A method, system and grasping mechanism for determining a grasping path

Abstract

The present application provides a grasping path determination method, system and grasping mechanism, which can obtain a starting node and a target node; then determine multiple reachable nodes of the starting node, screen all reachable nodes, and obtain multiple path safety nodes; obtain the movement cost and heuristic cost of the intelligent manipulator at each path safety node, determine the obstacle collision risk according to the obstacle distribution information, and determine the movement fusion cost and heuristic fusion cost of each path safety node; then determine the movement compensation ratio when moving from the starting node to each path safety node; according to the movement fusion cost of each path safety node and the corresponding movement compensation ratio, the grasping path of the intelligent manipulator is smoothly planned, and then the path planning result of the intelligent manipulator is generated according to the grasping path after smooth planning. The scheme of the present application can reduce the search redundancy in the path planning process to achieve efficient planning of the grasping path.

Description

Technical Field

[0001] The present application relates to the technical field of intelligent robotic arms, and more specifically, to a grasping path determination method, system and grasping mechanism. Background Art

[0002] Intelligent robotic arms are a highly flexible and precise robotic technology in the field of intelligent manufacturing. They are usually used to perform tasks with high repetitiveness and high precision requirements. The path determination of intelligent robotic arms refers to finding an optimal path between a given starting point and end point by considering various constraints such as obstacles, time, and resources. By combining artificial intelligence and machine learning technologies, path determination technology will continue to develop, thereby providing smarter solutions for complex manufacturing environments. By combining these technologies, intelligent robotic arms can play a key role in more application scenarios to improve the efficiency and safety of overall intelligent manufacturing.

[0003] Generally, the grasping path determination of an intelligent robot arm refers to the process of finding the optimal grasping path for the intelligent robot arm to move from the initial position to the target position (for example, the position of the grasped object) in space. This process needs to consider factors such as the kinematic and dynamic constraints of the intelligent robot arm, the position of obstacles, the smoothness and feasibility of the path, etc. In a complex intelligent manufacturing environment, path planning can help the intelligent robot arm avoid obstacles and thus avoid collisions. This not only protects the intelligent robot arm itself, but also ensures the safety of the intelligent manufacturing environment and reduces accidental damage. In addition, through the grasping path, the intelligent robot arm can reach the target position in the shortest time, thereby improving production efficiency. In the existing technology, the A* algorithm is often used to consider the cost of the current path and estimate the cost from the current node to the target node to determine the optimal grasping path. However, this technology often considers path diversity when finding the optimal path (that is, it will explore multiple possible paths to ensure that the path with the lowest cost is found), which will lead to searching more nodes, thereby reducing the efficiency of determining the grasping path. Therefore, how to reduce the search redundancy in the path planning process to achieve efficient planning of the grasping path of the intelligent robot arm has become a difficult problem faced by the industry. Summary of the invention

[0004] The present application provides a grasping path determination method, system and grasping mechanism, which can reduce search redundancy in the path planning process to achieve efficient planning of the grasping path of an intelligent robotic arm.

[0005] In a first aspect, the present application provides a method for determining a crawling path, comprising the following steps:

[0006] Start the planning program of the intelligent robot arm's grasping path and obtain the starting node and target node of the intelligent robot arm;

[0007] Determine multiple reachable nodes of the starting node by using a preset step length of the intelligent robotic arm, and then perform obstacle collision screening on all reachable nodes according to a convergence threshold of the intelligent robotic arm in the grasping direction to obtain multiple path safety nodes;

[0008] Obtain the movement cost and heuristic cost of the intelligent robotic arm at each path safety node, determine the obstacle collision risk of the intelligent robotic arm under the preset step length according to the obstacle distribution information in the grasping environment, and then perform cost fusion on the movement cost and the corresponding heuristic cost of each path safety node according to the obstacle collision risk, and obtain the movement fusion cost and heuristic fusion cost of each path safety node;

[0009] Determine a movement compensation ratio when moving from the starting node to each path safety node based on the distance from the starting node to the target node and the heuristic fusion cost of each path safety node;

[0010] The grasping path of the intelligent robotic arm is smoothly planned according to the mobile fusion cost of each path safety node and the corresponding mobile compensation ratio, and then the path planning result of the intelligent robotic arm is generated according to the grasping path after smooth planning.

[0011] In some embodiments, determining the multiple reachable nodes of the starting node by the preset step length of the intelligent robotic arm specifically includes:

[0012] Get the preset step length of the intelligent robot arm;

[0013] Determine a plurality of neighborhood nodes according to the preset step size;

[0014] A plurality of reachable nodes of the starting node are divided out from all neighboring nodes according to the positional relationship between the grasping target and the intelligent robotic arm.

[0015] In some embodiments, obstacle collision screening is performed on all reachable nodes according to the convergence threshold of the intelligent robotic arm in the grasping direction, and multiple path safety nodes are obtained, specifically including:

[0016] Obtain the convergence threshold of the intelligent robot arm in the grasping direction;

[0017] Obtain obstacle model based on the grid map of the grasping environment;

[0018] Determine the distance between each reachable node and the obstacle model through the obstacle model and each reachable node;

[0019] All reachable nodes are screened based on the convergence threshold and the distance from each reachable node to the obstacle model to obtain a plurality of path safety nodes.

[0020] In some embodiments, obtaining the movement cost and the heuristic cost of the intelligent robotic arm at each path safety node specifically includes:

[0021] Select a path safety node as the selected path safety node;

[0022] Determine the moving cost of the intelligent robotic arm at the selected path safety node according to the starting node and the selected path safety node;

[0023] Determine the heuristic cost of the intelligent robotic arm at the selected path safety node according to the target node and the selected path safety node;

[0024] Continue to determine the movement cost and heuristic cost of the intelligent robotic arm at the safety nodes of the remaining path.

[0025] In some embodiments, the movement cost and the corresponding heuristic cost of each path safety node are fused according to the obstacle collision risk to obtain the movement fusion cost and the heuristic fusion cost of each path safety node, which specifically includes:

[0026] Select a path safety node as the selected path safety node;

[0027] The movement cost corresponding to the selected path safety node and the obstacle collision risk are merged into the movement risk ratio of the selected path safety node;

[0028] Determining the mobile fusion cost of the selected path safety node according to the mobile risk ratio;

[0029] The heuristic cost corresponding to the selected path safety node and the obstacle collision risk are merged into the heuristic risk ratio of the selected path safety node;

[0030] Determining the heuristic fusion cost of the selected path safety node according to the heuristic risk ratio;

[0031] Continue to determine the moving fusion cost and heuristic fusion cost of the remaining path security nodes.

[0032] In some embodiments, determining the movement compensation ratio when moving from the starting node to each path safety node based on the distance from the starting node to the target node and the heuristic fusion cost of each path safety node specifically includes:

[0033] Determine the distance from the starting node to the target node;

[0034] Select a path safety node as the selected path safety node;

[0035] Obtaining a movement compensation factor at a selected path safety node that changes with the movement distance of the intelligent robot arm;

[0036] Determine a movement compensation ratio when moving from the starting node to the selected path safety node according to the movement compensation factor, the distance from the starting node to the target node, and the heuristic fusion cost of the selected path safety node;

[0037] Continue to determine the movement compensation ratio when moving from the starting node to the remaining path safety node.

[0038] In some embodiments, the smooth planning of the grasping path of the intelligent robotic arm according to the movement fusion cost of each path safety node and the corresponding movement compensation ratio specifically includes:

[0039] Determine the cost confidence of each path safety node according to the mobile fusion cost of each path safety node and the corresponding mobile compensation ratio;

[0040] The grasping path of the intelligent robotic arm is smoothly planned according to the cost confidence of each path safety node.

[0041] In a second aspect, the present application provides a grasping path determination system, comprising:

[0042] An acquisition module is used to acquire the starting node and target node of the intelligent robotic arm after starting the planning program of the grasping path of the intelligent robotic arm;

[0043] A processing module, used to determine multiple reachable nodes of the starting node through a preset step length of the intelligent robotic arm, and then perform obstacle collision screening on all reachable nodes according to a convergence threshold of the intelligent robotic arm in the grasping direction to obtain multiple path safety nodes;

[0044] The processing module is further used to obtain the movement cost and heuristic cost of the intelligent robotic arm at each path safety node, determine the obstacle collision risk of the intelligent robotic arm under the preset step length according to the obstacle distribution information in the grasping environment, and then perform cost fusion on the movement cost and the corresponding heuristic cost corresponding to each path safety node according to the obstacle collision risk, to obtain the movement fusion cost and heuristic fusion cost of each path safety node;

[0045] The processing module is further used to determine a movement compensation ratio when moving from the starting node to each path safety node based on the distance from the starting node to the target node and the heuristic fusion cost of each path safety node;

[0046] The execution module is used to smoothly plan the grasping path of the intelligent robotic arm according to the mobile fusion cost of each path safety node and the corresponding mobile compensation ratio, and then generate the path planning result of the intelligent robotic arm according to the grasping path after smooth planning.

[0047] In a third aspect, the present application provides a grasping mechanism, including an intelligent robotic arm, which also includes the above-mentioned grasping path determination system for determining the grasping path of the intelligent robotic arm.

[0048] In a fourth aspect, the present application provides a computer-readable storage medium, in which instructions or codes are stored. When the instructions or codes are run on a computer, the computer implements the above-mentioned crawling path determination method when executing.

[0049] In the grasping path determination method, system and grasping mechanism provided by the present application, after starting the planning program of the grasping path of the intelligent robotic arm, the starting node and the target node of the intelligent robotic arm are obtained; multiple reachable nodes of the starting node are determined by the preset step size of the intelligent robotic arm, and then obstacle collision screening is performed on all reachable nodes according to the convergence threshold of the intelligent robotic arm in the grasping direction to obtain multiple path safety nodes; the movement cost and heuristic cost of the intelligent robotic arm at each path safety node are obtained, and the obstacle collision risk of the intelligent robotic arm under the preset step size is determined according to the obstacle distribution information in the grasping environment, and then the movement cost and the corresponding heuristic cost of each path safety node are cost fused according to the obstacle collision risk to obtain the movement fusion cost and heuristic fusion cost of each path safety node; the movement compensation ratio when moving from the starting node to each path safety node is determined based on the distance from the starting node to the target node and the heuristic fusion cost of each path safety node; the grasping path of the intelligent robotic arm is smoothly planned according to the movement fusion cost and the corresponding movement compensation ratio of each path safety node, and then the path planning result of the intelligent robotic arm is generated according to the grasping path after smooth planning.

[0050] It can be seen that in the present application, after obtaining the starting node and the target node of the intelligent robotic arm, first, the nodes that can be reached at the next moving step are determined by the preset step size of the intelligent robotic arm and the starting node, that is, multiple reachable nodes. Secondly, the reachable nodes with collision hazards in all reachable nodes are filtered out through the positions of obstacles at each reachable node to obtain multiple path safety nodes. Subsequently, the cost of the intelligent robotic arm moving from the starting node to each path safety node is determined, that is, the moving cost. Then, the cost from each path safety node to the target node is determined, that is, the heuristic cost. Then, the obstacle collision risk of the preset step size is determined according to the obstacle distribution information in the grasping environment. Then, the moving cost and the corresponding heuristic cost of each path safety node are fused according to the obstacle collision risk to obtain the moving fusion cost and the heuristic fusion cost of each path safety node. Again, based on the starting node, After the distance from the node to the target node and the heuristic fusion cost of each path safety node determine the movement compensation ratio of the starting node to each path safety node, the heuristic fusion cost is large in the early stage of the path search of the intelligent robotic arm, resulting in the path search tending to quickly expand the search space, that is, searching more nodes, thereby ensuring the optimal grasping path. In the later stage of the path search of the intelligent robotic arm, the heuristic fusion cost is small, resulting in the path search tending to search speed, that is, reducing the search nodes, thereby reducing the search redundancy in the path planning process. Finally, the grasping path of the intelligent robotic arm is smoothly planned according to the movement fusion cost of each path safety node and the corresponding movement compensation ratio, and then the path planning result of the intelligent robotic arm is generated according to the grasping path after smooth planning. In summary, the scheme of the present application can reduce the search redundancy in the path planning process to achieve efficient planning of the grasping path of the intelligent robotic arm. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 is a flowchart of a method for determining a grasping path according to some embodiments of the present application;

[0052] Figure 2 is a schematic diagram of a process of determining a reachable node according to some embodiments of the present application;

[0053] Figure 3 is a schematic diagram of a process for determining cost confidence according to some embodiments of the present application;

[0054] Figure 4 is a structural block diagram of a grasping path determination system according to some embodiments of the present application;

[0055] Figure 5 It is an internal structure diagram of a computer device for implementing a method for determining a crawling path according to some embodiments of the present application. DETAILED DESCRIPTION

[0056] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods. Figure 1 , which is a flow chart of a method for determining a grasping path according to some embodiments of the present application. The grasping path determination method 100 mainly includes the following steps:

[0057] In step 101, a planning program for the intelligent robotic arm grasping path is started to obtain a starting node and a target node of the intelligent robotic arm.

[0058] In specific implementation, after starting the planning program of the intelligent robotic arm's grasping path, a grid map of the grasping environment can be generated by modeling the grasping environment, and then the starting node where the intelligent robotic arm is located and the target node where the grasping target is located are obtained according to the grid map, thereby obtaining the starting node and target node of the intelligent robotic arm.

[0059] It should be noted that after obtaining the image of the grasping environment through a visual sensor, the present application combines computer vision algorithms, such as: simultaneous positioning and map construction to perform environmental modeling, and uses the modeling results as the grid map. The grid map represents the geometric information of the intelligent robotic arm, obstacles and grasping targets in the grasping environment. In addition, the grid map is a two-dimensional grid map.

[0060] In step 102, multiple reachable nodes of the starting node are determined by a preset step size of the intelligent robotic arm, and then obstacle collision screening is performed on all reachable nodes according to a convergence threshold of the intelligent robotic arm in the grasping direction to obtain multiple path safety nodes.

[0061] In some embodiments, reference Figure 2 As shown, this figure is a schematic diagram of a process of determining reachable nodes shown in some embodiments of the present application. The following steps can be used to determine multiple reachable nodes of the starting node by using a preset step length of the intelligent robotic arm:

[0062] First, in step 1021, a preset step length of the intelligent robotic arm is obtained;

[0063] Then, in step 1022, a plurality of neighboring nodes are determined according to the preset step size;

[0064] Finally, in step 1023, multiple reachable nodes of the starting node are divided from all neighboring nodes according to the positional relationship between the grasping target and the intelligent robotic arm.

[0065] It should be noted that the present application obtains the preset step length of the intelligent robotic arm through the factory settings of the intelligent robotic arm. In addition, the preset step length refers to the fixed distance moved in each step when the intelligent robotic arm performs path planning. In other embodiments, it can also be obtained by other methods, which is not limited here.

[0066] In specific implementation, determining multiple neighboring nodes according to the preset step size can be achieved in the following manner, namely: first, based on the 8-way search method in the prior art, 8 directions are obtained at the starting node, namely, directly above, directly below, directly left, directly right, upper left, lower left, upper right and lower right, and then, in the corresponding direction, a node at a preset step size away from the starting node is selected as a neighboring node, thereby obtaining multiple neighboring nodes. In other embodiments, it can also be determined by other methods, which will not be repeated here.

[0067] In specific implementation, the following method can be used to divide the multiple reachable nodes of the starting node from all neighborhood nodes through the positional relationship between the grasping target and the intelligent robotic arm, namely: according to the positional relationship between the grasping target and the intelligent robotic arm, the five neighborhood nodes closest to the target node where the grasping target is located among all neighborhood nodes corresponding to the initial node where the intelligent robotic arm is located are used as the reachable nodes in this application, thereby obtaining multiple reachable nodes of the starting node.

[0068] In some embodiments, obstacle collision screening is performed on all reachable nodes according to the convergence threshold of the intelligent robotic arm in the grasping direction, and multiple path safety nodes can be obtained by the following steps:

[0069] Obtain the convergence threshold of the intelligent robot arm in the grasping direction;

[0070] Obtain obstacle model based on the grid map of the grasping environment;

[0071] Determine the distance between each reachable node and the obstacle model through the obstacle model and each reachable node;

[0072] All reachable nodes are screened based on the convergence threshold and the distance from each reachable node to the obstacle model to obtain a plurality of path safety nodes.

[0073] It should be noted that the convergence threshold refers to the allowable error range set by the intelligent robotic arm. When the intelligent robotic arm moves in the grasping direction, if the error between the current position and the target position is less than or equal to the set threshold, the path planning is considered to be completed. If the error between the current position and the target position is greater than the set threshold, the path planning is considered to be incomplete, that is, there is a risk of collision with obstacles. Optionally, the convergence threshold of the intelligent robotic arm can be set according to the accuracy requirements of the grasping task. For example, a smaller convergence threshold is set for a high-precision grasping task, and a larger convergence threshold is set for a low-precision grasping task. In other embodiments, it can also be obtained by other methods, which will not be repeated here.

[0074] Optionally, obtaining an obstacle model based on a grid map of the grasping environment can be implemented in the following manner, namely: by detecting obstacles from sensor data when constructing a grid map, and marking corresponding cells in the grid map as obstacles. For example, when an obstacle is detected in a cell through sensor data, the cell is marked as occupied; when no obstacle is detected, the cell is marked as free, thereby using all cells marked as occupied as the obstacle model in the present application. It should be noted that the sensor data in the present application is collected by an ultrasonic sensor, and in other embodiments, it can also be obtained by other methods, which will not be repeated here.

[0075] In specific implementation, the distance from each reachable node to the obstacle model can be determined by the obstacle model and each reachable node in the following manner, namely: first, the central node of the obstacle model is obtained, and then, the Euclidean distance algorithm in the prior art is used to determine the distance from each reachable node to the central node, and then each distance is used as the distance from the corresponding reachable node to the obstacle model. In other embodiments, it can also be determined by other methods, which are not limited here.

[0076] In some embodiments, all reachable nodes are screened based on the convergence threshold and the distance from each reachable node to the obstacle model, and obtaining multiple path safety nodes can be achieved by using the following steps:

[0077] Determine the safe expansion amount of the obstacle;

[0078] Determine a safety threshold when the intelligent robotic arm approaches an obstacle according to the safety expansion amount and the convergence threshold;

[0079] All reachable nodes are screened according to the safety threshold and the distance between each reachable node and the obstacle model to obtain a plurality of path safety nodes.

[0080] In specific implementation, the safety expansion amount can be determined according to the shape regularity of the obstacle. For example, when the shape regularity of the obstacle is high, a smaller safety expansion amount is set; when the shape regularity of the obstacle is low, a larger safety expansion amount is set. In other embodiments, other methods can be used to determine the amount, which will not be repeated here.

[0081] In specific implementation, the safety threshold of the intelligent robotic arm approaching an obstacle can be determined based on the safety expansion amount and the convergence threshold in the following manner, namely: the sum of the safety expansion amount and the convergence threshold is used as the safety threshold when the intelligent robotic arm approaches an obstacle. In other embodiments, it can also be determined by other methods, which will not be repeated here.

[0082] It should be noted that the path safety node in the present application refers to a node on the grid map where the intelligent robotic arm will not collide with an obstacle when moving a preset step length; as a preferred embodiment, all reachable nodes are screened according to the safety threshold and the distance from each reachable node to the obstacle model, and multiple path safety nodes are obtained in the following manner, namely: first, a reachable node is selected, and then the distance from the reachable node to the obstacle model is obtained; secondly, when the distance from the reachable node to the obstacle model is less than the safety threshold, the reachable node is filtered; when the distance from the reachable node to the obstacle model is greater than or equal to the safety threshold, the reachable node is used as a path safety node; the above steps are repeated to screen the remaining reachable nodes, thereby obtaining multiple path safety nodes.

[0083] In step 103, the movement cost and heuristic cost of the intelligent robotic arm at each path safety node are obtained, and the obstacle collision risk of the intelligent robotic arm under the preset step length is determined according to the obstacle distribution information in the grasping environment. Then, the movement cost and the corresponding heuristic cost of each path safety node are fused according to the obstacle collision risk to obtain the movement fusion cost and heuristic fusion cost of each path safety node.

[0084] In some embodiments, obtaining the movement cost and heuristic cost of the intelligent robotic arm at each path safety node may be achieved by using the following steps:

[0085] Select a path safety node as the selected path safety node;

[0086] Determine the moving cost of the intelligent robotic arm at the selected path safety node according to the starting node and the selected path safety node;

[0087] Determine the heuristic cost of the intelligent robotic arm at the selected path safety node according to the target node and the selected path safety node;

[0088] Continue to determine the movement cost and heuristic cost of the intelligent robotic arm at the safety nodes of the remaining path.

[0089] It should be noted that the movement cost described in the present application represents the priority of the intelligent robotic arm moving from the starting node to the selected path safety node. The larger the movement cost, the lower the priority of the intelligent robotic arm moving from the starting node to the selected path safety node, and the smaller the movement cost, the higher the priority of the intelligent robotic arm moving from the starting node to the selected path safety node. As a preferred embodiment, the movement cost of the intelligent robotic arm at the selected path safety node can be determined according to the starting node and the selected path safety node. The following method is used, namely: first, the two-dimensional coordinates of the starting node and the selected path safety node in the grid map are obtained, and then the absolute value of the difference between the X-axis coordinates of the starting node and the path safety node in the two-dimensional coordinates is determined; the absolute value of the difference between the Y-axis coordinates of the starting node and the path safety node in the two-dimensional coordinates is determined, and finally the maximum value of the obtained results is used as the movement cost of the intelligent robotic arm at the selected path safety node. In other embodiments, it can also be determined by other methods, which will not be repeated here.

[0090] It should be noted that the heuristic cost described in the present application represents the priority of the intelligent robotic arm moving from the selected path safety node to the target node. The larger the heuristic cost, the lower the priority of the intelligent robotic arm moving from the selected path safety node to the target node, and the smaller the heuristic cost, the higher the priority of the intelligent robotic arm moving from the selected path safety node to the target node. As a preferred embodiment, the heuristic cost of the intelligent robotic arm at the selected path safety node can be determined according to the target node and the selected path safety node. The following method can be used to implement it, namely: first, obtain the two-dimensional coordinates of the target node and the selected path safety node in the grid map, and then determine the absolute value of the difference between the X-axis coordinates of the target node and the path safety node in the two-dimensional coordinates; determine the absolute value of the difference between the Y-axis coordinates of the target node and the path safety node in the two-dimensional coordinates, and finally use the maximum value of the obtained results as the heuristic cost of the intelligent robotic arm at the selected path safety node. In other embodiments, it can also be determined by other methods, which will not be repeated here.

[0091] In some embodiments, determining the obstacle collision risk of the intelligent robotic arm at the preset step length according to the obstacle distribution information in the grasping environment can be achieved by using the following steps:

[0092] Obtaining a moving area of ​​the intelligent robotic arm within a preset step length of the starting node;

[0093] Determine the area occupied by obstacles in the moving area, where the area occupied is the obstacle distribution information;

[0094] determining an obstacle density in a grasping environment based on the obstacle distribution information;

[0095] The obstacle collision risk of the intelligent robotic arm at the preset step length is determined according to the obstacle density.

[0096] In specific implementation, the starting node can be used as the center of the circle, and the area of ​​the circle formed by using the preset step size as the radius can be used as the moving area. In other embodiments, it can also be determined by other methods, which will not be repeated here.

[0097] In specific implementation, determining the proportion of the area of ​​obstacles in the moving area can be achieved in the following manner, namely: first, obtaining the nodes where all obstacles are located in the grid map, and then determining the area composed of the nodes where all obstacles are located; secondly, taking the area of ​​the area composed of the nodes where all obstacles are located and the area of ​​the intersection of the moving area as the proportion of the area of ​​obstacles in the moving area. In other embodiments, other methods can also be used to determine it, which is not limited here; in addition, as a preferred embodiment, determining the obstacle density in the grasping environment based on the obstacle distribution information can be achieved in the following manner, namely: taking the quotient of the proportion area corresponding to the obstacle distribution information and the moving area as the obstacle density in the grasping environment. In other embodiments, it can also be determined by other methods, which will not be repeated here.

[0098] It should be noted that the obstacle collision risk described in the present application represents the probability of the intelligent robotic arm colliding with an obstacle during the grasping process of a preset step length, that is, the degree of danger. The greater the obstacle collision risk, the higher the degree of danger of the intelligent robotic arm during the grasping process of the preset step length, and the smaller the obstacle collision risk, the lower the degree of danger of the intelligent robotic arm during the grasping process of the preset step length. As a preferred embodiment, determining the obstacle collision risk of the intelligent robotic arm at the preset step length according to the obstacle density can be achieved in the following manner, namely: first, determining the density risk coupling factor, and then taking the obstacle density as the exponent of the natural constant e. The value multiplied by the density risk coupling factor is used as the obstacle collision risk of the intelligent robotic arm under the preset step size, wherein the density risk coupling factor can be obtained by obtaining the obstacle density in the historical grasping process, and then constructing an objective function through all obstacle densities and corresponding obstacle collision risks (the goal of the objective function is to minimize the sum of squares of errors between the predicted value and the actual value), and then, the least squares method in the prior art is used to determine the optimal parameter value in the objective function, and the determined parameter value is used as the density risk coupling factor in this application. In other embodiments, it can also be determined by other methods, which are not limited here.

[0099] In some embodiments, the movement cost and the corresponding heuristic cost of each path safety node are fused according to the obstacle collision risk to obtain the movement fusion cost and the heuristic fusion cost of each path safety node, which can be achieved by the following steps:

[0100] Select a path safety node as the selected path safety node;

[0101] The movement cost corresponding to the selected path safety node and the obstacle collision risk are merged into the movement risk ratio of the selected path safety node;

[0102] Determining the mobile fusion cost of the selected path safety node according to the mobile risk ratio;

[0103] The heuristic cost corresponding to the selected path safety node and the obstacle collision risk are merged into the heuristic risk ratio of the selected path safety node;

[0104] Determining the heuristic fusion cost of the selected path safety node according to the heuristic risk ratio;

[0105] Continue to determine the moving fusion cost and heuristic fusion cost of the remaining path security nodes.

[0106] It should be noted that the mobile risk ratio described in the present application represents the cost of the intelligent robotic arm moving from the starting node to the selected path safety node under the risk of obstacle influence. The larger the mobile risk ratio is, the higher the cost of the intelligent robotic arm moving from the starting node to the selected path safety node under the risk of obstacle influence. The smaller the mobile risk ratio is, the lower the cost of the intelligent robotic arm moving from the starting node to the selected path safety node under the risk of obstacle influence. As a preferred embodiment, the mobile cost corresponding to the selected path safety node and the obstacle collision risk are merged into the mobile risk ratio of the selected path safety node, which can be achieved in the following manner, namely: first, determine the obstacle collision risk ratio. 1, and then, the mobile cost of the selected path safety node is divided by the value of the above result as the mobile risk ratio of the selected path safety node. In other embodiments, it can also be determined by other methods, which are not limited here. In specific implementation, the mobile fusion cost of the selected path safety node determined according to the mobile risk ratio can be implemented in the following manner, namely: first, the mobile risk ratio of the selected path safety node is obtained, and then the square value of the mobile risk ratio is determined, and then the value of the natural logarithm of the mobile risk ratio minus 1 is determined, and finally, the square value of the mobile risk ratio and the natural logarithm of the mobile risk ratio minus 1 are multiplied to obtain the result as the mobile fusion cost of the selected path safety node.

[0107] It should be noted that the heuristic risk ratio described in the present application represents the cost of the intelligent robotic arm moving from the selected path safety node to the target node under the risk of obstacle influence. The larger the heuristic risk ratio, the higher the cost of the intelligent robotic arm moving from the selected path safety node to the target node under the risk of obstacle influence. The smaller the heuristic risk ratio, the lower the cost of the intelligent robotic arm moving from the selected path safety node to the target node under the risk of obstacle influence. As a preferred embodiment, the heuristic cost corresponding to the selected path safety node and the obstacle collision risk are merged into the heuristic risk ratio of the selected path safety node, which can be achieved in the following manner, namely: first, determine the obstacle collision risk. The result of adding 1 to the risk, and then dividing the heuristic cost of the selected path safety node by the value of the above result is used as the heuristic risk ratio of the selected path safety node. In other embodiments, it can also be determined by other methods, which are not limited here. In specific implementation, the heuristic fusion cost of the selected path safety node determined according to the heuristic risk ratio can be implemented in the following way, namely: first, obtain the heuristic risk ratio corresponding to the selected path safety node, and then determine the square value of the heuristic risk ratio, and then determine the value of the natural logarithm of the heuristic risk ratio minus 1, and finally, multiply the square value of the heuristic risk ratio by the natural logarithm of the heuristic risk ratio minus 1 to obtain the result as the heuristic fusion cost of the selected path safety node.

[0108] It should be noted that cost fusion in this application refers to combining the risk of the intelligent robot arm's movement process with its movement cost and heuristic cost to obtain a more reasonable path cost, that is, when the path movement risk is considered, when the cost corresponding to the path safety node is large, the cost increment is reduced, and when the cost corresponding to the path safety node is small, the difference between the costs is magnified, that is, the movement risk ratio and the heuristic risk ratio are reconciled, which can reduce the cost fluctuation caused by distance changes, thereby avoiding unnecessary extreme choices in the path planning process and improving the smoothness of the grasping path. This application determines the square value of the movement cost or the heuristic cost, thereby magnifying the difference between the movement costs of each path safety node and the difference between the heuristic costs, so that a shorter path is tended to be selected in the grasping path planning, avoiding unnecessary detours. However, using the square value of the movement cost or the heuristic cost will cause the cost to change too drastically, causing the algorithm to tend to choose the local shortest rather than the global optimal path. Therefore, by introducing the natural logarithm of the movement cost and the heuristic cost, the cost growth rate is reduced, that is, the cost fluctuation caused by distance changes is reduced.

[0109] In step 104, a movement compensation ratio when moving from the starting node to each path safety node is determined based on the distance from the starting node to the target node and the heuristic fusion cost of each path safety node.

[0110] In some embodiments, determining the movement compensation ratio when moving from the starting node to each path safety node based on the distance from the starting node to the target node and the heuristic fusion cost of each path safety node can be implemented by the following steps:

[0111] Determine the distance from the starting node to the target node;

[0112] Select a path safety node as the selected path safety node;

[0113] Obtaining a movement compensation factor at a selected path safety node that changes with the movement distance of the intelligent robot arm;

[0114] Determine a movement compensation ratio when moving from the starting node to the selected path safety node according to the movement compensation factor, the distance from the starting node to the target node, and the heuristic fusion cost of the selected path safety node;

[0115] Continue to determine the movement compensation ratio when moving from the starting node to the remaining path safety node.

[0116] In specific implementation, determining the distance from the starting node to the target node can be achieved in the following manner, namely: first, obtaining the coordinates of the starting node and the target node on the grid map, and then using the coordinates of the starting node and the target node as input, using the Euclidean distance algorithm in the prior art to obtain a distance value, and using the obtained distance value as the distance from the starting node to the target node in this application. In other embodiments, it can also be determined by other methods, which will not be repeated here.

[0117] It should be noted that the movement compensation factor is set as the moving distance of the intelligent robotic arm changes. When the moving distance of the intelligent robotic arm is shorter, the present application sets a larger movement compensation factor. When the moving distance of the intelligent robotic arm is longer, the present application sets a smaller movement compensation factor.

[0118] In specific implementation, the movement compensation ratio when moving from the starting node to the selected path safety node is determined according to the movement compensation factor, the distance from the starting node to the target node and the heuristic fusion cost of the selected path safety node. The following method can be used, namely: first, the product of the heuristic fusion cost of the selected path safety node and the movement compensation factor is determined; then, the result is added to the distance from the starting node to the target node and then divided by the distance from the starting node to the target node; finally, the result is used as the movement compensation ratio when moving from the starting node to the selected path safety node.

[0119] It should be noted that the mobile compensation ratio described in the present application represents the degree of balance between the mobile fusion cost and the heuristic fusion cost considered by the grasping path planning system. The larger the mobile compensation ratio, the more the grasping path planning system is inclined to consider the heuristic fusion cost, and the smaller the mobile compensation ratio, the more the grasping path planning system is inclined to consider the mobile fusion cost.

[0120] In step 105, the grasping path of the intelligent robotic arm is smoothly planned according to the movement fusion cost of each path safety node and the corresponding movement compensation ratio, and then the path planning result of the intelligent robotic arm is generated according to the grasping path after smooth planning.

[0121] In some embodiments, smooth planning of the grasping path of the intelligent robotic arm according to the movement fusion cost of each path safety node and the corresponding movement compensation ratio can be implemented by the following steps:

[0122] Determine the cost confidence of each path safety node according to the mobile fusion cost of each path safety node and the corresponding mobile compensation ratio;

[0123] The grasping path of the intelligent robotic arm is smoothly planned according to the cost confidence of each path safety node.

[0124] Wherein, in some embodiments, reference Figure 3 As shown, the figure is a schematic diagram of a process for determining cost confidence shown in some embodiments of the present application. The cost confidence of each path safety node is determined according to the mobile fusion cost of each path safety node and the corresponding mobile compensation ratio, which can be implemented by the following steps:

[0125] First, in step 1051, a path safety node is selected as a selected path safety node;

[0126] Secondly, in step 1052, the heuristic fusion cost of the selected path safety node is compensated by the movement compensation ratio of the selected path safety node to obtain the compensation heuristic cost of the selected path safety node;

[0127] Next, in step 1053, the actual comprehensive cost of the selected path safety node is determined according to the mobile fusion cost of the selected path safety node and the corresponding compensation heuristic cost;

[0128] Then, in step 1054, the actual comprehensive cost of the remaining path safety nodes is continued to be determined;

[0129] Finally, in step 1055, the cost confidence of each path safety node is determined by all actual comprehensive costs.

[0130] In specific implementation, the heuristic fusion cost of the selected path safety node is compensated by the movement compensation ratio of the selected path safety node, and the compensation heuristic cost of the selected path safety node can be obtained in the following manner, namely: first, the movement compensation ratio and the heuristic fusion cost of the selected path safety node are obtained, and then the product of the movement compensation ratio and the heuristic fusion cost is used as the compensation heuristic cost of the selected path safety node. In other embodiments, it can also be determined by other methods, which will not be repeated here.

[0131] It should be noted that when the intelligent robotic arm moves, the system will search for more nodes due to the difference in size between the movement fusion cost and the heuristic fusion cost of the path safety node, thereby reducing the efficiency of determining the grasping path. The present application controls the balance between the movement fusion cost and the heuristic fusion cost of the intelligent robotic arm in the process of moving along the grasping path through the movement compensation ratio (i.e., multiplying the movement compensation ratio and the heuristic fusion cost), thereby avoiding the system's search redundancy and improving the efficiency of determining the grasping path.

[0132] In specific implementation, the actual comprehensive cost of the selected path safety node can be determined based on the mobile fusion cost of the selected path safety node and the corresponding compensation heuristic cost. The following method can be used for this purpose, namely: the sum of the mobile fusion cost of the selected path safety node and the corresponding compensation heuristic cost is used as the actual comprehensive cost of the selected path safety node. In other embodiments, the actual comprehensive cost can also be determined by other methods, which will not be repeated here.

[0133] It should be noted that the actual comprehensive cost represents the priority of the corresponding path safety node in the grasping path planning. The larger the actual comprehensive cost, the lower the priority of the corresponding path safety node in the grasping path planning. The smaller the actual comprehensive cost, the higher the priority of the corresponding path safety node in the grasping path planning.

[0134] In specific implementation, the cost confidence of each path safety node can be determined through all actual comprehensive costs by the following steps, namely: first, determine the maximum actual comprehensive cost and the minimum actual comprehensive cost among the actual comprehensive costs corresponding to all path safety nodes; then, select a path safety node, determine the maximum actual comprehensive cost minus the actual comprehensive cost of the selected path safety node, and divide the result by the difference between the maximum actual comprehensive cost and the minimum actual comprehensive cost; finally, subtract 1 from the result as the cost confidence of each path safety node; repeat the above steps to determine the cost confidence of each remaining path safety node.

[0135] It should be noted that the cost confidence represents the degree of confidence of the grasping path planning system in the actual comprehensive cost of the path safety node. The greater the cost confidence, the higher the degree of confidence of the grasping path planning system in the actual comprehensive cost of the path safety node. The smaller the cost confidence, the lower the degree of confidence of the grasping path planning system in the actual comprehensive cost of the path safety node.

[0136] Among them, in some embodiments, smooth planning of the grasping path of the intelligent robotic arm according to the cost confidence of each path safety node can be implemented by the following steps:

[0137] Extracting the path node after the initial node passes through a preset step length from all the path safety nodes according to the cost confidence of each path safety node;

[0138] After taking the path node as the initial transition node, repeating the above process of determining the initial transition node from the initial node until reaching the target node where the capture target is located;

[0139] The path composed of the initial node, the target node and all path nodes is used as the grasping path of the intelligent robotic arm, thereby realizing smooth planning of the grasping path of the intelligent robotic arm.

[0140] In specific implementation, the path node after a preset step length of the initial node is extracted from all path safety nodes according to the cost confidence of each path safety node. This can be achieved in the following way, namely: first, the path safety node corresponding to the maximum cost confidence is extracted from all cost confidences, and then the path safety node is used as the path node after a preset step length of the initial node.

[0141] It should be noted that the present application uses the path node as the initial transition node and then repeats the above process of determining the initial transition node from the initial node, that is, in the grasping path planning process of the intelligent robotic arm, finds all nodes with the highest cost confidence, and then uses the path composed of all path nodes, the initial node and the target node on the grid map as the grasping path of the intelligent robotic arm, so that the grasping path minimizes the path cost (that is, the shortest distance) while avoiding collision between the intelligent robotic arm and obstacles.

[0142] In some embodiments, the path planning result of the intelligent robotic arm is generated according to the grasping path after smooth planning, that is, the grasping path of the intelligent robotic arm determined after smooth planning is used as the selected grasping path of the intelligent robotic arm, and the selected allocated time of each path node in the selected grasping path is determined according to the path length of the selected grasping path of the intelligent robotic arm and the movement speed of the intelligent robotic arm, and finally the path planning result of the intelligent robotic arm is generated, that is, the path planning result is the final determined selected grasping path of the intelligent robotic arm and the selected allocated time of each path node in the selected grasping path, which will not be repeated here.

[0143] In specific implementation, the allocation time of each path node in the grasping path can be determined according to the generated grasping path length and the movement speed of the intelligent robotic arm. This can be achieved in the following manner, namely: first, the movement speed of the intelligent robotic arm is obtained, and then the length of each path node in the grasping path is divided by the movement speed to obtain the allocation time of each path node in the grasping path. In other embodiments, it can also be determined by other methods, which are not limited here.

[0144] In addition, in another aspect of the present application, in some embodiments, the present application provides a grasping path determination system, referring to Figure 4 , which is a structural block diagram of a grasping path determination system according to some embodiments of the present application, the grasping path determination system 200 includes: an acquisition module 201, a processing module 202 and an execution module 203, which are described as follows:

[0145] Acquisition module 201, in this application, acquisition module 201 is mainly used to acquire the starting node and target node of the intelligent robotic arm after starting the planning program of the intelligent robotic arm grasping path;

[0146] Processing module 202, in the present application, the processing module 202 is mainly used to determine multiple reachable nodes of the starting node through a preset step length of the intelligent robotic arm, and then perform obstacle collision screening on all reachable nodes according to a convergence threshold of the intelligent robotic arm in the grasping direction to obtain multiple path safety nodes;

[0147] In addition, the processing module 202 in the present application is also used to obtain the movement cost and heuristic cost of the intelligent robotic arm at each path safety node, determine the obstacle collision risk of the intelligent robotic arm under the preset step length according to the obstacle distribution information in the grasping environment, and then perform cost fusion on the movement cost and the corresponding heuristic cost corresponding to each path safety node according to the obstacle collision risk, and obtain the movement fusion cost and heuristic fusion cost of each path safety node;

[0148] In addition, the processing module 202 in the present application is also used to determine the movement compensation ratio when moving from the starting node to each path safety node based on the distance from the starting node to the target node and the heuristic fusion cost of each path safety node;

[0149] Execution module 203. In the present application, execution module 203 is mainly used to smoothly plan the grasping path of the intelligent robotic arm according to the mobile fusion cost of each path safety node and the corresponding mobile compensation ratio, and then generate the path planning result of the intelligent robotic arm according to the smoothly planned grasping path.

[0150] In some embodiments, the present application also provides a grasping mechanism, including an intelligent robotic arm, which also includes the above-mentioned grasping path determination system for determining the grasping path of the intelligent robotic arm, which will not be repeated here.

[0151] In addition, in some embodiments, the present application also provides a computer device, which includes a memory and a processor, the memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the computer device executes the above-mentioned crawling path determination method.

[0152] In some embodiments, reference Figure 5 , which is an internal structure diagram of a computer device using a crawling path determination method according to some embodiments of the present application. The crawling path determination method in the above embodiment can be Figure 5 The computer device 300 shown in the figure is implemented, and the computer device 300 includes at least one processor 301, a communication bus 302, a memory 303 and at least one communication interface 304.

[0153] The processor 301 may be a general-purpose central processing unit (CPU), or an application specific integrated circuit (ASIC) or one or more processors for controlling the execution of the method for determining the capture path in the present application.

[0154] The communication bus 302 is used to transmit information between the above components.

[0155] The memory 303 may be a read only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM) or other types of dynamic storage devices that can store information and instructions, or an electrically erasable programmable read only memory (EEPROM), a compact disc read only memory (CD ROM) or other optical disc storage, an optical disc storage (including a compressed optical disc, a laser disc, an optical disc, a digital versatile disc, a Blu-ray disc, etc.), a magnetic disk or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of an instruction or data structure and can be accessed by a computer, but is not limited thereto. The memory 303 may exist independently and be connected to the processor 301 via the communication bus 302. The memory 303 may also be integrated with the processor 301.

[0156] The memory 303 is used to store the program code for executing the solution of the present application, and the execution is controlled by the processor 301. The processor 301 is used to execute the program code stored in the memory 303. The program code may include one or more software modules. The method for determining the crawling path in the above embodiment can be implemented by the processor 301 and one or more software modules in the program code in the memory 303.

[0157] The communication interface 304 uses any transceiver or other device for communicating with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area networks (WLAN), etc.

[0158] In a specific implementation, as an embodiment, a computer device may include multiple processors, each of which may be a single-core (single CPU) processor or a multi-core (multi CPU) processor. The processor here may refer to one or more devices, circuits, and / or processing cores for processing data (such as computer program instructions).

[0159] The above-mentioned computer device may be a general-purpose computer device or a special-purpose computer device. In a specific implementation, the computer device may be a desktop computer, a portable computer, a network server, a personal digital assistant (PDA), a mobile phone, a tablet computer, a wireless terminal device, a communication device or an embedded device. The embodiment of the present application does not limit the type of computer device.

[0160] In addition, the present application also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned method for determining a crawling path is implemented.

[0161] In summary, in the grasping path determination method, system and grasping mechanism disclosed in the embodiments of the present application, after starting the planning program of the grasping path of the intelligent robotic arm, the starting node and the target node of the intelligent robotic arm are obtained; the multiple reachable nodes of the starting node are determined by the preset step length of the intelligent robotic arm, and then obstacle collision screening is performed on all reachable nodes according to the convergence threshold of the intelligent robotic arm in the grasping direction to obtain multiple path safety nodes; the movement cost and heuristic cost of the intelligent robotic arm at each path safety node are obtained, and the obstacle collision risk of the intelligent robotic arm under the preset step length is determined according to the obstacle distribution information in the grasping environment, and then the obstacle collision risk is determined according to the obstacle collision risk. The mobile cost corresponding to each path safety node and the corresponding heuristic cost are cost-fused to obtain the mobile fusion cost and heuristic fusion cost of each path safety node; the mobile compensation ratio when moving from the starting node to each path safety node is determined based on the distance from the starting node to the target node and the heuristic fusion cost of each path safety node; the grasping path of the intelligent robotic arm is smoothly planned according to the mobile fusion cost of each path safety node and the corresponding mobile compensation ratio, and then the path planning result of the intelligent robotic arm is generated according to the grasping path after smooth planning; the search redundancy in the path planning process can be reduced to achieve efficient planning of the grasping path.

[0162] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0163] Obviously, those skilled in the art may make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A method for determining a grasping path, characterized in that: The steps include: Start the planning program of the intelligent robot arm's grasping path and obtain the starting node and target node of the intelligent robot arm; Determine multiple reachable nodes of the starting node by using a preset step length of the intelligent robotic arm, and then perform obstacle collision screening on all reachable nodes according to a convergence threshold of the intelligent robotic arm in the grasping direction to obtain multiple path safety nodes; Obtain the movement cost and heuristic cost of the intelligent robotic arm at each path safety node, determine the obstacle collision risk of the intelligent robotic arm under the preset step length according to the obstacle distribution information in the grasping environment, and then perform cost fusion on the movement cost and the corresponding heuristic cost of each path safety node according to the obstacle collision risk, and obtain the movement fusion cost and heuristic fusion cost of each path safety node; Determine a movement compensation ratio when moving from the starting node to each path safety node based on the distance from the starting node to the target node and the heuristic fusion cost of each path safety node; The grasping path of the intelligent robot arm is smoothly planned according to the mobile fusion cost of each path safety node and the corresponding mobile compensation ratio, and then the path planning result of the intelligent robot arm is generated according to the grasping path after smooth planning; According to the obstacle collision risk, the movement cost and the corresponding heuristic cost of each path safety node are fused to obtain the movement fusion cost and the heuristic fusion cost of each path safety node, which specifically include: Select a path safety node as the selected path safety node; The movement cost corresponding to the selected path safety node and the obstacle collision risk are merged into the movement risk ratio of the selected path safety node; Determining the mobile fusion cost of the selected path safety node according to the mobile risk ratio; The heuristic cost corresponding to the selected path safety node and the obstacle collision risk are merged into the heuristic risk ratio of the selected path safety node; Determining the heuristic fusion cost of the selected path safety node according to the heuristic risk ratio; Continue to determine the mobile fusion cost and heuristic fusion cost of the remaining path security nodes; Among them, the mobile compensation ratio represents the balance degree of the grasping path planning system between considering the mobile fusion cost and the heuristic fusion cost. The larger the mobile compensation ratio is, the more the grasping path planning system is inclined to consider the heuristic fusion cost, and the smaller the mobile compensation ratio is, the more the grasping path planning system is inclined to consider the mobile fusion cost.

2. The method according to claim 1, characterized in that Determining the multiple reachable nodes of the starting node by the preset step length of the intelligent robotic arm specifically includes: Get the preset step length of the intelligent robot arm; Determine a plurality of neighborhood nodes according to the preset step size; A plurality of reachable nodes of the starting node are divided out from all neighboring nodes according to the positional relationship between the grasping target and the intelligent robotic arm.

3. The method according to claim 1, characterized in that According to the convergence threshold of the intelligent robot arm in the grasping direction, all reachable nodes are screened for obstacle collisions, and multiple path safety nodes are obtained, including: Obtain the convergence threshold of the intelligent robot arm in the grasping direction; Obtain obstacle model based on the grid map of the grasping environment; Determine the distance between each reachable node and the obstacle model through the obstacle model and each reachable node; All reachable nodes are screened based on the convergence threshold and the distance from each reachable node to the obstacle model to obtain a plurality of path safety nodes.

4. The method according to claim 1, characterized in that The specific steps to obtain the moving cost and heuristic cost of the intelligent robot arm at each path safety node include: Select a path safety node as the selected path safety node; Determine the moving cost of the intelligent robotic arm at the selected path safety node according to the starting node and the selected path safety node; Determine the heuristic cost of the intelligent robotic arm at the selected path safety node according to the target node and the selected path safety node; Continue to determine the movement cost and heuristic cost of the intelligent robotic arm at the safety nodes of the remaining path.

5. The method according to claim 1, characterized in that Determining the movement compensation ratio when moving from the starting node to each path safety node based on the distance from the starting node to the target node and the heuristic fusion cost of each path safety node specifically includes: Determine the distance from the starting node to the target node; Select a path safety node as the selected path safety node; Obtaining a movement compensation factor at a selected path safety node that changes with the movement distance of the intelligent robot arm; Determine a movement compensation ratio when moving from the starting node to the selected path safety node according to the movement compensation factor, the distance from the starting node to the target node, and the heuristic fusion cost of the selected path safety node; Continue to determine the movement compensation ratio when moving from the starting node to the remaining path safety node.

6. The method according to claim 1, characterized in that According to the mobile fusion cost of each path safety node and the corresponding mobile compensation ratio, the grasping path of the intelligent robot arm is smoothly planned, including: Determine the cost confidence of each path safety node according to the mobile fusion cost of each path safety node and the corresponding mobile compensation ratio; The grasping path of the intelligent robotic arm is smoothly planned based on the cost confidence of each path safety node.

7. A grasping path determination system, which adopts the method according to any one of claims 1 to 6 to determine the grasping path, characterized in that: The system includes: An acquisition module is used to acquire the starting node and target node of the intelligent robotic arm after starting the planning program of the grasping path of the intelligent robotic arm; A processing module, used to determine multiple reachable nodes of the starting node through a preset step length of the intelligent robotic arm, and then perform obstacle collision screening on all reachable nodes according to a convergence threshold of the intelligent robotic arm in the grasping direction to obtain multiple path safety nodes; The processing module is further used to obtain the movement cost and heuristic cost of the intelligent robotic arm at each path safety node, determine the obstacle collision risk of the intelligent robotic arm under the preset step length according to the obstacle distribution information in the grasping environment, and then perform cost fusion on the movement cost and the corresponding heuristic cost corresponding to each path safety node according to the obstacle collision risk, to obtain the movement fusion cost and heuristic fusion cost of each path safety node; The processing module is further used to determine a movement compensation ratio when moving from the starting node to each path safety node based on the distance from the starting node to the target node and the heuristic fusion cost of each path safety node; The execution module is used to smoothly plan the grasping path of the intelligent robotic arm according to the mobile fusion cost of each path safety node and the corresponding mobile compensation ratio, and then generate the path planning result of the intelligent robotic arm according to the grasping path after smooth planning.

8. A grasping mechanism, comprising an intelligent robotic arm, characterized in that: It also includes the grasping path determination system described in claim 7 for determining the grasping path of the intelligent robotic arm.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the grasping path determination method according to any one of claims 1 to 6 are implemented.

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