A Dynamic Path Planning Method, Device, Storage Medium and Electronic Device

Abstract

The present invention provides a dynamic path planning method, apparatus, storage medium and electronic device. It performs upsampling path planning and path planning in stages according to the path point feasibility strategy or the entire path reachability strategy, and iteratively updates the real-time search step size, and finally obtains a target path that the entire path can reach. By adopting the upsampling strategy, the algorithm no longer requires manual debugging and setting of the optimal step size, solves the long-existing step size selection problem in such path planning methods based on random sampling, and realizes the improvement of the efficiency of path planning and post-processing to meet the real-time requirements of serial robots for intelligent autonomous obstacle avoidance in a dynamic environment. At the same time, by deleting redundant path points, path dynamic simplification can gradually shorten the path length.

Description

Technical Field

[0001] The present invention relates to the field of robot motion planning, and in particular, to a dynamic path planning method, device, storage medium, and electronic device. Background Technique

[0002] Serial robots have been widely used in many fields such as manufacturing, logistics, and environmental monitoring due to their flexibility and high precision. As one of the key technologies for serial robots to achieve autonomous navigation and efficient operation, dynamic path planning technology has received extensive attention and research in recent years. The dynamic path planning technology aims to calculate and optimize an optimal or sub-optimal path in real time according to the current position of the robot, the target position, and the obstacle information in the environment. This technology not only requires the path planning algorithm to be efficient and accurate, but also needs to be able to cope with the dynamic changes in the environment, such as the movement of obstacle positions and the emergence of new obstacles. With the continuous progress of artificial intelligence and machine learning technologies, the dynamic path planning technology of serial robots is also constantly developing, from traditional graph search-based algorithms such as the A* algorithm and the Dijkstra algorithm to the intelligent path planning methods based on reinforcement learning and deep learning that have emerged in recent years. The introduction of these technologies has greatly improved the dynamic path planning ability of serial robots in complex environments.

[0003] Although serial robots have made significant progress in dynamic path planning, there are still many technical challenges in real-time obstacle avoidance planning. Therefore, developing a real-time obstacle avoidance planning algorithm that can not only efficiently handle dynamic environments but also fully consider the physical characteristics of the robot is of great significance for improving the autonomous navigation and operation capabilities of serial robots. Summary of the Invention

[0004] The purpose of the present invention is to provide a dynamic path planning method, device, storage medium, and electronic device to improve the above problems.

[0005] To achieve the above purpose, the technical solutions adopted in the embodiments of the present invention are as follows:

[0006] In a first aspect, an embodiment of the present invention provides a dynamic path planning method, and the method includes:

[0007] Perform first-class upsampling path planning according to the path point feasibility strategy, the real-time search step, and the basic path to obtain a first-class upsampling path;

[0008] Perform dynamic path simplification on the first-class upsampling path, delete the redundant points therein, and use the simplified path as the new basic path;

[0009] Perform shortening update on the basis of the current real-time search step to obtain a new real-time search step;

[0010] If the new real-time search step size is greater than or equal to the step size threshold, then repeat the first type of up-sampling path planning according to the path point feasibility strategy, based on the real-time search step size and the base path, to obtain the first type of up-sampling path;

[0011] If the new real-time search step size is less than the step size threshold, according to the whole path reachability strategy, perform the second type of up-sampling path planning based on the new real-time search step size and the base path, to obtain the second type of up-sampling path;

[0012] Perform dynamic path simplification on the second type of up-sampling path, delete the redundant points therein, to obtain the target path that the whole path can reach.

[0013] In a second aspect, an embodiment of the present invention provides a dynamic path planning device, and the device includes:

[0014] A first processing unit, configured to perform the first type of up-sampling path planning according to the path point feasibility strategy, based on the real-time search step size and the base path, to obtain the first type of up-sampling path;

[0015] A second processing unit, configured to perform dynamic path simplification on the first type of up-sampling path, delete the redundant points therein, and use the simplified path as the new base path;

[0016] The first processing unit is further configured to perform shortening update on the basis of the current real-time search step size, to obtain a new real-time search step size;

[0017] The first processing unit is further configured to, if the new real-time search step size is greater than or equal to the step size threshold, then repeat the first type of up-sampling path planning according to the path point feasibility strategy, based on the real-time search step size and the base path, to obtain the first type of up-sampling path;

[0018] The first processing unit is further configured to, if the new real-time search step size is less than the step size threshold, according to the whole path reachability strategy, perform the second type of up-sampling path planning based on the new real-time search step size and the base path, to obtain the second type of up-sampling path;

[0019] The second processing unit is further configured to perform dynamic path simplification on the second type of up-sampling path, delete the redundant points therein, to obtain the target path that the whole path can reach.

[0020] In a third aspect, an embodiment of the present invention provides a storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the above method is implemented.

[0021] Fourthly, an embodiment of the present invention provides an electronic device, which includes: a processor and a memory, and the memory is used to store one or more programs; when the one or more programs are executed by the processor, the above-mentioned method is implemented.

[0022] Compared with the prior art, a dynamic path planning method, device, storage medium and electronic device provided by an embodiment of the present invention perform a first type of upsampling path planning according to a path point feasibility strategy, a real-time search step length and a basic path to obtain a first type of upsampling path; perform dynamic path simplification on the first type of upsampling path, delete redundant points therein, and use the simplified path as a new basic path; perform shortening update on the basis of the current real-time search step length to obtain a new real-time search step length; if the new real-time search step length is greater than or equal to the step length threshold, repeat performing a first type of upsampling path planning according to the path point feasibility strategy, the real-time search step length and the basic path to obtain a first type of upsampling path; if the new real-time search step length is less than the step length threshold, perform a second type of upsampling path planning according to the whole path reachability strategy, the new real-time search step length and the basic path to obtain a second type of upsampling path; perform dynamic path simplification on the second type of upsampling path, delete redundant points therein, to obtain a target path that is reachable for the whole path. By adopting the upsampling strategy, the algorithm no longer needs to be manually debugged and set the optimal step length, solves the long-existing step length selection problem in such path planning methods based on random sampling, and realizes the improvement of the efficiency of path planning and post-processing to meet the real-time requirements of a serial robot for intelligent autonomous obstacle avoidance in a dynamic environment. At the same time, by deleting redundant path points, path dynamic simplification can gradually shorten the path length.

[0023] To make the above objects, features and advantages of the present invention more obvious and understandable, the following specific preferred embodiments are given and described in detail in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required to be used in the embodiments. It should be understood that the following drawings only show some embodiments of the present invention, and therefore should not be regarded as limiting the scope. For those of ordinary skill in the art, without creative efforts, other related drawings can be obtained based on these drawings.

[0025] Figure 1 It is a schematic structural diagram of the electronic device provided by the embodiment of the present invention.

[0026] Figure 2 It is one of the flowcharts of the dynamic path planning method provided by the embodiment of the present invention.

[0027] Figure 3Iterative schematic diagram of upsampling path planning provided by an embodiment of the present invention.

[0028] Figure 4 Schematic diagram of dynamic path simplification provided by an embodiment of the present invention.

[0029] Figure 5 Second flowchart schematic diagram of the dynamic path planning method provided by an embodiment of the present invention.

[0030] Figure 6 Unit schematic diagram of the dynamic path planning device provided by an embodiment of the present invention.

[0031] In the figure: 10 - processor; 11 - memory; 12 - bus; 13 - communication interface; 501 - first processing unit; 502 - second processing unit. Detailed implementation manners

[0032] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some but not all of the embodiments of the present invention. Components of the embodiments of the present invention generally described and illustrated in the figures herein can be arranged and designed in a variety of different configurations.

[0033] Therefore, the detailed description of the embodiments of the present invention provided in the drawings below is not intended to limit the scope of the claimed present invention, but merely represents selected embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0034] It should be noted that: like reference numerals and letters denote like items in the following figures, and thus, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. At the same time, in the description of the present invention, terms such as "first", "second", etc. are only used for descriptive distinction and cannot be construed as indicating or implying relative importance.

[0035] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, such that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the phrase "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or device comprising the element.

[0036] In the description of the present invention, it should be noted that the orientation or positional relationship indicated by the terms "upper", "lower", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings, or the orientation or positional relationship in which the inventive product is customarily placed during use. It is only for the convenience of describing the present invention and simplifying the description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as a limitation of the present invention.

[0037] In the description of the present invention, it should also be noted that unless otherwise clearly specified and limited, the terms "set" and "connect" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0038] The following will describe in detail some embodiments of the present invention with reference to the drawings. Without conflict, the following embodiments and the features in the embodiments can be combined with each other.

[0039] When a serial robot performs a dynamic obstacle avoidance task, it often needs to consider the position change of the obstacle. How to effectively avoid the obstacle while ensuring real-time performance is an important challenge. Secondly, real-time obstacle avoidance planning also needs to consider the performance of the planning result. If there are too many redundant points, it will significantly affect the movement efficiency of the robot. These challenges require that the real-time obstacle avoidance planning algorithm of the serial robot not only has a high degree of intelligence and adaptability, but also needs to be able to efficiently handle the obstacle avoidance planning problem and make quick and accurate decisions.

[0040] An embodiment of the present invention provides a dynamic path planning method, which adopts an upsampling strategy to enable the algorithm to no longer require manual debugging and setting of the optimal step size, solves the long-existing step size selection problem in such path planning methods based on random sampling, and improves the efficiency of path planning and post-processing to meet the real-time requirements of a serial robot for intelligent autonomous obstacle avoidance in a dynamic environment.

[0041] An embodiment of the present invention provides an electronic device, which can be a mobile phone device, a computer device, or a server device. The electronic device can maintain a communication connection with the robot, or the electronic device is the central control device of the robot. Please refer to Figure 1 , the structural schematic diagram of the electronic device. The electronic device includes a processor 10, a memory 11, and a bus 12. The processor 10 and the memory 11 are connected through the bus 12, and the processor 10 is used to execute an executable module stored in the memory 11, such as a computer program.

[0042] The processor 10 can be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the dynamic path planning method can be completed by the integrated logic circuit in the hardware of the processor 10 or instructions in software form. The above-mentioned processor 10 can be a general-purpose processor, including a central processing unit (CPU for short) and a network processor (NP for short); it can also be a digital signal processor (DSP for short), an application specific integrated circuit (ASIC for short), a field-programmable gate array (FPGA for short), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0043] The memory 11 may include a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk memory.

[0044] The bus 12 can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. Figure 1Although only one bidirectional arrow is used in the figure, it does not mean that there is only one bus 12 or only one type of bus 12 .

[0045] The memory 11 is used to store programs, such as programs corresponding to the dynamic path planning device. The dynamic path planning device includes at least one software function module that can be stored in the memory 11 in the form of software or firmware or fixed in the operating system (OS) of the electronic device. After receiving the execution instruction, the processor 10 executes the program to implement the dynamic path planning method.

[0046] Possibly, the electronic device provided by the embodiment of the present invention further includes a communication interface 13. The communication interface 13 is connected to the processor 10 via a bus.

[0047] It should be understood that Figure 1 The structure shown is only a schematic diagram of a portion of the electronic device. The electronic device may also include Figure 1 More or fewer components as shown, or with Figure 1 Different configurations are shown. Figure 1 Each component shown in the figure can be implemented by hardware, software or a combination thereof.

[0048] A dynamic path planning method provided by an embodiment of the present invention can be applied to, but not limited to, Figure 1 For detailed procedures, please refer to the electronic equipment shown in Figure 2 , the dynamic path planning method includes: S21, S22, S23, S24, S25 and S26, which are specifically described as follows.

[0049] S21 , according to the path point feasibility strategy, the first type of upsampling path planning is performed according to the real-time search step size and the basic path to obtain the first type of upsampling path.

[0050] It should be noted that, when there are obstacles on the linear path from the starting point to the end point in the joint space, the linear path from the starting point to the end point cannot be directly used. In this case, S21 can be executed.

[0051] When performing the first type of upsampling path planning for the first time, the real-time search step size may be a preset step size, or may be determined according to the linear path length from the start point to the end point in the joint space and the search step size scaling factor.

[0052] When the first type of upsampling path planning is performed for the first time, the basic path is empty, that is, there are no constraints on the basic path.

[0053] The path point feasibility strategy is used to ensure that there are no obstacles within the preset range of each path node on the first type of up-sampling path.

[0054] S22. Dynamically simplify the first type of upsampling path, delete the redundant points therein, and use the simplified path as the new base path.

[0055] Understandably, a redundant point refers to a node on the path that takes a detour.

[0056] S23. Perform a shortening update based on the current real-time search step size to obtain a new real-time search step size.

[0057] Among them, the new real-time search step size is smaller than the original real-time search step size.

[0058] Optionally, iteratively update the real-time search step size according to the search step size scaling factor to obtain a new real-time search step size.

[0059] In an optional implementation manner, the formula for the new real-time search step size is:

[0060] S s1 = S s0 / f s

[0061] Among them, S s1 represents the new real-time search step size, S s0 represents the current real-time search step size, and f s represents the search step size scaling factor.

[0062] S24. Determine whether the new real-time search step size is smaller than the step size threshold. If the new real-time search step size is greater than or equal to the step size threshold, repeat S21, and according to the path point feasibility strategy, plan the first type of upsampling path based on the real-time search step size and the base path to obtain the first type of upsampling path; if the new real-time search step size is smaller than the step size threshold, execute S25.

[0063] S25. According to the whole path reachability strategy, plan the second type of upsampling path based on the new real-time search step size and the base path to obtain the second type of upsampling path.

[0064] Among them, the whole path reachability strategy is used to ensure that there are no obstacles within the preset range of each path node on the second type of upsampling path, and there are no obstacles within the mechanical arm motion sweeping space corresponding to any two adjacent path nodes.

[0065] S26. Dynamically simplify the second type of upsampling path, delete the redundant points therein, to obtain the target path that is reachable for the whole path.

[0066] Among them, there are no obstacles within the preset range of each path node on the target path, and there are no obstacles within the swept space of the robotic arm corresponding to any two adjacent path nodes. The target path is the joint space movement path of the robot. In the embodiments of the present invention, the starting point refers to the starting point in the joint space, and the ending point refers to the ending point in the joint space.

[0067] In the dynamic path planning method provided by the embodiments of the present invention, an upsampling strategy is adopted, enabling the algorithm to no longer require manual debugging and setting of the optimal step size, solving the long-existing step size selection problem in such random sampling-based path planning methods, and achieving improved efficiency in path planning and post-processing to meet the real-time requirements of a serial robot for intelligent autonomous obstacle avoidance in a dynamic environment. At the same time, by deleting redundant path points, path dynamic simplification can gradually shorten the path length.

[0068] In the early and middle stages, upsampling path planning is performed with a large step size to quickly cover the search space, efficiently explore and identify a series of reachable intermediate path points. These intermediate path points act as a bridge between the starting point and the ending point, effectively decomposing the long-distance complex planning problem into a series of simpler sub-problems. In the early and middle stages, continuous collision detection will be skipped, and only the feasibility of the path points (whether there are obstacles within the preset range of the path nodes) will be judged, significantly reducing the computational complexity in the early and middle stages, and thus more quickly searching for reachable path regions. When the step size decreases below the preset step size threshold, a final round of upsampling is performed according to the whole-path reachability strategy. During this round of search, continuous collision detection is required to ensure the reachability of the whole path, ensuring that there are no obstacles within the preset range of each path node on the second type of upsampling path, and there are no obstacles within the swept space of the robotic arm corresponding to any two adjacent path nodes.

[0069] During the planning process, path dynamic simplification can dynamically simplify the path after each round of upsampling planning. By deleting redundant path points, path dynamic simplification can gradually shorten the path length.

[0070] In an optional implementation manner, only distance factors are considered for dynamic path simplification of the first type of upsampling path, and both distance factors and collision factors need to be considered for dynamic path simplification of the second type of upsampling path to reduce the computational amount of continuous collision detection.

[0071] In Figure 2Based on the above, regarding the content in S21, the embodiments of the present invention further provide an optional implementation manner. Please refer to the following text. S21, according to the path point feasibility strategy, perform the first type of upsampling path planning based on the real-time search step size and the basic path to obtain the first type of upsampling path, including: S211, S212, S213, S214, S215, and S216, which are specifically described as follows.

[0072] S211, according to the real-time search step size and the basic path, respectively expand the random search tree from the starting point and the ending point using the bidirectional connected rapid search random tree algorithm for the first growth.

[0073] Among them, the bidirectional connected rapid search random tree algorithm is also called the RRT-Connect algorithm.

[0074] S212, after the i-th growth of the random search tree is completed, determine whether there is a matching node combination. If there is a matching node combination, execute S213; if there is no matching node combination, execute S215.

[0075] Among them, the distance between the starting point side node and the ending point side node in the matching node combination is less than or equal to the real-time search step size; the starting point side node is a node on the random search tree on the starting point side, and the ending point side node is a node on the random search tree on the ending point side.

[0076] S213, determine whether there is a current matching path that meets the first type of feasible condition. If there is a current matching path that meets the first type of feasible condition, execute S214; if there is no current matching path that meets the first type of feasible condition, execute S215.

[0077] Among them, the current matching path is the path formed by splicing along the starting point to the starting point side node in the matching node combination and along the ending point to the ending point side node in the matching node combination, and the first type of feasible condition means that there are no obstacles within the preset range of each path node on the current matching path.

[0078] Among them, the preset range is the space range required for the robotic arm to occupy at the path node.

[0079] S214, if there is a current matching path that meets the first type of feasible condition, use the current matching path with the shortest path length as the first type of upsampling path.

[0080] The current matching path with the shortest path length is the current matching path with the fewest path nodes.

[0081] S215, let i = i + 1, and determine whether i is greater than the growth times threshold. If i is less than or equal to the growth times threshold, execute S216; if i is greater than the growth times threshold, determine that the path planning fails.

[0082] S216, perform the i-th growth.

[0083] After S216, repeatedly execute S212. After the i-th growth of the random search tree is completed, determine whether there is a matching node combination.

[0084] At Figure 2 Based on this, regarding the content in S22, the embodiments of the present invention also provide an alternative implementation manner. Please refer to the following. S22, perform dynamic path simplification on the first type of upsampling path, delete the redundant points therein, and use the simplified path as the new base path, including: S22, S222, S223, S224, S225, and S226, which are specifically described as follows.

[0085] S221, use the starting point of the first type of upsampling path as the recognition point.

[0086] S222, determine whether the current recognition point meets the first type of simplification condition. If the current recognition point meets the first type of simplification condition, execute S223; if the current recognition point does not meet the first type of simplification condition, execute S224.

[0087] Among them, the first type of simplification condition means that within a preset range with the current recognition point as the center and the real-time search step length as the radius, there is a point to be confirmed. The point to be confirmed is a node on the first type of upsampling path that is sorted after the current recognition point and has a sorting interval greater than 1.

[0088] S223, delete the next path node on the first type of upsampling path that is sorted after the current recognition point and the first M - 1 points to be confirmed corresponding to the current recognition point as redundant points, and use the M-th point to be confirmed after the current recognition point as the new recognition point.

[0089] Among them, M represents the number of points to be confirmed corresponding to the current recognition point.

[0090] It can be understood that the path nodes located after the simplified recognition point and before the last point to be confirmed are deleted as redundant points. The last point to be confirmed is the one with the largest sorting on the first type of upsampling path among the points to be confirmed corresponding to the simplified recognition point.

[0091] S224, use the next node on the first type of upsampling path that is sorted after the current recognition point as the new recognition point.

[0092] S225. After determining the new recognition point, determine whether the new recognition point is the end point of the first type of upsampling path. If it is not the end point, repeat S222 to determine whether the current recognition point meets the first type of simplification condition. If the new recognition point is the end point of the first type of upsampling path, execute S226.

[0093] S226. Use the simplified path after deleting redundant points as the new base path.

[0094] Based on Figure 2 Regarding the content in S25, the embodiments of the present invention also provide an optional implementation manner. Please refer to the following text. S25. According to the entire path reachability strategy, perform the second type of upsampling path planning based on the new real-time search step size and the base path to obtain the second type of upsampling path, including: S251, S252, S253, S254, S255, and S256, which are specifically described as follows.

[0095] S251. According to the real-time search step size and the base path, respectively expand the random search tree from the starting point and the end point using the bidirectional connected fast search random tree algorithm for the first growth.

[0096] S252. After the i-th growth of the random search tree is completed, determine whether there is a matching node combination. If there is a matching node combination, execute S253. If there is no matching node combination, execute S255.

[0097] Among them, the distance between the starting point side node and the end point side node in the matching node combination is less than or equal to the real-time search step size. The starting point side node is a node on the random search tree on the starting point side, and the end point side node is a node on the random search tree on the end point side.

[0098] S253. Determine whether there is a current matching path that meets the second type of feasible condition. If there is a current matching path that meets the second type of feasible condition, execute S254. If there is no current matching path that meets the second type of feasible condition, execute S255.

[0099] Among them, the current matching path is the path formed by splicing along the starting point to the starting point side node in the matching node combination and along the end point to the end point side node in the matching node combination. The second type of feasible condition means that there are no obstacles within the preset range of the path nodes on the current matching path, and there are no obstacles within the mechanical arm movement sweeping space corresponding to any two adjacent path nodes.

[0100] S254. If there is a current matching path that meets the second type of feasible condition, use the current matching path with the shortest path length as the second type of upsampling path.

[0101] The current matching path with the shortest path length is the current matching path with the fewest number of path nodes.

[0102] S255, let i = i + 1, and determine whether i is greater than the growth times threshold. If i is less than or equal to the growth times threshold, execute S256; if i is greater than the growth times threshold, determine that the path planning fails.

[0103] S256, perform the i-th growth.

[0104] After S256, repeatedly execute S252. After the i-th growth of the random search tree is completed, determine whether there is a matching node combination.

[0105] On the basis of Figure 2 Regarding the content in S26, the embodiment of the present invention also provides an optional implementation manner. Please refer to the following. S26, perform dynamic path simplification on the second type of upsampling path, and delete the redundant points therein to obtain the target path reachable by the entire path, including: S261, S262, S263, S264, S265, S266, and S267, which are specifically described as follows.

[0106] S261, use the starting point of the second type of upsampling path as the current recognition point.

[0107] S262, determine whether the current recognition point meets the first type of simplification condition. If the current recognition point meets the first type of simplification condition, execute S263; if the current recognition point does not meet the first type of simplification condition, execute S265.

[0108] Among them, the first type of simplification condition means that there is a point to be confirmed within a preset range with the current recognition point as the center and the real-time search step length as the radius. The point to be confirmed is a node on the first type of upsampling path that is sorted after the current recognition point and has a sorting interval greater than 1.

[0109] S263, determine whether there is a point to be confirmed that meets the second type of simplification condition among the M points to be confirmed. If there is a point to be confirmed that meets the second type of simplification condition, execute S264; if there is no point to be confirmed that meets the second type of simplification condition, execute S265.

[0110] Among them, M represents the number of points to be confirmed corresponding to the current recognition point. The second type of simplification condition means that there is no obstacle in the mechanical arm movement sweeping space corresponding to the current recognition point to the point to be confirmed.

[0111] S264, if there is a point to be confirmed that meets the second type of simplification condition, delete the path nodes on the second type of upsampling path that are sorted after the current recognition point and before the target node as redundant points, and use the target node as the new recognition point.

[0112] Among them, the target node is the last point to be confirmed that meets the second type of simplification condition, that is, the one with the largest sorting on the second type of upsampling path among the points to be confirmed that meet the second type of simplification condition.

[0113] S265. Take the next node on the second type of upsampling path that is sorted after the current recognition point as the new recognition point.

[0114] S266. After determining the new recognition point, judge whether the new recognition point is the end point of the second type of upsampling path. If the new recognition point is the end point of the second type of upsampling path, execute S267; if the new recognition point is not the end point of the second type of upsampling path, repeat S262 to determine whether the current recognition point meets the first type of simplification condition.

[0115] S267. Take the simplified path after deleting redundant points as the target path.

[0116] Please refer to Figure 3 and Figure 4 , Figure 3 which is the iterative schematic diagram of the upsampling path planning provided by the embodiment of the present invention, Figure 4 and is the schematic diagram of the dynamic path simplification provided by the embodiment of the present invention. Optionally, Figure 3 the number of iterations of the upsampling path planning in [the figure] increases sequentially, and the arrangements from top to bottom are: the initial linear path, the first first type of upsampling path, the second first type of upsampling path, the first second type of upsampling path, and it can be seen that the step size gradually decreases. Figure 4 the arrangements from top to bottom in [the figure] are: the first type of upsampling path or the second type of upsampling path, the path simplification process, the path simplification result, where the R s represents the radius and is equal to the current real-time search step size S s .

[0117] The embodiment of the present invention also provides an optional implementation manner. Please refer to Figure 5 , and the dynamic path planning method further includes: S11, S12, S13, S14, S15, S16, and S17, which are specifically described as follows.

[0118] S11. Calculate the pose of the robot end at the starting point according to the inverse kinematics of the robot to obtain the starting joint angle of the robot at the starting point.

[0119] S12. Calculate the pose of the robot end at the end point according to the inverse kinematics of the robot to obtain the end joint angle of the robot at the end point.

[0120] S13. Determine the Euclidean distance in the joint space between the two according to the starting joint angle and the end joint angle, and use it as the linear path length from the starting point to the end point in the joint space.

[0121] S14. Interpolate along the linear direction from the starting point to the ending point in the joint space according to a preset interpolation step size to generate a linear path.

[0122] S15. Determine whether a collision will occur when the robot moves along the linear path. If no collision will occur, execute S16; if a collision will occur, execute S17.

[0123] If a collision will occur, it means there is an obstacle on the linear path from the starting point to the ending point in the joint space.

[0124] S16. Determine the linear path as the target path.

[0125] S17. Determine the real-time search step size according to the length of the linear path from the starting point to the ending point in the joint space and the search step size ratio factor.

[0126] In an alternative embodiment, the formula for the real-time search step size is:

[0127] S s = L0 / f s

[0128] where S s represents the real-time search step size, L0 represents the length of the linear path, and f s represents the search step size ratio factor.

[0129] It should be noted that after S17, steps such as S21 can be executed.

[0130] A dynamic path planning method provided by an embodiment of the present invention can complete real-time obstacle avoidance planning for a serial robot based on upsampling and dynamic path simplification. First, based on the bidirectional connected rapidly-exploring random tree path planning framework, a targeted upsampling strategy is proposed, and an adaptive step size adjustment mechanism and a lightweight continuous collision detection mechanism are used to ensure efficient and safe path planning; second, for the problem of redundant path points caused by random search, a dynamic path simplification method based on real-time search step size is carried out to eliminate redundant path points, shorten the path length, and reduce the computational load at the same time. By adopting the upsampling strategy, the algorithm no longer requires manual debugging and setting of the optimal step size, solves the long-existing step size selection problem in such path planning methods based on random sampling, and realizes the improvement of the efficiency of path planning and post-processing to meet the real-time requirements of a serial robot for intelligent autonomous obstacle avoidance in a dynamic environment.

[0131] Please refer to Figure 6 , Figure 6 , which is a dynamic path planning device provided by an embodiment of the present invention. Optionally, this dynamic path planning device is applied to the electronic device described above.

[0132] The dynamic path planning device includes: a first processing unit 501 and a second processing unit 502.

[0133] The first processing unit 501 is configured to perform first - type up - sampling path planning according to the path - point feasibility strategy, based on the real - time search step and the base path, to obtain a first - type up - sampling path.

[0134] The second processing unit 502 is configured to perform dynamic path simplification on the first - type up - sampling path, delete the redundant points therein, and use the simplified path as a new base path.

[0135] The first processing unit 501 is further configured to perform shortening update based on the current real - time search step to obtain a new real - time search step.

[0136] The first processing unit 501 is further configured to, if the new real - time search step is greater than or equal to the step threshold, repeat performing first - type up - sampling path planning according to the path - point feasibility strategy, based on the real - time search step and the base path, to obtain a first - type up - sampling path.

[0137] The first processing unit 501 is further configured to, if the new real - time search step is less than the step threshold, perform second - type up - sampling path planning according to the whole - path reachability strategy, based on the new real - time search step and the base path, to obtain a second - type up - sampling path.

[0138] The second processing unit 502 is further configured to perform dynamic path simplification on the second - type up - sampling path, delete the redundant points therein, to obtain a target path that is reachable for the whole path.

[0139] Optionally, the second processing unit 502 may execute S22 and S26 above, and the first processing unit 501 may execute the other steps in the above - mentioned method embodiments.

[0140] It should be noted that the dynamic path planning device provided in this embodiment can execute the method flow shown in the above - mentioned method flow embodiment to achieve the corresponding technical effects. For the sake of brief description, for the parts not mentioned in this embodiment, reference can be made to the corresponding content in the above - mentioned embodiments.

[0141] An embodiment of the present invention further provides a storage medium, which stores computer instructions and programs. When the computer instructions and programs are read and run, they execute the dynamic path planning method in the above - mentioned embodiment. The storage medium may include memory, flash memory, registers, or a combination thereof, etc.

[0142] The following provides an electronic device, which can be a mobile phone device, a computer device, and a server device. The electronic device, such as Figure 1As shown, the above dynamic path planning method can be implemented. Specifically, the electronic device includes: a processor 10, a memory 11, and a bus 12. The processor 10 can be a CPU. The memory 11 is used to store one or more programs. When the one or more programs are executed by the processor 10, the dynamic path planning method of the above embodiment is executed.

[0143] In summary, a dynamic path planning method, device, storage medium, and electronic device provided by an embodiment of the present invention perform first-class up-sampling path planning according to a path point feasibility strategy, a real-time search step size, and a basic path to obtain a first-class up-sampled path; perform dynamic path simplification on the first-class up-sampled path, delete redundant points therein, and use the simplified path as a new basic path; perform shortening update on the basis of the current real-time search step size to obtain a new real-time search step size; if the new real-time search step size is greater than or equal to the step size threshold, repeat performing first-class up-sampling path planning according to the path point feasibility strategy, the real-time search step size, and the basic path to obtain a first-class up-sampled path; if the new real-time search step size is less than the step size threshold, perform second-class up-sampling path planning according to the whole path reachability strategy, the new real-time search step size, and the basic path to obtain a second-class up-sampled path; perform dynamic path simplification on the second-class up-sampled path, delete redundant points therein, to obtain a target path where the whole path is reachable. By adopting the up-sampling strategy, the algorithm no longer requires manual debugging and setting of the optimal step size, solves the long-existing step size selection problem in such path planning methods based on random sampling, and improves the efficiency of path planning and post-processing to meet the real-time requirements of a serial robot for intelligent autonomous obstacle avoidance in a dynamic environment. At the same time, by deleting redundant path points, path dynamic simplification can gradually shorten the path length.

[0144] The foregoing is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

[0145] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and can be implemented in other specific forms without departing from the spirit or basic characteristics of the present invention. Therefore, in any aspect, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, it is intended to embrace all changes within the meaning and scope of the equivalent elements of the claims in the present invention. Any reference numerals in the claims should not be regarded as limiting the claimed rights.

Claims

1. A dynamic path planning method, characterized in that, The method includes: According to the path point feasibility strategy, perform the first type of upsampling path planning based on the real-time search step size and the basic path to obtain the first type of upsampling path; Perform dynamic path simplification on the first type of upsampling path, delete the redundant points therein, and use the simplified path as the new basic path; Perform shortening update based on the current real-time search step size to obtain the new real-time search step size; If the new real-time search step size is greater than or equal to the step size threshold, repeat performing the first type of upsampling path planning according to the path point feasibility strategy, based on the real-time search step size and the basic path, to obtain the first type of upsampling path; If the new real-time search step size is less than the step size threshold, perform the second type of upsampling path planning according to the whole path reachability strategy, based on the new real-time search step size and the basic path, to obtain the second type of upsampling path; Perform dynamic path simplification on the second type of upsampling path, delete the redundant points therein, to obtain the target path where the whole path is reachable.

2. The dynamic path planning method according to claim 1, wherein The performing the first type of upsampling path planning according to the path point feasibility strategy, based on the real-time search step size and the basic path, to obtain the first type of upsampling path includes: According to the real-time search step size and the basic path, respectively expand the random search tree from the starting point and the ending point using the bidirectional connected fast search random tree algorithm for the first growth; After the i-th growth of the random search tree is completed, determine whether there is a matching node combination; Among them, the distance between the starting point side node and the ending point side node in the matching node combination is less than or equal to the real-time search step size; If there is a matching node combination, determine whether there is a current matching path that meets the first type of feasible condition; Among them, the current matching path is the path spliced along the starting point to the starting point side node in the matching node combination and along the ending point to the ending point side node in the matching node combination, and the first type of feasible condition means that there are no obstacles within the preset range of each path node on the current matching path; If there is a current matching path that meets the first type of feasible condition, use the current matching path with the shortest path length as the first type of upsampling path; If there is no matching node combination, or there is no current matching path that meets the first type of feasible condition, let i = i + 1, determine whether i is greater than the growth times threshold, if i is less than or equal to the growth times threshold, perform the i-th growth, and repeat determining whether there is a matching node combination after the i-th growth of the random search tree is completed.

3. The dynamic path planning method according to claim 1, wherein, The performing dynamic path simplification on the first type of upsampling path, deleting the redundant points therein, and using the simplified path as the new basic path includes: Use the starting point of the first type of upsampling path as the identification point; Determine whether the current identification point meets the first type of simplification condition; Among them, the first type of simplification condition means that there is a point to be confirmed within the preset range with the current identification point as the center and the real-time search step size as the radius, and the point to be confirmed is a node on the first type of upsampling path that is sorted after the current identification point and the sorting interval is greater than 1; If the current recognition point meets the first type of simplification condition, then the next path node on the first type of upsampling path that is sorted after the current recognition point and the first M-1 to-be-confirmed points corresponding to the current recognition point are taken as redundant points and deleted, and the Mth to-be-confirmed point after the current recognition point is taken as the new recognition point, where M represents the number of to-be-confirmed points corresponding to the current recognition point; If the current recognition point does not meet the first type of simplification condition, the next node on the first type of upsampling path that is sorted after the current recognition point is taken as the new recognition point; After determining the new recognition point, it is judged whether the new recognition point is the end point of the first type of upsampling path; If it is not the end point, then it is repeated to determine whether the current recognition point meets the first type of simplification condition; If the new recognition point is the end point of the first type of upsampling path, then the simplified path after deleting the redundant points is taken as the new base path.

4. The dynamic path planning method according to claim 1, wherein, The second type of upsampling path planning is carried out according to the whole path reachability strategy, based on the new real-time search step size and the base path, to obtain the second type of upsampling path, including: According to the real-time search step size and the base path, a random search tree is expanded from the starting point and the end point respectively by using the bidirectional connected fast search random tree algorithm for the first growth; After the ith growth of the random search tree is completed, it is determined whether there is a matching node combination; Among them, the distance between the starting point side node and the end point side node in the matching node combination is less than or equal to the real-time search step size; If there is a matching node combination, it is determined whether there is a current matching path that meets the second type of feasible condition; Among them, the current matching path is the path spliced along the starting point to the starting point side node in the matching node combination and along the end point to the end point side node in the matching node combination, and the second type of feasible condition means that there are no obstacles within the preset range of the path nodes on the current matching path, and there are no obstacles within the mechanical arm movement sweeping space corresponding to any two adjacent path nodes; If there is a current matching path that meets the second type of feasible condition, the current matching path with the shortest path length is taken as the second type of upsampling path; If there is no matching node combination, or there is no current matching path that meets the second type of feasible condition, then let i = i + 1, and it is determined whether i is greater than the growth times threshold. If i is less than or equal to the growth times threshold, then the ith growth is carried out, and it is repeated to determine whether there is a matching node combination after the ith growth of the random search tree is completed.

5. The dynamic path planning method according to claim 1, wherein The dynamic path simplification of the second type of upsampling path is carried out to delete the redundant points therein to obtain the target path that can reach the whole path, including: Taking the starting point of the second type of upsampling path as the current recognition point; Determining whether the current recognition point meets the first type of simplification condition; Among them, the first type of simplification condition means that there are to-be-confirmed points within the preset range with the current recognition point as the center and the real-time search step size as the radius. The to-be-confirmed points are the nodes on the first type of upsampling path that are sorted after the current recognition point and the sorting interval is greater than 1; If the current recognition point satisfies the first type of simplification condition, it is determined whether there is a pending confirmation point among the M pending confirmation points that satisfies the second type of simplification condition, where M represents the number of pending confirmation points corresponding to the current recognition point, and the second type of simplification condition means that there are no obstacles in the robotic arm motion sweeping space corresponding to the current recognition point to the pending confirmation point; If there is a pending confirmation point that satisfies the second type of simplification condition, the path nodes on the second type of upsampling path that are sorted after the current recognition point and before the target node are used as redundant points and deleted, and the target node is used as the new recognition point; where the target node is the last pending confirmation point that satisfies the second type of simplification condition; If the current recognition point does not satisfy the first type of simplification condition, or there is no pending confirmation point that satisfies the second type of simplification condition, the next node on the second type of upsampling path that is sorted after the current recognition point is used as the new recognition point; After determining the new recognition point, it is determined whether the new recognition point is the end point of the second type of upsampling path; If it is not the end point, it is repeated to determine whether the current recognition point satisfies the first type of simplification condition; If the new recognition point is the end point of the second type of upsampling path, the simplified path after deleting the redundant points is used as the target path.

6. The dynamic path planning method according to claim 1, wherein The method further includes: Solving the pose of the robot end at the starting point according to the inverse kinematics of the robot to obtain the starting joint angle of the robot at the starting point; Solving the pose of the robot end at the end point according to the inverse kinematics of the robot to obtain the end joint angle of the robot at the end point; According to the starting joint angle and the end joint angle, determining the Euclidean distance between the two in the joint space as the linear path length from the starting point to the end point in the joint space; Performing linear interpolation along the linear direction from the starting point to the end point in the joint space according to the preset interpolation step size to generate a linear path; Determining whether a collision will occur when the robot moves along the linear path; If no collision will occur, the linear path is determined as the target path; If a collision will occur, the real-time search step size is determined according to the linear path length from the starting point to the end point in the joint space and the search step size proportionality factor.

7. The dynamic path planning method according to claim 1, wherein The shortening update based on the current real-time search step size to obtain a new real-time search step size includes: Iteratively updating the real-time search step size according to the search step size proportionality factor to obtain a new real-time search step size.

8. A dynamic path planning device, characterized in that, The device includes: A first processing unit, configured to perform first type of upsampling path planning according to the path point feasibility strategy, the real-time search step size and the basic path to obtain a first type of upsampling path; A second processing unit, configured to perform dynamic path simplification on the first type of upsampling path, delete the redundant points therein, and use the simplified path as a new basic path; The first processing unit is further configured to perform shortening update based on the current real-time search step size to obtain a new real-time search step size; The first processing unit is further configured to, if the new real-time search step size is greater than or equal to the step size threshold, repeatedly perform the first type of up-sampling path planning according to the path point feasibility strategy, based on the real-time search step size and the basic path, to obtain the first type of up-sampling path; The first processing unit is further configured to, if the new real-time search step size is less than the step size threshold, perform the second type of up-sampling path planning according to the whole path reachability strategy, based on the new real-time search step size and the basic path, to obtain the second type of up-sampling path; The second processing unit is further configured to perform dynamic path simplification on the second type of up-sampling path, delete the redundant points therein, to obtain the target path where the whole path is reachable.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method according to any one of claims 1-7.

10. An electronic device, characterized in that, Comprising: A processor and a memory, the memory is used to store one or more programs; When the one or more programs are executed by the processor, the method according to any one of claims 1-7 is implemented.

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