Path planning method, device, equipment and storage medium based on RRT_star algorithm

Abstract

The present invention relates to the field of path planning, and specifically to a path planning method, device, equipment and storage medium based on the RRT_star algorithm. The present invention uses an optimized RRT tree to carry out path exploration and planning, and performs the process simultaneously from the starting and ending task points. With the help of powerful computer computing resources, the path generation speed is accelerated and the planning efficiency is improved. Then, the original path depth is smoothed to solve the problem of excessive depth fluctuations at intermediate path points, making the path smoother. For application scenarios such as autonomous underwater robots that are sensitive to depth, the impact of depth changes on the performance and safety of their internal instruments can be reduced. The smoothed path is then iteratively optimized to further improve the path quality and adaptability. Finally, the navigation status is obtained based on the iteratively optimized path, and adjustments are made accordingly to obtain the final navigation optimization path, so that the path planning is more in line with the actual navigation conditions, the feasibility and safety of the path are enhanced, and the overall path planning is better from multiple aspects.

Description

Technical Field

[0001] The present invention relates to the field of path planning, and in particular to a path planning method, device, equipment and storage medium based on an RRT_star algorithm. Background Art

[0002] In recent years, with the development of science and technology, the demand for autonomous underwater robots to operate in the ocean, especially in the open ocean environment, has been increasing. In the field of path planning for autonomous underwater robots, traditional planning methods have several technical bottlenecks. First, RRT (Rapidly-exploring Random Although the path tree generated by the RRT algorithm can quickly explore the environment during path planning, its tree structure extends too evenly, resulting in a large amount of invalid calculations and reducing path planning efficiency. In addition, existing path planning based on the RRT algorithm often plans point by point from the starting point to the end point. This linear programming method fails to fully utilize the powerful computing resources of modern computers, resulting in low efficiency. Secondly, for the planned path, the random expansion characteristic of the RRT tree itself leads to excessive fluctuations in the intermediate depth of the planned path. In fact, most of the time, underwater robots do not need to frequently change the navigation depth during their maritime operations. Frequent navigation depth changes may affect the performance and safety of the internal instruments of the autonomous underwater robot, which is particularly important in deepwater operations. Furthermore, current technologies fail to fully utilize the idle computing power resources of the computer during the navigation of the autonomous underwater robot, resulting in resource waste. At the same time, the branch information in the RRT algorithm path tree, except for the final path, is not effectively utilized. This information actually contains rich environmental characteristics that can assist in adjusting the path. In view of this, developing a new path planning method to solve the above problems and improve the operating efficiency and safety of autonomous underwater robots has important practical significance and broad application prospects. Summary of the Invention

[0003] In order to solve the above-mentioned shortcomings in the prior art, the present invention proposes a path planning method, device, equipment and storage medium based on the RRT_star algorithm.

[0004] In order to solve the above technical problems, the technical solutions adopted by the present invention are as follows:

[0005] A path planning method based on the RRT_star algorithm includes: using the RRT_star algorithm to create original RRT trees at two adjacent task points on a preset map; optimizing the original RRT tree to obtain an optimized RRT tree; performing bidirectional path planning based on the optimized RRT tree to obtain an original path between the two task points; performing a deep smoothing operation on the original path to obtain a smoothed path; performing an iterative optimization operation on the smoothed path to obtain an iteratively optimized path; obtaining a navigation status based on the iteratively optimized path, and adjusting the iteratively optimized path based on the navigation status to obtain a navigation optimized path. By using an optimized RRT tree, path exploration and planning are performed simultaneously from the starting and ending task points, making full use of the computer's powerful computing resources, further accelerating the path generation speed and improving the efficiency of path planning; the original path is depth-smoothed to obtain a smoothed path, which can effectively solve problems such as excessive depth fluctuations at the intermediate points of the planned path, making the path smoother. This is especially true for application scenarios that are sensitive to depth changes, such as autonomous underwater robots, and can reduce the impact of frequent depth changes on the performance and safety of the autonomous underwater robot's internal instruments; subsequently, the smoothed path is iteratively optimized to obtain an iteratively optimized path. This step further improves the quality and adaptability of the path; finally, the navigation status is obtained based on the iteratively optimized path, and the iteratively optimized path is adjusted according to the navigation status to obtain the final navigation optimized path. This adjustment makes the path planning more in line with actual navigation conditions and improves the feasibility and safety of the path.

[0006] Furthermore, the optimization of the original RRT tree to obtain an optimized RRT tree includes: optimizing the step size of the original RRT tree to obtain an optimized primary RRT tree; and optimizing the node addition method of the primary RRT tree to obtain the optimized RRT tree. By optimizing the step size of the RRT tree, ineffective exploration caused by an unreasonable step size can be avoided. The optimized step size can better adapt to different environments and improve the efficiency of path planning. At the same time, the optimized primary RRT tree provides a better foundation for subsequent further optimization. Optimizing the node addition method of the primary RRT tree can make the tree structure more reasonable, make the tree growth more targeted, further improve path planning efficiency, and generate an optimized RRT tree that better meets actual needs.

[0007] Furthermore, the step length of the original RRT tree is optimized to obtain an optimized primary RRT tree, including: randomly obtaining a point on the map to obtain a first random point; judging whether there is an obstacle at the first random point according to the map; when there is no obstacle at the first random point, obtaining the direction of the first random point according to the map to obtain a first direction; obtaining the node closest to the first random point from the original RRT tree to obtain a node P1; obtaining its step length from the original RRT tree to obtain a first step length; taking node P1 as the starting point, obtaining a point on the map according to the first step length and the first direction to obtain a second random point; judging whether there is an obstacle between node P1 and the second random point according to the map; if there is no obstacle, returning to executing randomly obtaining a point on the map until a preset first iteration condition is met, then doubling the first step length to obtain the optimized primary RRT tree; if there is an obstacle, returning to executing randomly obtaining a point on the map until a preset second iteration condition is met, then halving the first step length to obtain the optimized primary RRT tree.

[0008] First, a random point is generated on the map to obtain the first random point. Next, the node closest to the first random point is obtained from the original RRT tree to obtain node P1, providing a good starting point for subsequent step-size optimization. The obstacle situation between node P1 and the first random point is determined. If there are no obstacles, the operation is repeated until the preset first iteration conditions are met, after which the step-size is doubled. This allows for rapid path expansion in obstacle-free areas, avoiding the inefficient calculations caused by uniform extension and improving planning efficiency. If there are obstacles, the operation is repeated until the preset second iteration conditions are met, after which the step-size is halved. This allows for detailed exploration in obstacle-prone areas and reduces inefficient attempts. The resulting optimized primary RRT tree makes path planning more targeted and efficient, effectively resolving the issue of excessive inefficient calculations in the RRT tree.

[0009] Furthermore, the node addition method of the primary RRT tree is optimized to obtain an optimized RRT tree, including: randomly obtaining a point on the map to obtain a third random point; finding the node pnear closest to the third random point from the primary RRT tree; obtaining the direction of the third random point according to the map to obtain a second direction; obtaining its step length from the primary RRT tree to obtain a second step length; taking the node pnear as the starting point, obtaining a node pnew according to the second step length and the second direction; performing node analysis on the primary RRT tree through a node optimization algorithm to obtain a node analysis result, and performing result analysis on the node analysis result; when the node analysis result shows that there is a node that meets the preset node conditions, If there is no primary RRT tree node that meets the preset node conditions, the primary RRT tree node that meets the preset node conditions is set as node pmin, and node pmin is set as the parent node of node pnew to obtain an optimized RRT tree; when the node analysis result is that there is no primary RRT tree node that meets the preset node conditions, it is determined whether there is an obstacle between node pnear and node pnew; if there is no obstacle between node pnear and node pnew, node pnear is set as the parent node of node pnew to obtain an optimized RRT tree; if there is an obstacle between node pnear and node pnew, the execution returns to randomly obtain a point on the map to obtain an optimized RRT tree. First, a third random point is randomly obtained on the map, and the node pnear closest to the third random point is found from the primary RRT tree. Taking this node as the starting point, a new node is obtained according to the second step length and the second direction, and then analyzed by the node optimization algorithm; if there is a node that meets the preset node conditions, it can be set as the parent node of the new node to obtain an optimized RRT tree, making the tree structure more reasonable and improving the efficiency of path planning; if there is no node that meets the conditions and there is no obstacle between the new node and the node pnear, the node pnear is also set as the parent node of the new node to obtain an optimized RRT tree; if there is an obstacle between the node pnear and the node pnew, then return to execute and randomly obtain a point on the map to obtain the optimized RRT tree to avoid invalid extension. This series of operations reduces the invalid calculation of the RRT tree, makes path planning more accurate and efficient, and provides a better infrastructure for subsequent path planning, which helps to improve the quality and speed of overall path planning and adapt to complex map environment requirements.

[0010] Furthermore, the bidirectional path planning is performed according to the optimized RRT tree to obtain the original path between the two task points, including: setting any one of the two task points as the first task point; obtaining the direction of the adjacent task point of the first task point based on the first task point and the map to obtain the third direction; randomly generating a first value according to a preset value range, and generating an optimized angle according to the first value and the third direction; obtaining the distance between the first task point and its adjacent task point according to the map to obtain the adjacent distance; obtaining the first range according to the adjacent distance and the preset range adjustment condition; randomly generating a second value according to the first range; obtaining the coordinates of the first task point according to the map; generating optimized coordinates according to the coordinates of the first task point, the optimized angle and the optimized distance value; and adjusting the distance according to the optimized coordinates. Generate a sampling point on the map and determine whether the sampling point is in an obstacle; when the sampling point is not in an obstacle, let the optimized RRT tree search whether there is an optimized RRT tree node of an adjacent task point within the preset second range; when the optimized RRT tree does not find the optimized RRT tree node of the adjacent task point, obtain its step length from the optimized RRT tree to obtain a third step length; obtain the direction of the sampling point according to the map; extend the optimized RRT tree to obtain a new node according to the third step length and the direction of the sampling point; when the optimized RRT tree finds the optimized RRT tree node of the adjacent task point, obtain its step length from the optimized RRT tree to obtain a third step length; according to the third step length, let the optimized RRT tree extend to intersect with the optimized RRT tree of the adjacent task point to obtain the original path between the two adjacent task points. By selecting any task point as the first task point and obtaining the directions of adjacent task points, the third direction is determined, providing an initial goal orientation for path planning and giving the entire planning process a clear direction. A randomly generated first value is combined with an optimization angle generated from the third direction to ensure the directionality of path planning while maintaining randomness. This increases the likelihood that the optimized RRT tree will expand toward the destination, making path planning more targeted. The adjacent distances between task points are obtained and adjusted according to a preset range to obtain the first range, dynamically adjusting the search range and improving the targetedness and efficiency of path search. Determining whether a sampling point is located on an obstacle improves the safety of path planning and ensures the feasibility of the path. When a sampling point is not located on an obstacle, the optimized RRT tree is used to search for adjacent task point nodes, improving the accuracy and efficiency of path planning. If no node is found, a path is extended toward the sampling point. If a node is found, the original path is intersected, significantly improving the speed and directness of path planning. These operations make the sampling points more directional and increase the probability of being arranged toward the destination. The optimized RRT tree can more efficiently expand toward the destination, reducing ineffective exploration, improving the accuracy and efficiency of path planning, and providing a more reliable path guarantee for achieving the mission objectives.

[0011] Furthermore, the depth smoothing operation is performed on the original path to obtain a smoothed path, including: obtaining the depth of the starting task point of the original path according to the map to obtain a first depth; obtaining the depth of the ending task point of the original path according to the map to obtain a second depth; performing depth smoothing on all path points on the original path one by one starting from the starting task point according to the first depth and the second depth to obtain a third depth for each path point; retrieving the path points at the third depth according to the map to obtain a first retrieval result; analyzing the first retrieval result and determining whether there are path points with obstacles in the first retrieval result; when the first retrieval result is that there are no path points with obstacles, generating a smoothed path according to the first depth, the second depth and the third depth; conversely, obtaining the depth of all path points on the original path according to the map to obtain the original depth of each path point; obtaining a fourth depth based on the original depth and the depth of all path points output by a preset depth optimization algorithm; and generating a smoothed path according to the first depth, the second depth and the fourth depth. First, by obtaining the depth information of the starting and ending task points, a depth reference is provided for the depth balancing operation of the path points; then, depth smoothing is performed on all path points on the original path to ensure that the depth changes of the path points are smooth; by retrieving and analyzing the depth information, it is possible to determine whether there are obstacles at the path points, thereby avoiding potential risks; if there are no obstacles, a smoothed path is obtained based on the depth information; if there are obstacles, the depth is re-optimized and a new smoothed path is obtained; through a series of depth processing steps, the depth smoothing and obstacle avoidance of the original path are achieved, and a balanced path is obtained, which improves the safety and feasibility of the path; this series of steps not only improves the adaptability and robustness of the path, but also reduces the significant impact of frequent extreme changes in depth on the performance of the autonomous underwater robot, ensuring the safety of the autonomous underwater robot's underwater operations, and has important practical significance for the path planning of the autonomous underwater robot; overall, these steps can optimize the path depth, improve the quality and safety of path planning, and ensure that the path is more in line with actual navigation needs.

[0012] Furthermore, the iterative optimization process of the smoothed path to obtain an iteratively optimized path includes: obtaining the path length between two task points on the smoothed path according to a map to obtain an initial length; utilizing a path planning algorithm to obtain an optimized path and an optimized length corresponding to the optimized path; comparing the initial length with the optimized length; and if the optimized length is less than the initial length, updating the initial length with the optimized length, and returning to execute the path planning algorithm to obtain the optimized path; and outputting an iteratively optimized path corresponding to the optimized length when a preset iterative stopping condition is met. First, by obtaining the initial length of the path between the two task points, a benchmark is provided for subsequent optimization; then, utilizing the path planning algorithm to obtain the optimized path and its length, this step aims to reduce the total length of the path and improve efficiency; by comparing the initial length with the optimized length, if the optimized result is better, updating the initial length and continuing the optimization process, forming an iterative process until a preset iterative stopping condition is met. This method not only reduces path length and improves the economy and efficiency of the path, but also ensures that the final iteratively optimized path is output after the conditions are met, thus ensuring the accuracy and practicality of the path planning. Overall, these steps optimize the path length and enhance the overall performance of the path planning.

[0013] Furthermore, obtaining the navigation status based on the iteratively optimized path and adjusting the iteratively optimized path based on the navigation status to obtain the optimized navigation path includes: obtaining the navigation status based on the iteratively optimized path; performing a state analysis on the navigation status; and, when the navigation status indicates the presence of an obstacle, adjusting the iteratively optimized path to obtain the optimized navigation path. Obtaining the navigation status through the iteratively optimized path and performing a state analysis to identify obstacles; once an obstacle is detected, the path is adjusted in a timely manner to ensure navigation safety and efficiency, thereby optimizing the navigation path and improving the navigation capability of the autonomous underwater vehicle.

[0014] Furthermore, the path planning device based on the RRT_star algorithm includes

[0015] The original RRT tree creation module uses the RRT_star algorithm to create original RRT trees at two adjacent mission points on a preset map. The original RRT tree optimization module optimizes the original RRT tree to obtain an optimized RRT tree. The original path planning module performs bidirectional path planning based on the optimized RRT tree to obtain the original path between the two mission points. The smoothed path generation module performs deep smoothing on the original path to obtain a smoothed path. The iteratively optimized path generation module iteratively optimizes the smoothed path to obtain an iteratively optimized path. The navigation-optimized path generation module obtains navigation status based on the iteratively optimized path and adjusts the iteratively optimized path accordingly to obtain a navigation-optimized path. Using the optimized RRT tree to implement bidirectional path exploration fully utilizes computing resources, accelerates path generation, and improves planning efficiency. Deep smoothing effectively reduces path depth fluctuations, which is particularly suitable for autonomous underwater vehicles (AUVs) sensitive to depth changes, minimizing the impact on instrument performance and safety. Iterative optimization further improves path quality and enhances adaptability. Finally, the path is adjusted based on navigation status to ensure that the plan aligns with actual navigation conditions, enhancing path feasibility and safety.

[0016] Furthermore, a path planning device based on the RRT_star algorithm comprises: a memory and at least one processor, wherein instructions are stored in the memory; at least one processor calls the instructions in the memory so that the path planning device based on the RRT_star algorithm performs the various steps of the path planning method based on the RRT_star algorithm as described above.

[0017] Furthermore, a computer-readable storage medium stores instructions thereon, characterized in that when the instructions are executed by a processor, the steps of the path planning method based on the RRT_star algorithm as described above are implemented.

[0018] The beneficial effects of the path planning method based on the RRT_star algorithm of the present invention are:

[0019] The optimized RRT tree is used to simultaneously explore and plan paths from the starting and ending task points, making full use of the computer's powerful computing resources, further accelerating the path generation speed and improving the efficiency of path planning; the original path is depth-smoothed to obtain a smoothed path, which can effectively solve problems such as excessive depth fluctuations at the intermediate points of the planned path, making the path smoother. This is especially true for application scenarios such as autonomous underwater robots that are sensitive to depth changes, and can reduce the impact of frequent depth changes on the performance and safety of the autonomous underwater robot's internal instruments; subsequently, the smoothed path is iteratively optimized to obtain an iteratively optimized path. This step further improves the quality and adaptability of the path; finally, the navigation status is obtained based on the iteratively optimized path, and the iteratively optimized path is adjusted according to the navigation status to obtain the final navigation optimized path. This adjustment makes the path planning more in line with actual navigation conditions and improves the feasibility and safety of the path. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 A first flow chart of a path planning method based on the RRT_star algorithm provided in an embodiment of the present invention;

[0021] Figure 2 A second flow chart of the path planning method based on the RRT_star algorithm provided in an embodiment of the present invention;

[0022] Figure 3 A third flow chart of a path planning method based on the RRT_star algorithm provided in an embodiment of the present invention;

[0023] Figure 4 A fourth flow chart of a path planning method based on the RRT_star algorithm provided in an embodiment of the present invention;

[0024] Figure 5 A fifth flow chart of a path planning method based on the RRT_star algorithm provided in an embodiment of the present invention;

[0025] Figure 6 A sixth flow chart of a path planning method based on the RRT_star algorithm provided in an embodiment of the present invention;

[0026] Figure 7 A seventh flow chart of a path planning method based on the RRT_star algorithm provided in an embodiment of the present invention;

[0027] Figure 8 An eighth flow chart of a path planning method based on the RRT_star algorithm provided in an embodiment of the present invention;

[0028] Figure 9A schematic structural diagram of a path planning device based on the RRT_star algorithm provided in an embodiment of the present invention;

[0029] Figure 10 A schematic structural diagram of a path planning device based on the RRT_star algorithm provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0030] The technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0031] The terms "first," "second," "third," "fourth," and the like (if any) in the description and claims of the present invention and in the accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a particular order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or apparatus that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to these processes, methods, products, or apparatus.

[0032] For ease of understanding, the specific process of the embodiment of the present invention is described below. Figure 1 In one embodiment of the present invention, a path planning method based on the RRT_star algorithm includes:

[0033] 101: Use the RRT_star algorithm to create the original RRT tree at two adjacent task points on the preset map;

[0034] 102: Optimizing the original RRT tree to obtain an optimized RRT tree;

[0035] 103: Perform bidirectional path planning based on the optimized RRT tree to obtain the original path between the two task points;

[0036] 104: performing a deep smoothing operation on the original path to obtain a smoothed path;

[0037] 105: performing an iterative optimization operation on the smooth path to obtain an iteratively optimized path;

[0038] 106: Obtaining a navigation state according to the iterative optimization path, and adjusting the iterative optimization path according to the navigation state to obtain a navigation optimization path.

[0039] In this embodiment, the optimization operation of the original RRT tree involves improving and optimizing the step size control parameters and node addition method in the ordinary RRT tree; when encountering a situation in actual application scenarios where it is necessary to plan paths between multiple adjacent task points, in order to improve planning efficiency, the appropriate number of threads should be selected based on the number of CPU cores, and then a multi-threaded method should be used to perform parallel path planning operations for the paths between these multiple adjacent task points, and the intermediate task point performs a bidirectional search on the two adjacent task points before and after it, which is equivalent to the two adjacent task points searching and extending toward each other.

[0040] The optimized RRT tree is used to simultaneously explore and plan paths from the starting and ending task points, making full use of the computer's powerful computing resources, further accelerating the path generation speed and improving the efficiency of path planning; the original path is depth-smoothed to obtain a smoothed path, which can effectively solve problems such as excessive depth fluctuations at the intermediate points of the planned path, making the path smoother. This is especially true for application scenarios such as autonomous underwater robots that are sensitive to depth changes, and can reduce the impact of frequent depth changes on the performance and safety of the autonomous underwater robot's internal instruments; subsequently, the smoothed path is iteratively optimized to obtain an iteratively optimized path. This step further improves the quality and adaptability of the path; finally, the navigation status is obtained based on the iteratively optimized path, and the iteratively optimized path is adjusted according to the navigation status to obtain the final navigation optimized path. This adjustment makes the path planning more in line with actual navigation conditions and improves the feasibility and safety of the path.

[0041] See also Figure 2 The second embodiment of the path planning method based on the RRT_star algorithm in the embodiment of the present invention includes:

[0042] 201: Optimizing the step size of the original RRT tree to obtain an optimized primary RRT tree;

[0043] 202: Optimize the node adding method of the primary RRT tree to obtain an optimized RRT tree.

[0044] In this embodiment, by optimizing the step size of the original RRT tree, ineffective exploration caused by unreasonable step size can be avoided. The optimized step size can better adapt to different environments and improve the efficiency of path planning. At the same time, the optimized primary RRT tree provides a better basis for subsequent further optimization. Optimizing the node addition method of the primary RRT tree can make the tree structure more reasonable, make the tree growth more targeted, further improve the efficiency of path planning, and generate an optimized RRT tree that better meets actual needs.

[0045] See also Figure 3 The third embodiment of the path planning method based on the RRT_star algorithm in the embodiment of the present invention includes:

[0046] 301: randomly obtain a point on the map to obtain a first random point;

[0047] 302: Determine whether there is an obstacle at the first random point based on the map;

[0048] 303: When there is no obstacle at the first random point, obtaining a direction of the first random point according to the map to obtain a first direction;

[0049] 304: Obtain the node closest to the first random point from the original RRT tree to obtain node P1;

[0050] 305: Obtain the step length from the original RRT tree to obtain the first step length;

[0051] 306: Taking node P1 as the starting point, obtain a point on the map according to the first step length and the first direction to obtain a second random point;

[0052] 307: Determine whether there is an obstacle between the node P1 and the second random point according to the map;

[0053] 308: If there is no obstacle, the process returns to randomly obtain a point on the map until the preset first iteration condition is met, then the first step length is doubled to obtain the optimized primary RRT tree;

[0054] 309: If there is an obstacle, the process returns to randomly obtain a point on the map until the preset second iteration condition is met, and the first step length is halved to obtain an optimized primary RRT tree.

[0055] In this embodiment, the first iteration condition is to execute step 301 for T0 (preset value) consecutive times; the second iteration condition is to execute step 301 for T1 (preset value) consecutive times, wherein the step size of the RRT tree has an update upper and lower limit, and the preset value in the step size update condition of each RRT tree is different;

[0056] First, in step 301, a random point is generated on the map to obtain the first random point. Then, in step 304, the node closest to the first random point is obtained from the original RRT tree to obtain node P1, providing a good starting point for subsequent step-size optimization. The obstacle situation between node P1 and the first random point is determined. If there are no obstacles, the operation is repeated until the preset first iteration condition is met, then the step-size is doubled. This allows for rapid path expansion in obstacle-free areas, avoiding inefficient calculations caused by uniform extension and improving planning efficiency. If there are obstacles, the operation is repeated until the preset second iteration condition is met, then the step-size is halved, allowing for detailed exploration in obstacle-prone areas to reduce inefficient attempts. Ultimately, the optimized primary RRT tree is obtained, making path planning more targeted and efficient, effectively solving the problem of excessive inefficient calculations in the RRT tree.

[0057] See also Figure 4 The fourth embodiment of the path planning method based on the RRT_star algorithm in the embodiment of the present invention includes:

[0058] 401: Get a random point on the map to get the third random point;

[0059] 402: Find the node pnear closest to the third random point from the primary RRT tree;

[0060] 403: Obtain the direction of the third random point according to the map to obtain the second direction;

[0061] 404: Obtain the step length from the primary RRT tree to obtain a second step length;

[0062] 405: Taking the node pnear as the starting point, obtain the node pnew according to the second step length and the second direction;

[0063] 406: Performing node analysis on the primary RRT tree using a node optimization algorithm to obtain a node analysis result, and performing result analysis on the node analysis result;

[0064] 407: When the node analysis result shows that there is a primary RRT tree node that meets the preset node conditions, the primary RRT tree node that meets the preset node conditions is set as node pmin, and node pmin is set as the parent node of node pnew to obtain an optimized RRT tree;

[0065] 408: When the node analysis result shows that there is no primary RRT tree node that meets the preset node conditions, determine whether there is an obstacle between the node pnear and the node pnew;

[0066] 409: If there is no obstacle between the node pnear and the node pnew, set the node pnear as the parent node of the node pnew to obtain the optimized RRT tree;

[0067] 410: If there is an obstacle between the node pnear and the node pnew, return to execute to randomly obtain a point on the map to obtain the optimized RRT tree;

[0068] In this embodiment, the existing node adding method of the original RRT tree (existing ordinary RRT tree) is optimized. The existing node adding method includes the following steps 1, 2, 3, and 4, where steps 1, 2, 3, 4, and 5 are steps of the existing algorithm:

[0069] 1. Randomly point Prand on the map;

[0070] 2. Find the node Pnear closest to the random point Prand in the tree T, and extend the distance d from Pnear to the random point Prand to get the node Pnew;

[0071] 3. Test whether there is an obstacle collision between Pnear and Pnew. If so, return to step 1. If not, set the parent node of Pnew to Pnear.

[0072] 4. Find a node Pmin near Pnew in the tree T (with a distance radius less than r) so that the total distance from Pmin to Pnew is the shortest and there is no obstacle collision. If the total distance from Pmin to Pnew is shorter than the total distance from Pnear to Pnew, change the parent node of node Pnew to node Pmin.

[0073] 5. Find all nodes p1 near Pnew in the tree T. If the total distance from Pnew to p1 is shorter than the total distance from p1's current parent node to p1 and there is no obstacle collision, change p1's parent node to Pnew.

[0074] Assuming that there are nodes a and b on the primary RRT tree, the total length from node a to node b is the path distance from the starting task point to node a plus the distance from node a to node b. The specific steps of performing node analysis on the primary RRT tree using the node optimization algorithm in step 404 include:

[0075] Determine, based on the node optimization algorithm, whether there is a total length from a node to the node pnew within a preset third range in the primary RRT tree that is shorter than the total length from the node pnear to the node pnew, to obtain a first determination result;

[0076] When there is a node within the third range in the primary RRT tree where the total length from the node to the node pnew is shorter than the total length from the node pnear to the node pnew, determining whether there is an obstacle between the node and the node pnew based on the node optimization algorithm to obtain a second determination result;

[0077] When there is no obstacle between the node and the node pnew, a node analysis result is generated based on the node optimization algorithm, the first determination result, and the second determination result;

[0078] When there is an obstacle between the node and the node pnew, a node analysis result is generated based on the node optimization algorithm, the first determination result, and the second determination result;

[0079] When the primary RRT tree does not have a total length from a node to the node pnew shorter than the total length from the node pnear to the node pnew within the third range, generating a node analysis result based on the node optimization algorithm and the first determination result;

[0080] First, a third random point is randomly obtained on the map, and the node pnear closest to the third random point is found from the primary RRT tree. Taking this node as the starting point, a new node is obtained according to the second step length and the second direction, and then analyzed by the node optimization algorithm; if there is a node that meets the preset node conditions, it can be set as the parent node of the new node to obtain an optimized RRT tree, making the tree structure more reasonable and improving the efficiency of path planning; if there is no node that meets the conditions and there is no obstacle between the new node and the node pnear, the node pnear is also set as the parent node of the new node to obtain an optimized RRT tree; if there is an obstacle between the node pnear and the node pnew, then return to execute a random point on the map to avoid invalid extension. This series of operations reduces the invalid calculation of the RRT tree, makes path planning more accurate and efficient, and provides a better infrastructure for subsequent path planning, which helps to improve the quality and speed of overall path planning and adapt to complex map environment requirements.

[0081] See also Figure 5 The fifth embodiment of the path planning method based on the RRT_star algorithm in the embodiment of the present invention includes:

[0082] 501: Set any of the two mission points as the first mission point;

[0083] 502: Obtain directions of adjacent task points of the first task point based on the first task point and the map to obtain a third direction;

[0084] 503: randomly generating a first value according to a preset value range, and generating an optimized angle according to the first value and the third direction;

[0085] 504: Obtain the distance between the first task point and its adjacent task points according to the map to obtain the adjacent distance;

[0086] 505: Obtain a first range according to the adjacent distance and a preset range adjustment condition;

[0087] 506: randomly generate a second value based on the first range;

[0088] 507: Obtain the coordinates of the first mission point according to the map;

[0089] 508: Generate optimized coordinates according to the coordinates of the first task point, the optimized angle, and the optimized distance value;

[0090] 509: Generate sampling points on the map according to the optimized coordinates, and determine whether the sampling points are located near obstacles;

[0091] 510: When there is no obstacle at the sampling point, let the optimized RRT tree search whether there is an optimized RRT tree node of an adjacent task point within the preset second range;

[0092] 511: When the optimized RRT tree fails to find the optimized RRT tree node of the adjacent task point, obtain its step length from the optimized RRT tree to obtain a third step length;

[0093] 512: Get the direction of the sampling point according to the map;

[0094] 513: Extend the optimized RRT tree to obtain a new node according to the third step length and the sampling point direction;

[0095] 514: When the optimized RRT tree finds the optimized RRT tree node of the adjacent task point, the step length thereof is obtained from the optimized RRT tree to obtain a third step length;

[0096] 515: Extend the optimized RRT tree to intersect with the optimized RRT tree of the adjacent task point according to the third step length, so as to obtain the original path between the two adjacent task points.

[0097] In this embodiment, it is assumed that the robot needs to pass through multiple task points during a navigation operation. For a task point C, its next adjacent task point D is at a 45° direction, and its previous adjacent task point B is at a -90° direction. If the optimized RRT tree on the task point C needs to be able to expand both toward the task point B and toward the task point D, the expansion range of the optimized RRT tree on the task point C needs to fall within the angle range of ±15° in the direction of the task point B and ±15° in the direction of the task point D (the angle can be set to other appropriate values, here it is 30° to 60° and -105° to -75°). In this way, there is a greater possibility that the optimized RRT tree on the task point C will expand toward the adjacent task points of the task point C.

[0098] To facilitate understanding of this embodiment, the following example is explained using task points C, D, and B. First, a number is randomly generated from 1 to 100 to obtain a value a (501: a first value is randomly generated according to a preset value range), and each value in the interval has an equal probability of being randomly selected. In actual operation, a random number can be selected from a range of 1 to 10,000 or larger. The random number is mapped to an angle: if the value a is in the range of 1 to 66, it is mapped to 30° to 60° or -105° to -75° (uniformly distributed. For example, if the randomly generated value a is in the range of 1 to 33, the value a is converted into an angle α. If the value a is in the range of 34 to 66, convert the value a into an angle α. ), if the value a is in the range of 67 to 100, then the value a is mapped to other angles (uniformly distributed). (And the optimized angle is generated based on the first value and the third direction;) That is, the probability that the angle mapped by the value a falls into the range of 30° to 60° (±15° in the D direction) or -105° to -75° (±15° in the B direction) is The probability that the angle mapped by the value a falls into the remaining interval is

[0099] The following is an example of expanding the generation of the second random value: If the distance between two adjacent task points is L, (here the distance between task point C and task point D or the distance between task point C and task point B is used as an example), L is updated after Ti times of iterative update operations to obtain the final L, (i is the number of iterative updates of L), L increases by 1.5 times after each iterative update, that is, L = L*1.5, and the maximum value of L shall not exceed the length of the path that has been explored between the two adjacent task points (here the length of the path that has been explored between task point C and task point D or the length of the path that has been explored between task point C and task point B is used as an example); if there is no explored path between the two task points, L shall not exceed the maximum navigation distance of the robot, that is, the distance between the starting task point and the end task point of the robot's current operation; the final L is obtained according to the above adjustment method (that is, the range adjustment condition referred to in step 503); according to the L generation interval (0 to )(the first range of step 503); from (0 to ) randomly selects a value d (the second value in step 504 );

[0100] Assume that the coordinates of the task point C are (cx, cy), then the coordinates of the sampling point can be obtained by the following formula: x = cx + d*cosα, y = cy + d*sinα, and the coordinates of the sampling point are (cx + d*cosα, cy + d*sinα).

[0101] By selecting any task point as the first task point and obtaining the directions of adjacent task points, the third direction is determined, providing an initial goal orientation for path planning and giving the entire planning process a clear direction. A randomly generated first value is combined with an optimization angle generated from the third direction to ensure the directionality of path planning while maintaining randomness. This increases the likelihood that the optimized RRT tree will expand toward the destination, making path planning more targeted. The adjacent distances between task points are obtained and adjusted according to a preset range to obtain the first range, dynamically adjusting the search range and improving the targetedness and efficiency of path search. Determining whether a sampling point is located on an obstacle improves the safety of path planning and ensures the feasibility of the path. When a sampling point is not located on an obstacle, the optimized RRT tree is used to search for adjacent task point nodes, improving the accuracy and efficiency of path planning. If no node is found, a path is extended toward the sampling point. If a node is found, the original path is intersected, significantly improving the speed and directness of path planning. These operations make the sampling points more directional and increase the probability of being arranged toward the destination. The optimized RRT tree can more efficiently expand toward the destination, reducing ineffective exploration, improving the accuracy and efficiency of path planning, and providing a more reliable path guarantee for achieving the mission objectives.

[0102] See also Figure 6 The sixth embodiment of the path planning method based on the RRT_star algorithm in the embodiment of the present invention includes:

[0103] 601: Obtain the depth of the starting task point of the original path according to the map to obtain a first depth;

[0104] 602: Obtain the depth of the end task point of the original path according to the map to obtain a second depth;

[0105] 603: Perform depth smoothing processing on all path points on the original path starting from the starting task point according to the first depth and the second depth to obtain a third depth for each path point;

[0106] 604: searching for a path point at a third depth according to the map to obtain a first search result;

[0107] 605: Analyze the first search result and determine whether there is a path point with an obstacle in the first search result;

[0108] 606: When the first search result indicates that there is no path point with an obstacle, generate a smooth path according to the first depth, the second depth, and the third depth;

[0109] 607: Conversely, obtain the depth of all path points on the original path according to the map to obtain the original depth of each path point;

[0110] 608: Obtain a fourth depth based on the original depth and the depths of all path points output by a preset depth optimization algorithm;

[0111] 609: Generate a smooth path according to the first depth, the second depth, and the fourth depth.

[0112] In this embodiment, if the depth of the middle path point between two task points is higher / lower than the depth of the two task points, it can be considered to be directly raised / lowered, that is, the depth balance operation is performed on the middle path point; for ease of understanding, the following example is used to explain: Assume that the robot needs to pass through multiple task points during a navigation operation. For two adjacent task points A and B, it is assumed that the planned path between task points A and B is needed as the robot's underwater navigation route, where task point A is the starting point on the navigation route, the depth of task point A is 100, and task point B is the end point. The depth is also 100. Assuming there are only two path points p1 and p2 between task points A and B, and the distance from task point A to p1 is equal to the distance from p1 to p2, and equal to the distance from p2 to task point B, the robot's navigation order is: A->p1->p2->B. If the depth of p1 is 150 and the depth of p2 is 50, the depth of the navigation route changes from 100->150->50->100. The depth changes from 100 to 150, then to 50, and finally back to 100. This frequent fluctuation in depth is caused by the random expansion of the optimized RRT tree itself.

[0113] In fact, in most cases, it is not necessary to perform such frequent depth changes during navigation. In this case, recursive interpolation can be used to perform smooth depth processing. If an obstacle appears at a path point during the smooth depth processing operation, the path point with the obstacle is regarded as a point whose depth cannot be changed. The recursive interpolation method is continued to be used to perform smooth depth processing on other path points on the navigation route where no obstacle exists.

[0114] According to the depth of task points A and B, p1 is first interpolated and smoothed to 100, that is, it becomes 100->100->50->100, and then p2 is smoothed to 100;

[0115] However, assuming that there is an obstacle at a depth of 100 in p2, and it must float up to 50 to bypass it (that is, it cannot be smoothed to 100), the depth value of p2 is not modified, and then the depths from task point A to p2 and from p2 to task point B are recursively interpolated and smoothed. The depth of p1 is smoothed to 75 based on the depths of task points A and p2 (original depths). There are no other points between p2 and point B, and the final path depth change between task points A and B is smoothed to 100->75->50->100 (608: the depths of all path points output based on the original depth and the preset depth optimization algorithm to obtain the fourth depth;).

[0116] First, by obtaining the depth information of the starting and ending task points, a depth reference is provided for the depth balancing operation of the path points; then, depth smoothing is performed on all path points on the original path to ensure that the depth changes of the path points are smooth; by retrieving and analyzing the depth information, it is possible to determine whether there are obstacles at the path points, thereby avoiding potential risks; if there are no obstacles, a smoothed path is obtained based on the depth information; if there are obstacles, the depth is re-optimized and a new smoothed path is obtained; through a series of depth processing steps, the depth smoothing and obstacle avoidance of the original path are achieved, and a balanced path is obtained, which improves the safety and feasibility of the path; this series of steps not only improves the adaptability and robustness of the path, but also reduces the significant impact of frequent extreme changes in depth on the performance of the autonomous underwater robot, ensuring the safety of the autonomous underwater robot's underwater operations, and has important practical significance for the path planning of the autonomous underwater robot; overall, these steps can optimize the path depth, improve the quality and safety of path planning, and ensure that the path is more in line with actual navigation needs.

[0117] See also Figure 7 The seventh embodiment of the path planning method based on the RRT_star algorithm in the embodiment of the present invention includes:

[0118] 701: Obtain the path length between two task points on the smooth path according to the map to obtain an initial length;

[0119] 702: Using a path planning algorithm to obtain an optimized path and an optimized length corresponding to the optimized path;

[0120] 703: Compare the initial length and the optimized length. If the optimized length is smaller than the initial length, update the initial length with the optimized length, and return to execute the path planning algorithm to obtain the optimized path.

[0121] 704: When a preset iteration stop condition is met, output an iterative optimization path corresponding to the optimization length.

[0122] In this embodiment, the iteration stopping condition is: no better path is found or the path between the two task points needs to be used for T2 consecutive times (T2 is a preset value); the specific steps of planning the optimized path according to the path planning algorithm are:

[0123] Take the two task points as the focus and the initial length as the major axis to construct an ellipse range;

[0124] By optimizing the RRT tree, bidirectional path planning is performed within the ellipse to obtain the optimized path and the optimized length corresponding to the optimized path.

[0125] The ellipse has a property: for a point p that falls on the edge of the ellipse, the sum of the distances from p to the two foci is equal to the length of the major axis of the ellipse. That is, if a point falls outside the ellipse when exploring a new path, the length of the path passing through this point must be longer than the major axis (the length of the original path), and there is no need to continue subsequent calculations for this point.

[0126] To facilitate understanding of this embodiment, the embodiment is explained below through an example: Assume that the robot needs to pass through multiple task points during a navigation operation. During the navigation process, the path between any two adjacent task points can continue to be calculated and optimized: for the two task points, it has been calculated that there is a path that can be reached. Assuming that the length of this path is l, an ellipse is generated with the two task points as the focus and l as the major axis, and then a new two-way path planning is performed within the range of this ellipse (the nodes of the optimized RRT tree are all within the range of the ellipse). If a path with a shorter length of l0 is found, a new ellipse is generated with the two task points as the focus and l0 as the major axis, and the path iterative optimization operation is continued in a loop to make the planned path between the two task points shorter and shorter, until no better path is found for T2 consecutive times (T2 is a preset value) or the robot has reached the starting point of the two task points, that is, it is necessary to start navigation along the optimized path between the two task points and end the iterative optimization of the path between the two task points.

[0127] First, by obtaining the initial length of the path between the two task points, a benchmark is provided for subsequent optimization. Next, a path planning algorithm is used to obtain the optimized path and its length. This step aims to reduce the total length of the path and improve efficiency. By comparing the initial length and the optimized length, if the optimization result is better, the initial length is updated and optimization is continued, forming an iterative process until the preset iteration stop condition is met. This method not only reduces the path length and improves the economy and efficiency of the path, but also ensures that the final iterative optimization path is output after the conditions are met, ensuring the accuracy and practicality of path planning. Overall, these steps achieve the optimization of path length and improve the overall performance of path planning.

[0128] See also Figure 8The seventh embodiment of the path planning method based on the RRT_star algorithm in the embodiment of the present invention includes:

[0129] 801: Obtaining navigation status according to the iterative optimization path;

[0130] 802: Perform status analysis on navigation status;

[0131] 803: When the navigation state indicates that there is an obstacle blocking the navigation, the navigation optimization path is obtained according to the iterative optimization path adjustment.

[0132] In this embodiment, the iterative optimization path adjustment method includes removing the edges of the optimized RRT tree where obstacles exist from the optimized RRT tree, and then continuing to perform path search and adjustment operations based on the original path based on this optimized RRT tree to obtain an adjusted route to obtain a navigation optimized path. By adopting this method, the adjustment operation can be performed based on the original path, saving the computing power of the previously calculated part and avoiding repeated calculation of the calculated route; because the optimized RRT tree has already been established on the map and the map has been explored to a certain extent, the existing information can be continued in the original optimized RRT tree and the exploration can continue to extend toward the target task point. Since it is a two-way search, the target task point also extends an optimized RRT tree, so the branches of the two optimized RRT trees are relatively close. In fact, the two optimized RRT trees can be quickly reconnected to obtain the adjusted path.

[0133] The navigation status is obtained by iteratively optimizing the path, and status analysis is performed to identify obstacles. Once an obstacle is found, the path is adjusted in time to ensure navigation safety and efficiency, thereby optimizing the navigation path and improving the navigation capability of the autonomous underwater robot.

[0134] The above describes the path planning method based on the RRT_star algorithm in the embodiment of the present invention. The following describes the path planning device based on the RRT_star algorithm in the embodiment of the present invention. Figure 9 In one embodiment of the present invention, a path planning device based on the RRT_star algorithm includes:

[0135] A path planning device based on the RRT_star algorithm includes:

[0136] The original RRT tree creation module 1 is used to create an original RRT tree at two adjacent task points on a preset map using the RRT_star algorithm;

[0137] The original RRT tree optimization module 2 is used to optimize the original RRT tree to obtain an optimized RRT tree;

[0138] The original path planning module 3 is used to perform bidirectional path planning based on the optimized RRT tree to obtain the original path between the two task points;

[0139] The smooth path generation module 4 is used to perform a deep smoothing operation on the original path to obtain a smooth path;

[0140] Iterative optimization path generation module 5, used for performing iterative optimization processing operations on the smooth path to obtain an iterative optimization path;

[0141] The navigation optimization path generation module 6 is used to obtain the navigation status according to the iterative optimization path, and adjust the iterative optimization path according to the navigation status to obtain the navigation optimization path.

[0142] In this embodiment, an optimized RRT tree is used to implement bidirectional path exploration, fully utilizing computing resources, accelerating path generation, and improving planning efficiency. Depth smoothing effectively reduces path depth fluctuations, making it particularly suitable for autonomous underwater vehicles (AUVs) sensitive to depth variations, minimizing the impact on instrument performance and safety. Iterative optimization further improves path quality and enhances adaptability. Finally, the path is adjusted based on navigation status to ensure that the plan aligns with actual navigation conditions, improving path feasibility and safety.

[0143] Figure 10 6 is a schematic diagram of the structure of a path planning device based on the RRT_star algorithm provided in an embodiment of the present invention. The path planning device 600 based on the RRT_star algorithm may vary significantly due to different configurations or performances, and may include one or more processors (central processing units, CPUs) 613 (for example, one or more processors) and a memory 620, and one or more storage media 630 (for example, one or more mass storage devices) storing application programs 633 or data 632. The memory 620 and the storage medium 630 may be temporary storage or permanent storage. The program stored in the storage medium 630 may include one or more modules (not shown), each of which may include a series of instruction operations in the path planning device 600 based on the RRT_star algorithm. Furthermore, the processor 610 may be configured to communicate with the storage medium 630, and execute a series of instruction operations in the storage medium 630 on the path planning device 600 based on the RRT_star algorithm to implement the steps of the decision-making assistance method provided in the above-mentioned method embodiments.

[0144] The path planning device 600 based on the RRT_star algorithm may further include one or more power supplies 640, one or more wired or wireless network interfaces 650, one or more input and output interfaces 660, and / or one or more operating systems 631, such as Windows Server, Mac OS X, Unix, Linux, FreeBSD, etc. It will be understood by those skilled in the art that Figure 10 The illustrated structure of the path planning device based on the RRT_star algorithm does not constitute a limitation on the path planning device based on the RRT_star algorithm, and may include more or fewer components than illustrated, or a combination of certain components, or a different arrangement of components.

[0145] The present invention also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. The computer-readable storage medium stores instructions, which, when executed on a computer, cause the computer to execute the steps of a path planning method based on the RRT_star algorithm.

[0146] Finally, it should be noted that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art will be able to modify the technical solutions described in the aforementioned embodiments or substitute equivalents for some of the technical features. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A path planning method based on the RRT_star algorithm, characterized by: include: The RRT_star algorithm is used to create the original RRT tree at two adjacent task points on the preset map; Optimizing the original RRT tree to obtain an optimized RRT tree; Perform bidirectional path planning based on the optimized RRT tree to obtain the original path between the two task points; Performing a deep smoothing operation on the original path to obtain a smoothed path; Performing iterative optimization processing operations on the smooth path to obtain an iteratively optimized path; Obtaining the navigation status according to the iterative optimization path, and adjusting the iterative optimization path according to the navigation status to obtain the navigation optimization path; Obtaining the depth of the starting task point of the original path according to the map to obtain a first depth; Obtain the depth of the end point of the original path according to the map to obtain a second depth; Performing depth smoothing on all path points on the original path starting from the starting task point according to the first depth and the second depth to obtain a third depth for each path point; Retrieving the path point at the third depth according to the map to obtain a first search result; Analyze the first search result and determine whether there is a path point with an obstacle in the first search result; When the first search result indicates that there is no path point with an obstacle, a smooth path is generated according to the first depth, the second depth, and the third depth; Conversely, the depth of all path points on the original path is obtained according to the map to obtain the original depth of each path point; The fourth depth is obtained based on the original depth and the depth of all path points output by the preset depth optimization algorithm; A smooth path is generated based on the first depth, the second depth, and the fourth depth.

2. The path planning method based on the RRT_star algorithm according to claim 1, characterized in that: Optimizing the original RRT tree to obtain an optimized RRT tree includes: Optimizing the step size of the original RRT tree to obtain an optimized primary RRT tree; The node adding method of the primary RRT tree is optimized to obtain an optimized RRT tree.

3. The path planning method based on the RRT_star algorithm according to claim 2, characterized in that: The step size of the original RRT tree is optimized to obtain an optimized primary RRT tree, including: Get a random point on the map to get the first random point; Determine whether there is an obstacle at the first random point based on the map; When there is no obstacle at the first random point, obtaining the direction of the first random point according to the map to obtain a first direction; Obtain the node closest to the first random point from the original RRT tree to obtain node P1; Get the step length from the original RRT tree to get the first step length; Taking node P1 as the starting point, obtain a point on the map according to the first step length and the first direction to obtain the second random point; Determine whether there is an obstacle between node P1 and the second random point based on the map; If there is no obstacle, the process returns to randomly obtain a point on the map until the preset first iteration condition is met, then the first step length is doubled to obtain the optimized primary RRT tree; If there is an obstacle, the process returns to randomly obtain a point on the map until the preset second iteration condition is met. The second step length is halved to obtain the optimized primary RRT tree.

4. The path planning method based on the RRT_star algorithm according to claim 2, characterized in that: The method of optimizing the node addition method of the primary RRT tree to obtain an optimized RRT tree includes: Get a random point on the map to get the third random point; Find the node pnear closest to the third random point from the primary RRT tree; Obtain the direction of the third random point according to the map to obtain the second direction; Get the step length from the primary RRT tree to get the second step length; Take the node pnear as the starting point and obtain the node pnew according to the second step length and the second direction; Perform node analysis on the primary RRT tree through a node optimization algorithm to obtain node analysis results, and perform result analysis on the node analysis results; When the node analysis result shows that there is a primary RRT tree node that meets the preset node conditions, the primary RRT tree node that meets the preset node conditions is set as node pmin, and node pmin is set as the parent node of node pnew to obtain an optimized RRT tree; When the node analysis result shows that there is no primary RRT tree node that meets the preset node conditions, determine whether there is an obstacle between the node pnear and the node pnew; If there is no obstacle between node pnear and node pnew, set node pnear as the parent node of node pnew to obtain the optimized RRT tree; If there is an obstacle between the node pnear and the node pnew, the process returns to randomly obtain a point on the map to obtain the optimized RRT tree.

5. The path planning method based on the RRT_star algorithm according to claim 1, wherein: The bidirectional path planning is performed based on the optimized RRT tree to obtain the original path between the two task points, including: Set any of the two mission points as the first mission point; Obtaining directions of adjacent task points of the first task point based on the first task point and the map to obtain a third direction; Randomly generate a first value according to a preset value range, and generate an optimized angle according to the first value and a third direction; Obtain the distance between the first task point and its adjacent task points according to the map to obtain the adjacent distance; Obtaining a first range according to the adjacent distance and a preset range adjustment condition; randomly generating a second value based on the first range; Get the coordinates of the first mission point according to the map; Generate optimized coordinates according to the coordinates of the first task point, the optimized angle and the optimized distance value; Generate sampling points on the map according to the optimized coordinates and determine whether the sampling points are in the presence of obstacles; When the sampling point is not located at an obstacle, the optimized RRT tree is used to search for an optimized RRT tree node of an adjacent task point within the preset second range. When the optimized RRT tree fails to find the optimized RRT tree node of the adjacent task point, the step length is obtained from the optimized RRT tree to obtain the third step length; Get the direction of the sampling point according to the map; According to the third step length and the direction of the sampling point, the optimized RRT tree is extended to obtain a new node; When the optimized RRT tree finds the optimized RRT tree node of the adjacent task point, its step length is obtained from the optimized RRT tree to obtain the third step length; According to the third step length, the optimized RRT tree is extended to intersect with the optimized RRT tree of the adjacent task point to obtain the original path between the two adjacent task points.

6. The path planning method based on the RRT_star algorithm according to claim 1, wherein: The iterative optimization processing operation is performed on the smooth path to obtain the iteratively optimized path, including: Obtain the path length between two task points on the smooth path according to the map to obtain the initial length; Using a path planning algorithm to obtain an optimized path and the optimized length corresponding to the optimized path; Compare the initial length and the optimized length. If the optimized length is smaller than the initial length, use the optimized length to update the initial length, and return to execute the path planning algorithm to obtain the optimized path. When the preset iteration stop condition is met, the iterative optimization path corresponding to the optimization length is output.

7. The path planning method based on the RRT_star algorithm according to claim 1, wherein: The obtaining of the navigation status according to the iterative optimization path and adjusting the iterative optimization path according to the navigation status to obtain the navigation optimization path includes: Obtain navigation status based on iterative optimization path; Conduct status analysis on navigation status; When the navigation state is that there is an obstacle blocking the way, the navigation optimization path is obtained according to the iterative optimization path adjustment.

8. A path planning device based on the RRT_star algorithm, characterized in that: include: The original RRT tree creation module is used to create the original RRT tree at two adjacent task points on the preset map using the RRT_star algorithm; The original RRT tree optimization module is used to optimize the original RRT tree to obtain an optimized RRT tree; The original path planning module is used to perform bidirectional path planning based on the optimized RRT tree to obtain the original path between two task points; The smooth path generation module is used to perform deep smoothing operations on the original path to obtain a smooth path, specifically including: Obtaining the depth of the starting task point of the original path according to the map to obtain a first depth; Obtain the depth of the end point of the original path according to the map to obtain a second depth; Performing depth smoothing on all path points on the original path starting from the starting task point according to the first depth and the second depth to obtain a third depth for each path point; Retrieving the path point at the third depth according to the map to obtain a first search result; Analyze the first search result and determine whether there is a path point with an obstacle in the first search result; When the first search result indicates that there is no path point with an obstacle, a smooth path is generated according to the first depth, the second depth, and the third depth; Conversely, the depth of all path points on the original path is obtained according to the map to obtain the original depth of each path point; The fourth depth is obtained based on the original depth and the depth of all path points output by the preset depth optimization algorithm; generating a smooth path according to the first depth, the second depth, and the fourth depth; An iterative optimization path generation module is used to perform iterative optimization processing operations on the smooth path to obtain an iterative optimization path; The navigation optimization path generation module is used to obtain the navigation status according to the iterative optimization path, and adjust the iterative optimization path according to the navigation status to obtain the navigation optimization path.

9. A path planning device based on the RRT_star algorithm, characterized in that: The path planning device based on the RRT_star algorithm includes: a memory and at least one processor, wherein the memory stores instructions; At least one of the processors calls the instructions in the memory to enable the path planning device based on the RRT_star algorithm to perform each step of the path planning method based on the RRT_star algorithm as described in any one of claims 1 to 7.

10. A computer-readable storage medium having instructions stored thereon, characterized in that: When the instructions are executed by the processor, the steps of the path planning method based on the RRT_star algorithm as described in any one of claims 1 to 7 are implemented.

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