A Path Planning Method and Device for a Six-Axis Industrial Robotic Arm Based on an Improved RRT Algorithm

Abstract

This invention discloses a path planning method and apparatus for a six-axis industrial robotic arm based on an improved RRT algorithm. The method first constructs a sampling constraint region, then uses a target bias strategy to adaptively optimize and expand a random tree within the constraint region, generating new nodes and performing collision detection. Next, an angle constraint strategy is used to construct an angle constraint region, determining whether the new node is within this region. If not, a new node is resampled and generated as the parent node for the next planning iteration. Then, it is determined whether the distance between the new node and the target point is less than a set threshold. If it is, the presence of obstacles is further checked. If no obstacles are found, the new node is directly connected to the target point, generating an initial path. Finally, redundant points in the initial path are removed to obtain the planned path. The purpose of this invention is to reduce the randomness in the search process and improve the quality and efficiency of path planning.

Description

Technical Field

[0001] This invention belongs to the field of robotic arm path planning technology, specifically relating to a six-axis industrial robotic arm path planning method and device based on an improved RRT algorithm. Background Technology

[0002] Path planning technology, a fundamental and crucial topic in the research of six-axis industrial robotic arms, aims to solve the problem of finding an optimal motion path in obstacle-prone working environments, given a starting point and a desired target point, and according to certain evaluation criteria, that connects the starting point to the target point while avoiding all obstacles. Numerous scholars both domestically and internationally have conducted in-depth analysis and research in the field of path planning, proposing various algorithms and technical solutions. Among them, the RRT (Rapidly-exploring Random Tree) algorithm has gained widespread popularity in robotic arm path planning due to its advantages such as probabilistic completeness, strong versatility, and ease of implementation, especially its excellent performance in handling high-dimensional space problems. However, the RRT algorithm also has some significant drawbacks, such as high randomness in the search process, leading to slow search speed, and often resulting in tortuous paths with low quality.

[0003] To overcome these shortcomings of the RRT algorithm and further improve its efficiency and path quality, scholars both domestically and internationally have conducted extensive research on improvements. For example, some have proposed the RRT* algorithm, which effectively improves path length through strategies such as reselecting parent nodes and rewiring. However, this improvement also increases the time cost of path planning, affecting the algorithm's real-time performance. Others have proposed a bidirectional RRT algorithm, which accelerates the search speed by simultaneously expanding two trees from the starting and target points. However, this method is essentially still a global random search, and the quality of the planned paths is not ideal. Furthermore, some scholars have attempted to combine other algorithms with the RRT algorithm to achieve better planning results. For instance, the idea of ​​an artificial potential field is introduced into the RRT algorithm, shortening the overall path planning time by improving the growth direction of the random trees. However, this method still cannot prevent the algorithm from excessively exploring useless spaces, thus wasting computational resources. Alternatively, combining the RRT algorithm with reinforcement learning algorithms improves the feasibility of planning but also significantly increases the computational load, which is not conducive to real-time applications.

[0004] In summary, while some progress has been made in improving the RRT algorithm, these improvements mainly focus on increasing convergence speed and reducing path length. Effective optimization methods are still lacking for addressing the core issues of excessive sampling randomness and poor path quality in the RRT algorithm. Therefore, how to further improve the RRT algorithm, reduce the randomness in its search process, and improve the quality and efficiency of path planning remains a crucial issue that urgently needs to be addressed in the field of path planning. Summary of the Invention

[0005] To address the problems existing in the prior art, this invention provides a path planning method and device for a six-axis industrial robotic arm based on an improved RRT algorithm, aiming to reduce the randomness in the search process and improve the quality and efficiency of path planning.

[0006] To solve the above-mentioned technical problems, the present invention is achieved through the following technical solution:

[0007] According to a first aspect of the present invention, a path planning method for a six-axis industrial robotic arm based on an improved RRT algorithm is provided, comprising:

[0008] S1. Construct a sampling restriction region, use a target bias strategy to perform adaptive optimization expansion of the random tree within the restriction region, generate new nodes, and determine whether the new nodes pass the collision detection. If they pass, proceed to S2. If they fail, resample within the restriction region until they pass.

[0009] S2. Construct an angle-constrained region using an angle constraint strategy. Determine whether the new node in S1 is within the angle constraint region. If it is, use the new node in S1 as the parent node for the next planning step. If it is not, resample and generate a new node within the angle constraint region, and use that node as the parent node for the next planning step.

[0010] S3. Determine if the distance between the new node generated in S2 and the target point is less than the set threshold. If it is not less than the threshold, return to S1 to continue the adaptive optimization and expansion of the random tree. If it is less than the threshold, determine if there is an obstacle between the new node generated in S2 and the target point. If there is an obstacle, return to S1 to continue the adaptive optimization and expansion of the random tree. If there is no obstacle, connect the new node generated in S2 directly to the target point, complete the search, and generate the initial path.

[0011] S4. Remove redundant points from the initial path to obtain the planned path.

[0012] In one possible implementation of the first aspect, constructing the sampling restriction region specifically involves:

[0013] Determine sampling points and the parent node in the random tree that is closest to the sampling point Connect the target points With parent node , with target point With parent node The line segment that forms the diameter is used to construct a circle, and this area is the sampling restriction area.

[0014] In one possible implementation of the first aspect, the adaptive optimization expansion of the random tree within the restricted region using a target bias strategy specifically involves:

[0015] Set a threshold Its range is between (0, 1);

[0016] When random tree selects sampling points At that time, generate a random number between (0, 1). ;

[0017] like Then set the sampling point as the target point. Perform target bias sampling;

[0018] like If so, then random sampling will be performed.

[0019] In one possible implementation of the first aspect, the construction of the angle-constrained region using an angle constraint strategy specifically involves:

[0020] Start point Each with the parent node and target point Connect them to obtain the angle-constrained region.

[0021] In one possible implementation of the first aspect, the adaptive optimization extension of the random tree specifically includes:

[0022] The distance between the node and the obstacle To determine the step size and set the first level of safety distance Level 2 safety distance And three different step sizes, the three different step sizes being the large step size. Normal step length and small step length ;

[0023] When the distance between the node and the obstacle Greater than Level 2 safety distance At that time, a large step size was adopted. ;

[0024] When the distance between the node and the obstacle Between Level 1 and 2 Level 2 safety distance At that time, use normal step size ;

[0025] When the distance between the node and the obstacle Less than Level 1 safety distance At that time, a small step size is adopted. .

[0026] In one possible implementation of the first aspect, removing redundant points in the initial path specifically involves:

[0027] Traverse the nodes in the initial path. Let the nodes from the starting point to the target point be numbered from 1 to n. Starting from the first path node, connect the subsequent nodes in the path sequentially. If there is no collision between nodes 1 and 2 when connecting them, try connecting the next node. Continue until an obstacle is encountered and a collision occurs when connecting to the m-th node. At this point, directly connect node 1 to node m-1, and use node m-1 as the new starting point. Repeat the above process iteratively until the target point is reached. <m<n。

[0028] In one possible implementation of the first aspect, after removing redundant points in the initial path, the method further includes:

[0029] The curves were fitted using cubic B-spline curves.

[0030] According to a second aspect of the present invention, an apparatus is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the aforementioned six-axis industrial robotic arm path planning method based on an improved RRT algorithm.

[0031] According to a third aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing a computer program, which, when executed by a processor, implements the aforementioned path planning method for a six-axis industrial robotic arm based on an improved RRT algorithm.

[0032] According to a fourth aspect of the present invention, a computer program product is provided, which, when executed by a processor, implements the aforementioned six-axis industrial robotic arm path planning method based on an improved RRT algorithm.

[0033] Compared with the prior art, the present invention has at least the following beneficial effects:

[0034] This invention provides a path planning method for a six-axis industrial robotic arm based on an improved RRT algorithm. By constructing a sampling constraint region and employing a target bias strategy, the method guides a random tree to grow towards the target point with a certain probability, while restricting the expansion of the random tree within a specific region. This improvement effectively solves the problem of excessive randomness in the traditional RRT algorithm, reduces the algorithm's exploration in useless spaces, and significantly improves the efficiency and convergence speed of path planning. The traditional RRT algorithm uses a fixed step size to expand the random tree, resulting in limited exploration efficiency. This invention adaptively optimizes the expansion of the random tree, fully utilizing information from the environment and obstacles to dynamically adjust the expansion direction and step size of nodes, thereby accelerating node generation and expansion speed and further improving the algorithm's exploration efficiency. An angle constraint strategy is introduced to construct an angle constraint region, ensuring that newly generated nodes expand under the condition of satisfying the robotic arm's motion constraints. This strategy effectively avoids the problems of tortuous paths and numerous inflection points generated by the traditional RRT algorithm, improving the smoothness and executability of the path. Paths generated by the traditional RRT algorithm typically contain a large number of redundant points and inflection points, resulting in tortuous and unsmooth paths. This invention, after generating the initial path, performs smooth optimization by removing redundant points, resulting in a simpler and smoother final planned path. This ensures the robotic arm can complete tasks smoothly and efficiently during operation. By reducing randomness and optimizing path generation and expansion strategies, this invention significantly reduces the computational complexity of the algorithm and improves the real-time performance of path planning, enabling it to better meet the real-time path planning needs of six-axis industrial robotic arms in practical applications. In summary, this invention, by improving the sampling strategy, expansion method, and path optimization method of the RRT algorithm, effectively solves the problems of high randomness, poor path quality, and low computational efficiency in traditional RRT algorithms, significantly improving the efficiency and path quality of path planning for six-axis industrial robotic arms.

[0035] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0036] To more clearly illustrate the technical solutions in the specific embodiments of the present invention, the drawings used in the description of the specific embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0037] Figure 1 This is an overall flowchart of the path planning method for a six-axis industrial robotic arm based on the improved RRT algorithm according to an embodiment of the present invention;

[0038] Figure 2 This is a schematic diagram of the restricted area of ​​the sampling points in the example;

[0039] Figure 3 This is a schematic diagram illustrating the angle limitation of the sampling points in an embodiment.

[0040] Figure 4 This is a schematic diagram illustrating the adaptive optimization extension of an embodiment.

[0041] Figure 5 This is a schematic diagram illustrating the removal of redundant points in the secondary optimization of the path in the example embodiment;

[0042] Figure 6 This is a schematic diagram of the secondary optimization and smoothing process for the path in the example. Detailed Implementation

[0043] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0044] Combination Figure 1 As shown, this invention provides a path planning method for a six-axis industrial robotic arm based on an improved RRT algorithm. Addressing the problems of weak goal orientation and excessive exploration of useless spatial regions in traditional RRT algorithms, this method enhances the goal orientation of the algorithm by introducing a sampling region constraint strategy, avoiding the random tree from exploring unnecessary spatial regions and reducing the generation of useless nodes. Secondly, an adaptive optimization expansion strategy is combined to accelerate the convergence of the random tree and reduce path generation time. Finally, the initially planned path is subjected to secondary optimization to improve path quality. Specifically, the method includes the following steps:

[0045] S1. Construct a sampling restriction region, and use a target bias strategy to perform adaptive optimization expansion of the random tree within the restriction region to generate new nodes. Determine whether the new node passes the collision detection. If it passes, proceed to S2. If it fails, resample within the restriction region until it passes.

[0046] like Figure 2 As shown, after sampling in space using a target bias strategy, the specific steps for constructing the sampling constraint region are: determining the sampling points. and the parent node in the random tree that is closest to the sampling point Connect the target points With parent node , with target point With parent node The line segment that forms the diameter is used to construct a circle, and this area is the sampling restriction area.

[0047] A sampling region restriction strategy is introduced to enhance the algorithm's goal orientation. First, a goal bias strategy is introduced, causing the random tree to grow towards the target point with a certain probability, enhancing the goal orientation of the random tree expansion and improving algorithm efficiency. Specifically, the goal bias strategy is used to adaptively optimize the expansion of the random tree within the restricted region, which involves setting a threshold. Its range is between (0, 1); when the random tree selects sampling points At that time, generate a random number between (0, 1). ;like Then set the sampling point as the target point. Perform target bias sampling; if If so, then random sampling will be performed.

[0048]

[0049] Specifically, the adaptive optimization extension of the random tree is as follows:

[0050] The distance between the node and the obstacle To determine the step size and set the first level of safety distance Level 2 safety distance And three different step sizes, the three different step sizes being the large step size. Normal step length and small step length When the distance between the node and the obstacle Greater than Level 2 safety distance At that time, a large step size was adopted. This accelerates the exploration of unknown environments by random trees; when the distance between a node and an obstacle... Between Level 1 and 2 Level 2 safety distance At that time, use normal step size When the distance between the node and the obstacle Less than Level 1 safety distance At that time, a small step size is adopted. This enhances the ability of random trees to explore complex environments.

[0051]

[0052] Adaptive optimization expansion strategies can better expand new nodes according to different environments, enhance the algorithm's exploration capabilities, reduce invalid searches, and improve the algorithm's efficiency.

[0053] S2. Construct an angular constraint region using an angular constraint strategy, and determine whether the new node in S1 is within the angular constraint region. If it is, use the new node in S1 as the parent node for the next planning; if not, resample within the angular constraint region to generate a new node, and use this node as the parent node for the next planning.

[0054] That is to say, after obtaining a new node through region restriction, further optimization judgment is made on it according to the angular restriction conditions, as Figure 3 shown. Specifically, the construction of the angular constraint region using the angular constraint strategy is as follows: Connect the starting point to the parent node and the target point respectively to obtain the angular constraint region.

[0055] By restricting the sampling region, the exploration of the random tree in the invalid space can be effectively reduced. And as the parent node continuously approaches the target point, the sampling region is also continuously restricted and compressed, thus accelerating the convergence speed of the algorithm.

[0056] S3. Determine whether the distance between the new node generated in S2 and the target point is less than the set threshold. If it is not less than, return to S1 to continue the adaptive optimization expansion of the random tree. If it is less than, determine whether there is an obstacle between the new node generated in S2 and the target point. If there is an obstacle, return to S1 to continue the adaptive optimization expansion of the random tree. If there is no obstacle, directly connect the new node generated in S2 and the target point. There is no step size limit when directly connecting, and the search is completed and the initial path is generated, as Figure 4 shown.

[0057] S4. Remove the redundant points in the initial path and perform fitting processing with a cubic B-spline curve to obtain the final planned path.

[0058] Specifically, as Figure 5 shown, removing the redundant points in the initial path is as follows:

[0059] Traverse the nodes in the initial path. Assume that the node numbers of the initial path from the starting point to the target point are 1 to n. Then, starting from the first path node, connect the subsequent nodes in the path in sequence. If there is no collision with an obstacle when connecting between the 1st and 2nd nodes, try to connect the next node until a collision occurs with an obstacle when connecting to the mth node (1 < m < n). At this time, directly connect the 1st node and the (m - 1)th node, and use the (m - 1)th node as the new starting point. Repeat the above process and perform iteration until reaching the target point.

[0060] After removing redundant points from the path, some inflection points will still remain. To ensure the smoothness of the planned motion path, cubic B-splines are used to fit and optimize the path, generating a smoother path. B-spline curves are linear combinations of B-spline basis functions, and the formula for a k-th order B-spline curve is shown below.

[0061]

[0062] in, For node vectors, As control points, Let be the basis function of the k-th order B-spline curve, and its formula is shown below.

[0063]

[0064] In a cubic B-spline curve, the node vectors are set to a non-decreasing set, i.e. ,in , Therefore, its basis functions can be derived from equation (5), as shown in the following equation.

[0065]

[0066] Therefore, the formula for a cubic B-spline curve can be derived as follows.

[0067]

[0068] like Figure 6 As shown, the path after redundancy removal is fitted with a cubic B-spline curve, which effectively removes the inflection points in the path and makes the turning points of the path smoother.

[0069] In summary, the present invention can solve the problems of existing path planning methods being limited to convergence speed and path length, and failing to effectively handle the excessive randomness of the algorithm itself and the poor quality of the planned path. It can be widely applied to various working scenarios of six-axis industrial robotic arms and has certain theoretical research and practical application value.

[0070] In another embodiment of the present invention, a computer device is provided, comprising a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions from the computer storage medium to achieve a corresponding method flow or corresponding function. The processor described in this embodiment of the present invention can be used for the operation of a path planning method for a six-axis industrial robotic arm based on an improved RRT algorithm.

[0071] In another embodiment of the present invention, a storage medium is provided, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device used to store programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the computer device and extended storage media supported by the computer device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, the storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be high-speed RAM or non-volatile memory, such as at least one disk storage device. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the path planning method for a six-axis industrial robotic arm based on an improved RRT algorithm in the above embodiments.

[0072] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0073] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0074] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0075] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0076] This invention also provides a computer program product, which is used to execute any of the above-described path planning methods for a six-axis industrial robotic arm based on an improved RRT algorithm. Since the computer program product provided by this invention belongs to the same inventive concept as the above-described path planning method for a six-axis industrial robotic arm based on an improved RRT algorithm, it possesses all the advantages of the above-described path planning method for a six-axis industrial robotic arm based on an improved RRT algorithm. Therefore, the beneficial effects of the computer program product provided by this invention will not be elaborated upon here.

[0077] In this invention, the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to a specific feature, structure, material, or characteristic described in connection with that embodiment or example, which is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0078] Finally, it should be noted that the above-described embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A six-axis industrial robot path planning method based on an improved RRT algorithm, characterized in that, The method comprises the following steps: S1, constructing a sampling limit area, performing adaptive optimization expansion of a random tree in the limit area by using a target bias strategy, generating a new node, and judging whether the new node passes the collision detection, if yes, performing S2, if not, re-sampling in the limit area until passing; the construction of the sampling limit area is specifically: determining sampling points and the parent node in the random tree closest to the sampling point , connecting the target point with the parent node , the line segment formed by the target point and the parent node is a diameter, and a circle is constructed, and the area is the sampling limit area; The adaptive optimization expansion of the random tree is specifically: Setting a threshold ranging between (0, 1); When the random tree selects the sampling point , a random number between (0, 1) is generated ; If then set the sample point as the target point Target bias sampling is performed; If then random sampling is performed; S2, constructing an angle constraint area by using an angle constraint strategy, and judging whether the new node in S1 is in the angle constraint area; if yes, taking the new node in S1 as the parent node for next planning, if not, re-sampling in the angle constraint area and generating a new node, and taking the node as the parent node for next planning; S3, judging whether the distance between the new node generated in S2 and the target point is less than a set threshold; if not, returning to S1 to continue the adaptive optimization expansion of the random tree; if yes, judging whether there is an obstacle between the new node generated in S2 and the target point; if yes, returning to S1 to continue the adaptive optimization expansion of the random tree, if no, connecting the new node generated in S2 and the target point directly, and searching is completed and an initial path is generated; S4, removing redundant points in the initial path to obtain a planned path.

2. The six-axis industrial robot path planning method based on improved RRT algorithm according to claim 1, characterized in that, The construction of the angle constraint area by using the angle constraint strategy is specifically: The starting point is connected with the parent node and the target point respectively to obtain an angle constraint region.

3. The path planning method of a six-axis industrial robot based on the improved RRT algorithm according to claim 1, characterized in that, The adaptive optimization expansion of the random tree is specifically: The step size is determined by the distance between the node and the obstacle A primary safety distance A secondary safety distance And three different step sizes, the three different step sizes being a large step size A normal step size And a small step size ; When the distance between the node and the obstacle Greater than Level 2 safety distance At that time, a large step size was adopted. ; When the distance between the node and the obstacle Between Level 1 and 2 Level 2 safety distance At that time, use normal step size ; When the distance between the node and the obstacle is less than the primary safety distance a small step size is used.

4. The six-axis industrial robot path planning method based on improved RRT algorithm according to claim 1, characterized in that, The removal of the redundant points in the initial path is specifically: Traversing the nodes in the initial path, setting the node numbers of the nodes in the initial path from the starting point to the target point as 1 to n, starting from the first path node, sequentially connecting the subsequent nodes in the path, if there is no collision with the obstacle when connecting the 1st node and the 2nd node, trying to connect the next node, until the connection with the mth node encounters the obstacle and collision, at this time, connecting the 1st node and the m-1th node directly, and taking the m-1th node as a new starting point, repeating the above process, and iterating until the target point is reached; wherein, 1 5. The method of claim 1, wherein, After removing the redundant points in the initial path, the method further comprises: fitting and processing by using a cubic B-spline curve.

6. An apparatus comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein, The processor executes the computer program to implement the six-axis industrial robot path planning method based on the improved RRT algorithm according to any one of claims 1 to 5.

7. A computer-readable storage medium storing a computer program, wherein the computer program comprises the following steps of: receiving a request for a resource from a client; determining whether the client is authorized to access the resource; and if the client is authorized to access the resource, providing the resource to the client. The computer program is executed by the processor to implement the six-axis industrial robot path planning method based on the improved RRT algorithm according to any one of claims 1 to 5.

8. A computer program product, characterised in that, The computer program product is executed by the processor to implement the six-axis industrial robot path planning method based on the improved RRT algorithm according to any one of claims 1 to 5.

Images