Path planning method and system based on connecting line type search inflection point

Abstract

The invention provides a path planning method and system based on a connecting line type search inflection point, and the method comprises the following steps: constructing an obstacle map and a target set, and taking a starting point and an ending point as a target point pair to be stored in the target set; connecting the target point pairs, calculating directed distances from the vertexes of the obstacles intersecting with the connecting line to the connecting line, and selecting the vertex with the maximum absolute value on the positive and negative sides of the connecting line as an effective vertex; if no intersecting obstacle exists, deleting the point pair from the target set and adding the point pair into a result set; calculating the weight of an obstacle, determining the obstacle with the maximum weight, calculating the comprehensive cost of effective inflection points of the obstacle, selecting an inflection point with the smaller cost as an optimal inflection point, and updating the target set; and repeating the steps until the target set is empty, and connecting the target point pairs in the result set to obtain a path planning result. The method provided by the invention has good stability and adaptability, is suitable for different types of maps, greatly reduces the total steering frequency and the total steering angle of the path, and reduces the total distance and calculation time.

Description

Technical Field

[0001] This invention belongs to the field of engineering vehicle path planning technology, specifically relating to a path planning method and system based on a linear search for inflection points. Background Technology

[0002] In modern engineering, large-scale engineering vehicles such as loaders, mining trucks, and articulated dump trucks have become indispensable operating equipment in construction engineering, mining, and logistics warehousing. With their large load capacity, high passability, and flexible steering characteristics, they undertake core tasks such as material transportation, site leveling, and ore loading in complex terrain. However, with the continuous expansion of engineering scale and the acceleration of intelligent transformation, the path planning algorithms in traditional assisted driving and autonomous driving technologies are no longer sufficient to meet the actual needs of articulated engineering vehicles in high-density operating scenarios such as narrow mine pits, dynamic stockpiles, and underground tunnels. Frequent turning, detours, and computational delays occur frequently, not only restricting efficiency and energy economy but also directly affecting the stability and safety of vehicle operation, urgently requiring targeted technological innovation.

[0003] Tradition The algorithm and its improved schemes perform excellently in path planning for general mobile robots. Its core logic involves generating discretized polyline paths on a gridded map through a node expansion mechanism to achieve obstacle avoidance. Meanwhile, in recent years, researchers have also attempted to alleviate some problems in path planning for articulated engineering vehicles through post-processing methods, such as using spline curve smoothing and turning point clustering for local optimization. For example, Bézier curve smoothing can reduce the number of turns to some extent, and dynamic programming can reduce the cumulative turning angle.

[0004] But tradition The existing algorithms fundamentally conflict with the motion characteristics of articulated engineering vehicles. Due to their articulated body structure, these vehicles require simultaneous control of the front and rear body angles during steering, resulting in large turning radii, significant inertia, and steering angles limited by the mechanical structure. Existing algorithms generate discretized polyline paths with excessive number of turning points, excessively large total turning angles, and inability to perform calculations correctly in complex and irregular terrain. Furthermore, existing path planning schemes lack adaptation to the specific operational scenarios of engineering vehicles. For example, in narrow passages at construction sites, the sweep space during articulated vehicle steering is not considered, potentially leading to theoretically feasible but practically impassable paths. In inclined transport scenarios at open-pit mines, the distribution of turning points is not optimized for slope changes, leading to power interruptions or even the risk of vehicle slippage. Insufficient optimization of path length results in redundant travel distances. Although post-processing methods exist, these methods only locally optimize the path morphology and cannot fundamentally reconstruct the path topology. Moreover, existing algorithms generally neglect embedded modeling of the kinematic constraints of engineering vehicles, causing the planning results to be disconnected from the actual vehicle handling capabilities. Summary of the Invention

[0005] This invention proposes a path planning method and system based on connecting inflection points, which solves the problem that paths generated by existing technologies cannot be adapted to engineering vehicle operation scenarios.

[0006] To address the aforementioned technical problems, this invention provides a path planning method based on connecting inflection points, comprising the following steps:

[0007] Step S1: Obtain the vertex coordinates of each obstacle in the path map, construct an obstacle map based on the vertex coordinates of the obstacles, construct a target set, and store the starting point and ending point of the path planning as target point pairs in the target set;

[0008] Step S2: Connect all pairs of target points in the target set. For each obstacle whose line intersects with a target point: calculate the directed distance from all vertices of the obstacle to the line connecting the target point. Select the vertex with the largest absolute value of the directed distance on both the positive and negative sides of the line connecting the target point as the valid vertex of the obstacle; if a target point line... It does not intersect with any obstacle, The corresponding target point pairs are removed from the target set and added to the result set;

[0009] Step S3: Use the sum of the distances from the two valid vertices to the target point as the weight of the obstacle, and take the obstacle with the highest weight as the maximum weight obstacle. Calculate the comprehensive cost of the two valid inflection points of the maximum weight obstacle, select the valid inflection point with the smaller comprehensive cost as the optimal valid inflection point, and take the optimal valid inflection point as the intermediate point to update the target point pairs in the target set.

[0010] Step S4: Repeat steps S2 to S3 until the target set is empty. Connect the target point pairs in the result set to obtain the path planning result.

[0011] Preferably, the expression for calculating the combined cost of the two effective inflection points of the obstacle with the maximum weight in step S3 is as follows:

[0012] ;

[0013] ;

[0014] In the above formula, , These are the combined costs of effective inflection points A and B, respectively. , These are the distances from the lines connecting points A and B to the target point, respectively. , These represent the number of obstacles that intersect the line connecting points A and B to the new target point after taking points A and B as the optimal effective inflection points; The length of the line connecting the target points; The number of obstacles that intersect the line connecting to the target point; This is the distance deviation influencing factor.

[0015] Preferably, after obtaining the path planning result in step S4, the path planning result is locally optimized, including the following steps:

[0016] Step S41: Obtain the ordered set of result points corresponding to the result set;

[0017] Step S42: Traverse the ordered set of result points starting from the starting point. If the starting point and non-adjacent points are... If the line connecting the points does not intersect any obstacle, then delete the line connecting the starting point and the point where the line originates. All points between, from Continue traversing the ordered set of result points starting from the next point;

[0018] Step S43: Repeat step S42 until the entire ordered result point set is traversed, and connect all points in the ordered result point set in turn to obtain the locally optimized path planning result.

[0019] Preferably, the expression for calculating the directed distances from all vertices of the obstacle to the target point in step S2 is:

[0020] ;

[0021] In the formula, for The directed distance of the line connecting to the target point; , Here are the coordinates of the two target points; Let i be the coordinates of the i-th vertex of the n-th obstacle that intersects the line connecting it to the target point.

[0022] The present invention also provides a path planning system based on the inflection point of the linear search, which is based on the above-mentioned path planning method based on the inflection point of the linear search and includes: an initialization module, a dataset update module and a path generation module;

[0023] The initialization module: obtains the coordinates of the starting point, the ending point, and the vertex coordinates of all obstacles, constructs an obstacle map, initializes the target set as {(starting point, ending point)}, and initializes the result set;

[0024] The dataset update module iterates through each pair of target points in the target set. For each pair of target points, it checks whether the line connecting the target points intersects with any obstacle. If there is no intersection, the pair of points is removed from the target set and added to the result set. If there is an intersection, it calculates the directed distances from all vertices of each intersecting obstacle to the line connecting the target point. The vertex with the largest absolute value of the directed distance on both the positive and negative distance sides is selected as the valid vertex. The effective inflection point with the smaller overall cost is selected as the optimal effective inflection point, and the optimal effective inflection point is added to the target set.

[0025] The path generation module updates the target set and result set using the dataset update module until the target set is empty, then connects the point pairs in the result set to form a continuous path.

[0026] Preferably, the system further includes a local optimization module, which: starting from the starting point of the continuous path, sequentially checks the connections between non-adjacent points; if the connection does not intersect with any obstacle, then deletes all points between the starting point and non-adjacent points, and continues to check the intersection of the connections between non-adjacent points and obstacles starting from the next point that has not been deleted, until the entire path has been traversed.

[0027] Preferably, in the dataset update module, the expression for calculating the overall cost is:

[0028] ;

[0029] ;

[0030] In the above formula, , These are the combined costs of effective inflection points A and B, respectively. , These are the distances from the lines connecting points A and B to the target point, respectively. , These represent the number of obstacles that intersect the line connecting points A and B to the new target point after taking points A and B as the optimal effective inflection points; The length of the line connecting the target points; The number of obstacles that intersect the line connecting to the target point; This is the distance deviation influencing factor.

[0031] Preferably, the system further includes a debugging module, which adjusts the deviation influence factor based on the number of obstacles in the map, the map area, and planning preferences. The size should be increased when obstacles are dense, numerous, and occupy a large area of ​​the map. When obstacles are sparse, few in number, and occupy a small area of ​​the map, adjust the size. When the planning preference is to optimize the number of path turns, increase the value. When the planning preference is to optimize the total path distance, reduce the value. .

[0032] The present invention also provides an electronic device, comprising: a memory, a processor, and a computer program, wherein the computer program is stored in the memory and configured to be executed by the processor to implement the above-described path planning method based on a linear search inflection point.

[0033] The present invention further provides a computer-readable storage medium storing a computer program, which is executed by a processor to implement the above-described path planning method based on a linear search inflection point.

[0034] The beneficial effects of the present invention include at least the following:

[0035] 1. By obtaining the vertex coordinates of the obstacles, an obstacle map is constructed. The vertex with the largest absolute value of the directed distance on both the positive and negative sides of the line connecting the target points is selected as the effective vertex of the obstacle. This allows the planned path to fit the boundary of the obstacle more closely, avoiding the problem that the path may be too conservative in traditional algorithms.

[0036] 2. By selecting the optimal effective inflection point as the intermediate point and continuously updating the target point pairs in the target set, the path can be made smoother, the number of turning points and the total turning angle in the path can be greatly reduced, the load on the steering mechanism of the engineering vehicle can be reduced, tire wear can be reduced, and the stability and safety of the vehicle can be improved. Attached Figure Description

[0037] Figure 1 This is a schematic diagram of the method flow according to an embodiment of the present invention;

[0038] Figure 2 This is a schematic diagram of a grid map according to an embodiment of the present invention;

[0039] Figure 3 As described in the embodiments of the present invention Diagram showing the intersection with obstacles;

[0040] Figure 4 This is the second-level connection in the embodiment of the present invention. , Diagram showing the intersection with obstacles;

[0041] Figure 5 This is a schematic diagram illustrating the process of finding the next level connection in an embodiment of the present invention;

[0042] Figure 6 This is a schematic diagram of the final path generated in an embodiment of the present invention;

[0043] Figure 7 This is a performance diagram of the path planning algorithm according to an embodiment of the present invention;

[0044] Figure 8 For tradition Performance diagram of the algorithm;

[0045] Figure 9 To improve Performance diagram of the algorithm;

[0046] Figure 10 This is a schematic diagram illustrating the implementation effect of the path planning algorithm of this invention under various working conditions;

[0047] Figure 11 This is a comparison chart of the planning results of the method of this embodiment and various path planning methods;

[0048] Figure 12 This is a schematic diagram of the system structure according to an embodiment of the present invention. Detailed Implementation

[0049] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the protection scope of the present invention.

[0050] like Figure 1 As shown, this embodiment of the invention provides a path planning method based on start-end turning points (SETL, Start-End Turningpoints Linebased Path Algorithm), which includes the following steps:

[0051] Step S1: Obtain the vertex coordinates of each obstacle in the path map, construct an obstacle map based on the vertex coordinates of the obstacles, construct a target set, and store the starting point and ending point of the path planning as target point pairs in the target set.

[0052] Specifically, the first step is to determine the target points for path planning. There can be multiple target points; in this embodiment of the invention, the starting point and the ending point are used as the two target points for the first-level connection. As a starting point As the endpoint, the constructed target point set is .

[0053] Step S2: Connect all pairs of target points in the target set. For each obstacle whose line intersects with a target point: calculate the directed distance from all vertices of the obstacle to the line connecting the target point. Select the vertex with the largest absolute value of the directed distance on both the positive and negative sides of the line connecting the target point as the valid vertex of the obstacle; if a target point line... It does not intersect with any obstacle, The corresponding target point pairs are removed from the target set and added to the result set.

[0054] Specifically, connection and , obtain line segment Confirm with line segment Intersecting obstacles, and find each obstacle on the line segment. The two vertices that are furthest apart on both sides are considered the effective inflection points of the obstacle.

[0055] , They are the starting points and the end point The coordinates are then used to obtain the line segment. The equation:

[0056] .

[0057] Connect the nth element with The coordinates of the i-th vertex of the intersecting obstacles are: The vertex to The distance is .calculate The expression is:

[0058] .

[0059] Place each obstacle in The two vertices with the largest distance between them are considered as valid inflection points.

[0060] Step S3: Use the sum of the distances from the two valid vertices to the target point as the weight of the obstacle, and take the obstacle with the highest weight as the maximum weight obstacle. Calculate the comprehensive cost of the two valid inflection points of the maximum weight obstacle, select the valid inflection point with the smaller comprehensive cost as the optimal valid inflection point, and take the optimal valid inflection point as the intermediate point to update the target point pairs in the target set.

[0061] Specifically, calculate the two valid vertices to The sum of distances is used as the weight of the obstacle, and the obstacle with the highest weight is selected as the... For the obstacle with the highest weight, calculate the comprehensive cost of the two effective inflection points, and select the effective inflection point with the smaller comprehensive cost as the first optimal effective inflection point. Update the target point set to .

[0062] In this embodiment of the invention, when selecting the optimal effective inflection point, the two effective inflection points of the obstacle with the highest weight at that level are selected. If only the effective inflection point with the smaller distance is used as the optimal effective inflection point, the selected optimal effective inflection point may not be the global optimal, resulting in the features of the next level connection between the two effective inflection points and the obstacle not being used well.

[0063] Based on this characteristic, the embodiments of the present invention additionally introduce a distance deviation influence factor. ,pass Comprehensive cost The calculation is made using a non-linear approach. Distance deviation influence factor. The distance deviation referred to in the text is the distance deviation between the two effective inflection points of the highest-weight obstacle in that level of connection, i.e. When the distance deviation is less than a certain percentage of the length of the cascade, right The influence weight of the value will decrease, at which point it is even more necessary to The number of obstacles that intersect when used as the target point for the next level of connection. The influence of distance deviation on the length of the connecting line is used to determine which point, A or B, is better. When the distance deviation exceeds a certain percentage of the line length, right The influence weight of the value will increase, at which point the number of intersections between the level connection and obstacles will be added. Thus dilute The number of obstacles that intersect when used as the target point for the next level of connection. The influence of distance deviation. This proportion is the distance deviation influence factor. By introducing this distance deviation influence factor This allows for a more accurate selection of the optimal effective inflection point, resulting in the optimal overall path planning, i.e., the optimal combination of path length and turning conditions.

[0064] Specifically, calculation and The two valid inflection points of each intersecting obstacle to distance and ,Will The corresponding obstacles are The maximum weighted obstacle. Calculate the combined cost of the two valid inflection points of the maximum weighted obstacle, denoted as points A and B respectively, and calculate their combined costs. :

[0065] ;

[0066] ;

[0067] In the above formula, , The combined costs for points A and B are respectively; , From point A and point B respectively The distance; , After taking points A and B as the next level target points respectively, and... , The number of obstacles that the lines connecting them intersect; For the current line segment Length; To and The number of intersecting obstacles; Distance deviation is an influencing factor. The larger the value, the fewer the total steering angle at the turning point, and the longer the relative distance traveled. The smaller the value, the shorter the distance, and the greater the total turning angle relative to the turning point.

[0068] Comparing the overall costs, the optimal effective inflection point for this level is selected as follows: :

[0069] .

[0070] Distance Deviation Influence Factor The adjustment can be made according to the working conditions. When the obstacles are dense, numerous, and occupy a large area of ​​the map, the adjustment can be made appropriately. to Left and right; when obstacles are sparse, few in number, and occupy a small area of ​​the map, the size can be adjusted appropriately. to Left and right. It can also be adjusted according to the planning preference for turning; when the planning preference is turning, it can be increased appropriately. The planning preference is for shorter distances, so the distance can be adjusted accordingly. Based on the analysis of a large amount of experimental data, the distance deviation influence factor... At that time, the overall performance indicators were optimal.

[0071] Step S4: Repeat steps S2 to S3 until the target set is empty. Connect the target point pairs in the result set to obtain the path planning result.

[0072] Specifically, after adding the optimal effective inflection point of each level of connection to the target point set, the next level of connection is made, and the search is carried out progressively downwards, that is, steps S3-S5 are repeated level by level. If the connection of a pair of target points does not intersect with any obstacle, the pair of target points is deleted from the target point set, and the target point is stored in the result set, until the target point set in step S1 is empty, that is, there are no paired target points that need to be searched for corresponding optimal effective inflection points, that is, all results are found. A preliminary path before local optimization is generated based on the vertices in the result set.

[0073] Step S7: Starting from the starting point, check all vertices in the preliminary path that are not adjacent to the starting point. If the line connecting the starting point and a certain vertex does not intersect any obstacle, delete all intermediate points between the starting point and that vertex, and keep the line as a new path to optimize the preliminary path.

[0074] Specifically, the initial path is locally optimized. Starting from the starting point, each vertex is connected sequentially. If the line connecting a vertex to a non-adjacent vertex does not intersect any obstacle, then all vertices between the vertex and that non-adjacent vertex are deleted, and the connection is retained as the new path.

[0075] For example, if the initial path without local optimization is:

[0076] .

[0077] Get the result set .from Begin by connecting to all subsequent non-adjacent vertices in sequence, i.e., connecting to all vertices except... Connect the vertices in sequence. If a certain connection, for example... Clear if it does not intersect with any obstacles. and All optimal effective inflection points between, i.e. Then, starting from the next point that has not been deleted, continue connecting it to all subsequent vertices except for adjacent ones. Delete vertices that satisfy the above conditions to obtain the locally optimized result set. Connect each line segment in the result set to obtain the final locally optimized path:

[0078] .

[0079] Complete the path planning based on the path planning method of inflection points in a linear search.

[0080] Example 1

[0081] For example Figure 2 As shown, the size is Taking a grid map G as an example, firstly, the vertex coordinates of the grid map G are extracted to obtain the obstacle coordinate system map:

[0082] .

[0083] The current target point set is ,in , obtain the first level connection The equation:

[0084] ;

[0085] ;

[0086] .

[0087] like Figure 3 As shown, the connection Given a map with 9 intersecting obstacles in its coordinate system, calculate the distance from the vertex of each obstacle to the line connecting them. :

[0088] .

[0089] Find the two vertices with the largest distance between each obstacle on both sides of the line connecting them as valid inflection points, and calculate the sum of the distances between these two valid inflection points. ,turn up The corresponding obstacle is designated as the highest-weighted obstacle for that connection. In this embodiment, the highest-weighted obstacle is... Figure 3 The 7th obstacle in the middle, its two valid inflection points are: .

[0090] set up Calculate the combined cost of these two effective inflection points. .current , , , , Substituting into the formula for calculating the overall cost, we get Therefore, choose As the first-level optimal effective inflection point .

[0091] The first-level optimal effective inflection point Store the target point set to obtain Repeat the steps described above to find the optimal effective inflection point to obtain the second-level connection. , .like Figure 4 As shown, the second-level optimal effective inflection point was found. And continue to connect the lines downwards to search, such as Figure 5 As shown.

[0092] If the line connecting a pair of target points does not intersect any obstacle, then that target point is deleted from the target point set and stored in the result set. This process continues until the target point set is empty, meaning there are no more paired target points requiring a search for the corresponding optimal effective inflection point; that is, all results are found, and the result set is obtained, which represents the preliminary path before local optimization.

[0093] The initial path undergoes local optimization. Starting from the starting point, lines are connected to each vertex sequentially. If a line connecting the starting point to a non-adjacent vertex does not intersect any obstacle, all vertices between the connecting points are deleted, and the connecting line is retained as the new path. In this embodiment, there are no paths requiring local optimization, so the initial path before local optimization is directly used as the final path. Figure 6 The image shows the final path after the entire path planning is completed.

[0094] Path planning algorithms are among the most commonly used heuristic search algorithms in path planning. Their core idea is to select the optimal path by comprehensively considering the actual cost g(n) of the nodes and the heuristically estimated cost h(n). The core formula of the path planning algorithm is f(n) = g(n) + h(n), where g(n) represents the actual cost from the starting point to the current node, and h(n) represents the estimated cost from the current node to the target node. The path planning algorithm manages nodes by maintaining OPEN and CLOSE tables, prioritizing the expansion of nodes with the smallest f(n) value until the target node is found or the OPEN table is empty. Subsequent researchers have further explored... Path planning algorithms have proposed various optimization methods. For example, bidirectional... The algorithm searches simultaneously from both the starting and ending points, significantly reducing the search range and improving efficiency. The dynamic weighting method uses larger heuristic weights in the early stages of the search to quickly approach the target, then reduces the weights later to accurately find the optimal path. Furthermore, the jump-point search technique detects obstructions on the straight path and skips these points directly, thereby reducing the search space and improving search efficiency.

[0095] The path planning method of this invention is compared with the traditional... Algorithm and Improvement The algorithm compares the results and obtains the following: Figures 7-9 The path planning results. For example... Figure 7 As shown, in this embodiment, the total path length is 60.2454, the number of turns is 13, the total turning angle is 451.252, and the planning time is 0.0181s. Figure 8 As shown, using traditional The algorithm yielded a total path length of 63.9411, 26 turns, a total turning angle of 1170 degrees, and a planning time of 0.0786 seconds. Figure 9 As shown, using the improved The algorithm yielded a total path length of 68.7668, 14 turns, a total turning angle of 657.0598, and a planning time of 0.100168 seconds. The method of this embodiment differs from traditional methods. Algorithm and Improvements Compared to other algorithms, this method optimizes path routing using obstacle vertex features, reducing the total distance by 8%-15%, especially in situations with dead-end obstacles, avoiding redundant detours. The smoother steering angles improve path continuity, reducing the total steering angle by 50%-70%, resulting in more stable vehicle operation and optimized energy consumption. In vector maps such as CAD architectural floor plans, the planning success rate of the method in this embodiment is higher than... The algorithm is 32% faster, and the computation time is not affected by map type. The rasterization process of the algorithm will result in an additional 15%-20% time overhead. By dynamically selecting the obstacle with the highest weight and the optimal inflection point, the method of this embodiment significantly improves the avoidance efficiency of U-shaped and maze-shaped obstacles. In simulation tests, the method of this embodiment achieved a first-time planning success rate of up to 98% for dead-end obstacles, while... The algorithm's success rate is only 78% due to backtracking from invalid nodes.

[0096] like Figure 10 The diagram shows the path planning results of the method of this invention under various working conditions. It can be seen that the method of this invention exhibits superior planning performance under various working conditions, indicating that the method of this invention is adaptable to path planning under various complex working conditions. Specifically, the method of this invention utilizes innovative mechanisms such as steering cost function reconstruction, dynamic weighting of obstacle vertices, and continuous space search to simultaneously optimize the number of steering points, total steering angle, path length, and computational efficiency during the path generation stage. By reducing the number of steering points and the total steering angle, the stability of the engineering vehicle can be significantly improved: Firstly, the number of steering points is reduced by more than 50%, reducing the need for frequent vehicle attitude adjustments, thereby avoiding lateral swaying caused by centroid shift; secondly, the total steering angle is compressed to the 400-600° range, reducing the inertial impact and hydraulic system pressure fluctuations during a single steering maneuver, ensuring a smooth and controllable steering process; thirdly, combined with continuous curvature path design, it further suppresses vehicle body vibration and tire slippage. Data shows that loaders using this method experience a 35%-45% reduction in lateral acceleration fluctuations and a 60% decrease in the incidence of abnormal vibrations in the steering mechanism in typical mining scenarios. Simultaneously, the increased handling tolerance due to improved stability reduces the frequency of emergency braking by over 40%. This breakthrough not only alleviates efficiency losses and mechanical wear caused by frequent steering in construction vehicles but also provides key technological support for new operational models such as reduced manpower in mines and zero emissions on construction sites by shortening paths and responding to dynamic environments in real time. This accelerates the evolution of the construction machinery industry towards intelligence and sustainability.

[0097] like Figure 11The figure shows a comparison of the planning results of the method of this invention and various path planning methods. GA represents the Genetic Algorithm, SSA the Sparrow Search Algorithm, PSO the Particle Swarm Optimization Algorithm, GWO the Grey Wolf Algorithm, AO the Eagle Algorithm, IAO the Information Acquisition Algorithm, and DBO the Dung Beetle Algorithm. As can be seen from the figure, the method of this invention, by replacing node-by-node expansion with a linear search, significantly reduces invalid computation. In a grid map of the same size, the average planning time and the number of path turning points of the method of this invention are significantly reduced, and the efficiency advantage is more pronounced the larger the map.

[0098] like Figure 12 The diagram shows a structural schematic of a path planning system based on a linear search inflection point according to an embodiment of the present invention, including: an initialization module, a dataset update module, a path generation module, a local optimization module, and a debugging module.

[0099] Initialization module: Obtain the coordinates of the start point, the end point, and the vertex coordinates of all obstacles, construct an obstacle map, initialize the target set as {(start point, end point)}, and initialize the result set.

[0100] Dataset update module: Iterate through each pair of target points in the target set. For each pair of target points, check whether the line connecting the target points intersects with any obstacle. If there is no intersection, remove the pair of points from the target set and add it to the result set. If there is an intersection, calculate the directed distance from all vertices of each intersecting obstacle to the line connecting the target point. Select the vertex with the largest absolute value of the directed distance on both the positive and negative distance sides as the valid vertex. Select the valid inflection point with the smaller overall cost as the optimal valid inflection point and add the optimal valid inflection point to the target set.

[0101] Path generation module: Updates the target set and result set using the dataset update module until the target set is empty, then connects the point pairs in the result set to form a continuous path.

[0102] Local optimization module: Starting from the beginning of the continuous path, check the connection between the point and the non-adjacent point in turn. If the connection does not intersect with any obstacle, delete all points between the starting point and the non-adjacent point. Start from the next point that has not been deleted and continue to check the intersection of the connection between the non-adjacent point and the obstacle, until the entire path is traversed.

[0103] Debugging module: Adjusts deviation influencing factors based on the number of obstacles in the map, map area, and planning preferences. The size should be increased when obstacles are dense, numerous, and occupy a large area of ​​the map. When obstacles are sparse, few in number, and occupy a small area of ​​the map, adjust the size. When the planning preference is to optimize the number of path turns, increase the value. When the planning preference is to optimize the total path distance, reduce the value. .

[0104] This invention also provides an electronic device, including: a memory, a processor, and a computer program, wherein the computer program is stored in the memory and configured to be executed by the processor to implement the above-described path planning method based on a connection-based search inflection point.

[0105] This invention also provides a computer-readable storage medium storing a computer program, which is executed by a processor to implement the above-described path planning method based on a connection-based search for inflection points.

[0106] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described; only preferred embodiments of the present invention are illustrated. The descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the present invention. As long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification.

[0107] It should be noted that those skilled in the art can make various modifications and improvements without departing from the inventive concept, and these all fall within the scope of protection of this invention. Therefore, the scope of protection of this invention should be determined by the appended claims.

Claims

1. A path planning method based on connecting inflection points, characterized in that, Includes the following steps: Step S1: Obtain the vertex coordinates of each obstacle in the path map, construct an obstacle map based on the vertex coordinates of the obstacles, construct a target set, and store the starting point and ending point of the path planning as target point pairs in the target set; Step S2: Connect all pairs of target points in the target set. For each obstacle that intersects with the line connecting the target point: calculate the directed distance from all vertices of the obstacle to the line connecting the target point. Select the vertex with the largest absolute value of the directed distance on the positive and negative distance sides of the line connecting the target point as the valid vertex of the obstacle. If a target point is connected by a line It does not intersect with any obstacle, The corresponding target point pairs are removed from the target set and added to the result set; Step S3: Use the sum of the distances from the two valid vertices to the target point as the weight of the obstacle, and take the obstacle with the highest weight as the maximum weight obstacle. Calculate the comprehensive cost of the two valid inflection points of the maximum weight obstacle, select the valid inflection point with the smaller comprehensive cost as the optimal valid inflection point, and take the optimal valid inflection point as the intermediate point to update the target point pairs in the target set. Step S4: Repeat steps S2 to S3 until the target set is empty. Connect the target point pairs in the result set to obtain the path planning result.

2. The path planning method based on connecting inflection points according to claim 1, characterized in that: The expression for calculating the combined cost of the two effective inflection points of the maximum weight obstacle in step S3 is as follows: ； ； In the above formula, , These are the combined costs of effective inflection points A and B, respectively. , These are the distances from the lines connecting points A and B to the target point, respectively. , These represent the number of obstacles that intersect the line connecting points A and B to the new target point after taking points A and B as the optimal effective inflection points; The length of the line connecting the target points; The number of obstacles that intersect the line connecting to the target point; This is the distance deviation influencing factor.

3. The path planning method based on connecting inflection points according to claim 1, characterized in that: After obtaining the path planning result in step S4, the path planning result is locally optimized, including the following steps: Step S41: Obtain the ordered set of result points corresponding to the result set; Step S42: Traverse the ordered set of result points starting from the starting point. If the starting point and non-adjacent points are... If the line connecting the points does not intersect any obstacle, then delete the line connecting the starting point and the point where the line originates. All points between, from Continue traversing the ordered set of result points starting from the next point; Step S43: Repeat step S42 until the entire ordered result point set is traversed, and connect all points in the ordered result point set in turn to obtain the locally optimized path planning result.

4. The path planning method based on connecting inflection points according to claim 1, characterized in that: The expression for calculating the directed distances from all vertices of the obstacle to the target point in step S2 is: ； In the formula, for The directed distance of the line connecting to the target point; , Here are the coordinates of the two target points; Let i be the coordinates of the i-th vertex of the n-th obstacle that intersects the line connecting it to the target point.

5. A path planning system based on connecting search inflection points, implemented based on the path planning method based on connecting search inflection points as described in any one of claims 1 to 4, characterized in that, include: Initialization module, dataset update module, and path generation module; The initialization module: obtains the coordinates of the starting point, the ending point, and the vertex coordinates of all obstacles, constructs an obstacle map, initializes the target set as {(starting point, ending point)}, and initializes the result set; The dataset update module iterates through each pair of target points in the target set. For each pair of target points, it checks whether the line connecting the target points intersects with any obstacle. If there is no intersection, the pair of points is removed from the target set and added to the result set. If they intersect, calculate the directed distances from all vertices of each intersecting obstacle to the target point. Select the vertex with the largest absolute value of the directed distance on both the positive and negative distance sides as the effective vertex. Select the effective inflection point with the smaller overall cost as the optimal effective inflection point and add the optimal effective inflection point to the target set. The path generation module updates the target set and result set using the dataset update module until the target set is empty, then connects the point pairs in the result set to form a continuous path.

6. A path planning system based on connecting search inflection points according to claim 5, characterized in that: The system also includes a local optimization module, which: starting from the starting point of the continuous path, sequentially checks the connection between the non-adjacent points and the obstacles. If the connection does not intersect with any obstacle, all points between the starting point and the non-adjacent points are deleted. Starting from the next point that has not been deleted, the system continues to check the intersection of the connection between the non-adjacent points and the obstacles until the entire path has been traversed.

7. A path planning system based on connecting search inflection points according to claim 5, characterized in that: In the dataset update module, the expression for calculating the overall cost is: ； ； In the above formula, , These are the combined costs of effective inflection points A and B, respectively. , These are the distances from the lines connecting points A and B to the target point, respectively. , These represent the number of obstacles that intersect the line connecting points A and B to the new target point after taking points A and B as the optimal effective inflection points; The length of the line connecting the target points; The number of obstacles that intersect the line connecting to the target point; This is the distance deviation influencing factor.

8. A path planning system based on connecting inflection points according to claim 7, characterized in that: The system also includes a debugging module, which adjusts the deviation influencing factor based on the number of obstacles in the map, the map area, and planning preferences. The size should be increased when obstacles are dense, numerous, and occupy a large area of ​​the map. When obstacles are sparse, few in number, and occupy a small area of ​​the map, adjust the size. ; When the planning preference is to optimize the number of path turns, increase the adjustment. ; When the planning preference is to optimize the total path distance, reduce the adjustment. .

9. An electronic device, comprising: The memory, processor, and computer program are characterized in that: the computer program is stored in the memory and configured to be executed by the processor to implement the path planning method based on the search of inflection points according to any one of claims 1 to 4.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program that is executed by a processor to implement the path planning method based on a linear search inflection point as described in any one of claims 1 to 4.

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