, is the overall sampling probability density after normalization of the first joint angle; P6. Repeat steps P2-P5 to obtain the normalized overall adoption probability density of the remaining joint angles. Use the rejection sampling method to generate six randomly sampled joint values ​​according to the reachable range of the joints and merge them into the final sampling point.

[0025] Each sampling point in the tree corresponds to a path node; six random joint values ​​combined together specify a path node (the robot's six joint values ​​correspond to a unique point in space).

[0026] S3. After generating the initial path, perform a quick path detection to check whether there are any obstacles or track obstructions on the path. The quick path detection includes forward path detection and reverse path detection. The forward path is the path the robot moves from the starting point to the end point, and the reverse path is the path the robot moves from the end point to the starting point.

[0027] As a preferred embodiment, Figure 3 As shown, the fast path detection is specifically as follows: After obtaining the initial path, a virtual robot is built based on the CAD file using robot inverse solution, trajectory interpolation, and collision detection algorithms; First, perform forward path detection, drive the virtual robot to move at high speed from the starting point to the end point along the initial path, and record the angles of each joint of the robot in real time during the movement. According to the obstacle information, when it is determined that the initial path will be blocked by an obstacle and a collision will occur, record the last forward collision-free path point in the initial path. ; In this application, the obstacle information is the human collision body information obtained in step S1; When the forward path detection determines that the robot will collide when moving along the initial path, the reverse path detection is triggered, and the virtual robot is driven to move at high speed from the end point to the starting point along the initial path. During the movement, the joint angles of the robot are also recorded in real time. According to the obstacle information, the last reverse collision-free path point in the initial path is recorded. ; Finally, the fast path detection determines that 、 The initial paths between them will collide and path replanning is required.

[0028] If there is no occlusion, the path optimization strategy is performed; specifically: First, for two separated path points 、 The path between them is quickly collided. If the robot can directly Exercise to If no collision occurs, delete the intermediate path points ; Afterwards This process is continued until all paths between the paths collide with each other. The path with the intermediate paths removed is retained to complete the optimization.

[0029] S4. If occlusion is detected, the path is regenerated based on the probability field RRT-Connect algorithm and path optimization strategy on the basis of the initial path; As a preferred embodiment, step S4 is specifically as follows: Get the forward collision-free path point and reverse collision-free pathpoints After that, keep the initial path starting point arrive , the initial path end point arrive The original path of and As the new starting point and end point, RRT-Connect planning based on probability field arrive new trajectory; At the same time, during the path regeneration, the following mechanism is established: when the fast path detection determines that a collision will occur, but the real robot has not collided, on the one hand, the path regeneration is triggered, and on the other hand, the real robot is set to only press to The trajectory moves until a new path is generated. If the real robot has reached This mechanism parallelizes the real robot's motion and path regeneration, minimizing the real robot's waiting time and effectively improving planning efficiency.

[0030] S5. After the path is regenerated, steps S3-S4 are repeated until the robot moves along the path without collision.

[0031] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above and that the invention can be embodied in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the invention is defined by the appended claims, not the foregoing description, and all variations within the meaning and range of equivalents of the claims are intended to be included therein. Any reference sign in a claim should not be construed as limiting the claim to which it relates.

[0032] In addition, it should be understood that although this specification is described in terms of implementation methods, not every implementation method contains only one independent technical solution. This narrative method of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other implementation methods that can be understood by those skilled in the art.

Claims

1. A dynamic obstacle avoidance method for industrial robots oriented to human-machine collaboration safety, characterized in that: The specific steps include: S1. Obtain human collision body information in real time through the depth camera and update and synchronize it to the industrial robot coordinate system; Step S1 specifically includes: S11. Based on the color image obtained by the depth camera, use the OpenPose human detection network to detect key points of the human body in the color image and obtain the 2D coordinates of the key points of the human body in the color image; S12, according to the depth map provided by the depth camera, the 2D coordinates of the key points of the human body are mapped to the depth map to obtain the 3D coordinates of the key points of the human body, which is expressed as follows: ； ； in, is the 2D coordinate of the key point, is the value of the depth map on the corresponding pixel, is the internal parameter corresponding to the camera, 3D coordinates of key points of the human body; S13, using the obtained 3D coordinates of the key points of the human body, assembling the human body collision body in a split manner, modeling the collision body by taking the joints of the human limbs, the torso, and the neck as cylinders, and modeling the collision body by taking the head as a sphere, and assembling the whole collision body of the human body; S14, looping through steps S11-S13, performing real-time detection on the real-time image captured by the depth camera to obtain real-time 3D coordinates of key points of the human body; S2. Design the probability field RRT-Connect algorithm to quickly plan a feasible initial path; S3. After generating the initial path, perform a quick path detection to check whether there are any obstacles or track obstructions on the path; if there are no obstacles, perform a path optimization strategy; S4. If occlusion is detected, the path is regenerated based on the probability field RRT-Connect algorithm and path optimization strategy on the basis of the initial path; S5. After the path is regenerated, steps S3-S4 are repeated until the robot moves along the path without collision.

2. The method for industrial robot dynamic obstacle avoidance for human-machine collaboration safety according to claim 1, characterized in that: The probability field RRT-Connect algorithm in step S2 adds probability field sampling on the basis of the RRT-Connect algorithm architecture, changing the sampling strategy of the two trees in the original algorithm; the probability field sampling integrates the gravitational field idea of ​​the artificial potential field method, converting the attractive and repulsive forces into gravitational probability fields and repulsive probability fields. For a single tree, a gravitational probability field is generated at the joint value in the joint space corresponding to its end point, guiding the tree to explore toward the end point, and the existing nodes in the tree generate a repulsive probability field, promoting the divergent growth of the tree.

3. The method for industrial robot dynamic obstacle avoidance for human-machine collaboration safety according to claim 2, characterized in that: The sampling strategy for each tree in the probability field RRT-Connect algorithm is as follows: P1, record the end point of the initial path is the target point for tree growth, and the starting point of the initial path As the starting point of tree growth, record i Path nodes The first i There are nodes; each node is represented by: ； ； ； in, 、 、 The target point, the starting point, i Six joint angles of existing nodes; P2, taking the first joint angle as an example, Generate gravitational probability field, probability density function of gravitational probability field For normal distribution, the formula is: ； ； in, is the angle value of the first joint of the robot; is the mean value, and in the gravitational probability field its value is The first joint angle value of is the standard deviation; P3, also taking the first joint angle as an example, the starting point With existing nodes Generate a repulsive probability field, and the probability density function of the repulsive probability field is set to an inverted normal distribution, which is expressed as follows: ； ； ； ； in, is the probability density function of the repulsive force probability field, is the normal distribution function, which is used Substitute the calculation into ; is the mean, for , ;for , ; is the standard deviation; P4. Added a uniformly distributed probability density function within the range of joint angles. , increase the sampling probability of the end point and reduce the sampling probability of existing nodes. The formula is expressed as: ； in, is the minimum joint value within the reachable range of the first joint angle, is the maximum joint value within the reachable range of the first joint angle; P5, probability density function of weighted mixed normal distribution, inverted normal distribution and uniform distribution, to obtain the overall sampling probability density in the first joint angle , the formula is: ； in, is the weight of the normal distribution, is a uniformly distributed weight, 、 For the i , j The weights of the inverted normal distribution, is the number of inverted normal distributions; Normalize the overall sampling probability density to ensure that the integral is 1. The formula is: ； in, is the overall sampling probability density after normalization of the first joint angle; P6. Repeat steps P2-P5 to obtain the normalized overall adoption probability density of the remaining joint angles. Use the rejection sampling method to generate six randomly sampled joint values ​​according to the reachable range of the joints, merge them into the final sampling point, and use it as a node in the tree.

4. The method for industrial robot dynamic obstacle avoidance for human-machine collaboration safety according to claim 1, characterized in that: The fast path detection in step S3 includes forward path detection and reverse path detection. The forward path is the path of the robot moving from the starting point to the end point, and the reverse path is the path of the robot moving from the end point to the starting point.

5. The method for industrial robot dynamic obstacle avoidance for human-machine collaboration safety according to claim 4, characterized in that: The fast path detection in step S3 is specifically as follows: After obtaining the initial path, a virtual robot is built based on the CAD file using robot inverse solution, trajectory interpolation, and collision detection algorithms; First, perform forward path detection, drive the virtual robot to move at high speed from the starting point to the end point along the initial path, and record the angles of each joint of the robot in real time during the movement. According to the obstacle information, when it is determined that the initial path will be blocked by an obstacle and a collision will occur, record the last forward collision-free path point in the initial path. ; When the forward path detection determines that the robot will collide when moving along the initial path, the reverse path detection is triggered, and the virtual robot is driven to move at high speed from the end point to the starting point along the initial path. During the movement, the joint angles of the robot are also recorded in real time. According to the obstacle information, the last reverse collision-free path point in the initial path is recorded. ; Finally, the fast path detection determines that 、 The initial paths between them will collide and path replanning is required.

6. The method for industrial robot dynamic obstacle avoidance for human-machine collaboration safety according to claim 1, characterized in that: The path optimization strategy in step S3 is specifically as follows: First, for two separated path points 、 The path between them is quickly collided. If the robot can directly Exercise to If no collision occurs, delete the intermediate path points ; Afterwards This process is continued until all paths between the paths collide with each other. The path with the intermediate paths removed is retained to complete the optimization.

7. The method for industrial robot dynamic obstacle avoidance for human-machine collaboration safety according to claim 5, characterized in that: Step S4 is specifically as follows: Get the forward collision-free path point and reverse collision-free pathpoints After that, keep the initial path starting point arrive , the initial path end point arrive The original path of and As the new starting point and end point, RRT-Connect planning based on probability field arrive new trajectory; At the same time, during the path regeneration, the following mechanism is established: when the fast path detection determines that a collision will occur, but the real robot has not collided, on the one hand, the path regeneration is triggered, and on the other hand, the real robot is set to only press to The trajectory moves until a new path is generated. If the real robot has reached point, the real robot waits for the new path in place.

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