Mobile robot high environment adaptability path planning method based on IDBVSB-P-RRT

Abstract

The invention discloses a mobile robot high environment adaptability path planning method based on IDBVSB-P-RRT. The mobile robot high environment adaptability path planning method comprises the following steps of S1, a sampling stage; the method comprises the following steps: firstly, defining two extension trees V1 and V2, two extension lists E1 and E2 and a list Xsoln; s2, an expansion stage; s3, a collision detection stage; s4, a connection stage; and S5, performing a pruning strategy. On the basis of the RRT algorithm, the sampling area is optimized in the sampling stage, the search range is limited within an oval range, the accuracy of the sampling search range is higher, the operation cost of sampling, expansion and collision detection is greatly reduced due to the optimized sampling area, and the IDBVSB-P-RRT algorithm has the advantages that the algorithm is simple, the algorithm is convenient to operate, and the reliability is high. The complexity in the path planning process of the mobile robot can be remarkably reduced, and the path planning efficiency is improved.

Description

Technical Field

[0001] This invention relates to the field of robot path planning, and in particular to a highly environmentally adaptable path planning method for mobile robots based on IDBVSB-P-RRT. Background Technology

[0002] With the widespread application of mobile robots in industrial automation, intelligent services, and other fields, path planning technology in complex dynamic environments faces significant challenges. Traditional path planning methods have obvious limitations in terms of environmental adaptability and real-time performance: while the graph search-based A* algorithm can guarantee optimality, its computational complexity increases exponentially with environmental complexity; although the Rapid Expanding Random Tree (RRT) series of algorithms has probabilistic completeness, its random sampling characteristics lead to low planning efficiency and poor performance in complex scenarios such as narrow passages. Especially in application scenarios with high real-time requirements, such as service robots and autonomous driving, existing methods struggle to simultaneously meet the multiple demands of path quality, planning speed, and environmental adaptability.

[0003] Current mainstream improvement schemes can be mainly divided into three categories: First, sampling-based optimization methods, such as RRT, improve path quality through asymptotic optimality, but their convergence speed is slow; second, heuristic methods, such as P-RRT, accelerate convergence through target bias, but are prone to invalid sampling in multi-obstacle environments; and third, hybrid methods, such as Informed-RRT, which significantly improves efficiency by limiting the search space through elliptic sampling domains, but lacks adaptability to dynamic environments. The Bi-RRT algorithm proposed in 2018 adopts a bidirectional expansion strategy, reducing planning time by about 40%, but still suffers from problems such as path tortuosity and poor smoothness. The F-RRT* algorithm, which appeared in 2021, optimizes path quality through force field guidance, but its fixed step size setting leads to insufficient adaptability in complex terrain.

[0004] To improve the environmental perception and adaptability, path planning efficiency, and path quality of mobile robots, this invention first adopts the sampling region optimization concept of the Informed-RRT algorithm, limiting the search range to an elliptical region. This significantly reduces the computational overhead of sampling, expansion, and collision detection. Furthermore, an improved gravitational potential field (APF) algorithm is introduced in the RRT* expansion stage. By incorporating environmental factors around the mobile robot, an adaptive step size strategy is designed, significantly enhancing environmental adaptability and guidance. Therefore, the improved algorithm proposed in this invention enables mobile robots to achieve strong adaptability and ultra-high-speed capabilities in complex environments. Summary of the Invention

[0005] The technical problem to be solved by this invention is: for path planning of mobile robots, the sampling area optimization idea is used to limit the search range to an elliptical area, an adaptive expansion step size is designed in combination with the obstacle situation around the robot, and the gravity calculation strategy is improved to achieve the timeliness, adaptability to complex environments, and guidance of path planning.

[0006] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is: a highly environmentally adaptable path planning method for mobile robots based on IDBVSB-P-RRT, comprising the following steps:

[0007] Step S1: Sampling phase;

[0008] First, define two expansion trees V1 and V2, two expansion lists E1 and E2, and a list Xsoln. V1 is initialized with qstart, V2 with qgoal, E1 and E2 are initially set to 0, and Xsoln stores the path length. Before the algorithm runs, the RRT algorithm is run 100 times, and the average length is used to initialize Xsoln. Next, the robot's path search range is restricted by selecting the smallest path in Xsoln as the major axis of the ellipse sampling region, limiting the search range to an elliptical area. A pre-designed sampling function (DBVSB Sample) is used to sample within the elliptical area, calculating a random point qrand. This sampling function guides the random tree to expand towards the unobstructed area. Finally, the point qnearest, which is closest to the random point qrand, is found in the set of points in the known tree.

[0009] Step S2: Extension phase;

[0010] The predefined IP_steer function is used for expansion to obtain an expansion point qnew. Specifically, this expansion function employs an adaptive step-size expansion strategy, determining the variable step size based on the number and size of obstacles within the elliptical region. The specific adaptive step size can be expressed as:

[0011]

[0012] Where n represents the number of obstacles within the ellipse, R step R represents the step size of the robot moving within the elliptical area. ne and R se R represents the relative number ratio of obstacles within the ellipse and the ratio of obstacle spatial areas, respectively. ne and R se The calculations are as follows:

[0013]

[0014] Where nobse and s obse S represents the number of obstacles and the total area of ​​obstacles within the ellipse, respectively. totale This represents the area within the ellipse.

[0015] Furthermore, the introduction of an improved gravity calculation strategy enables mobile robots to avoid getting stuck in dangerous obstacle zones. Specifically, gravity calculation is expressed as:

[0016]

[0017] Where λ is the gravity coefficient, D(q, q goal D(q, q) is the distance to the target point within the elliptical region. obs ) is the distance to the center of the obstacle within the elliptical region. D obs It is a high-risk area near obstacles within the elliptical region, and also the range of the obstacle's repulsive force.

[0018] Step S3: Collision detection phase;

[0019] Check if qnearest and qnew collide. If they collide, start sampling again. If they don't collide, use the extended point qnew as the center and find a set of neighboring points Qnear within a certain range. Treat each neighboring point in set Qnear as the parent node of qnew. If replacing the parent node results in a shorter path length, replace the parent node of qnew (ChooseParent). Child nodes are also updated in the same way (Reware).

[0020] Step S4: Connection phase;

[0021] Observe whether the expansion point qnew is within the elliptical region. If it is, keep the point; otherwise, discard the point. Use a bidirectional search strategy to determine the distance between the two expansion trees and whether there are obstacles between them. Only when the distance is less than a certain value and there are no obstacles in between, stop the expansion and obtain the path. Otherwise, swap the random trees T1 and T2.

[0022] Step S5: Pruning strategy;

[0023] The random trees T1 and T2 are optimized using an optimization path function, and the target path T, consisting of an expanded list and an expanded tree, is finally obtained.

[0024] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0025] 1. This invention is based on the RRT algorithm and optimizes the sampling area during the sampling stage, so that the search range is limited to an elliptical range, thereby improving the accuracy of the sampling search range.

[0026] 2. The optimized sampling area of ​​this invention greatly reduces the computational cost of sampling, expansion, and collision detection.

[0027] 3. The IDBVSB-P-RRT algorithm proposed in this invention can significantly reduce the complexity of the path planning process for mobile robots and improve the efficiency of path planning. Attached Figure Description

[0028] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings.

[0029] Figure 1 This is a flowchart of the key steps of the IDBVSB-P-RRT algorithm. Detailed Implementation

[0030] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0031] Example 1

[0032] The following details a highly environmentally adaptable path planning method for mobile robots based on IDBVSB-P-RRT, comprising the following steps:

[0033] Step S1: Sampling phase;

[0034] First, define two expansion trees V1 and V2, two expansion lists E1 and E2, and a list Xsoln. V1 is initialized with qstart, V2 with qgoal, and E1 and E2 are initially set to 0. Xsoln stores the path length. Before the algorithm runs, the RRT algorithm is run 100 times, and the average length is used to initialize Xsoln. Next, the robot's path search range is restricted by selecting the smallest path in Xsoln as the major axis of the ellipse sampling region, limiting the search range to an elliptical area. A pre-designed sampling function (DBVSB Sample) is used to sample within the elliptical area, calculating a random point qrand. This sampling function guides the random tree to expand towards the unobstructed area. Finally, the point qnearest, which is closest to the random point qrand, is found in the known tree's point set.

[0035] Step S2: Extension phase;

[0036] The predefined IP_steer function is used for expansion to obtain an expansion point qnew. Specifically, this expansion function employs an adaptive step-size expansion strategy, determining the variable step size based on the number and size of obstacles within the elliptical region. The specific adaptive step size can be expressed as:

[0037]

[0038] Where n represents the number of obstacles within the ellipse, R step R represents the step size of the robot moving within the elliptical area. ne and R se R represents the relative number ratio of obstacles within the ellipse and the ratio of obstacle spatial areas, respectively. ne and R se The calculations are as follows:

[0039]

[0040] Where n obse and s obse S represents the number of obstacles and the total area of ​​obstacles within the ellipse, respectively. totale This represents the area within the ellipse.

[0041] Furthermore, the introduction of an improved gravity calculation strategy enables mobile robots to avoid getting stuck in dangerous obstacle zones. Specifically, gravity calculation is expressed as:

[0042]

[0043] Where λ is the gravity coefficient, D(q, q goal D(q, q) is the distance to the target point within the elliptical region. obs ) is the distance to the center of the obstacle within the elliptical region. D obs It is a high-risk area near obstacles within the elliptical region, and also the range of the obstacle's repulsive force.

[0044] Step S3: Collision detection phase;

[0045] Check if qnearest and qnew collide. If they collide, start sampling again. If they don't collide, use the extended point qnew as the center and find a set of neighboring points Qnear within a certain range. Treat each neighboring point in set Qnear as the parent node of qnew. If replacing the parent node results in a shorter path length, replace the parent node of qnew (ChooseParent). Child nodes are also updated in the same way (Reware).

[0046] Step S4: Connection phase;

[0047] Observe whether the expansion point qnew is within the elliptical region. If it is, keep the point; otherwise, discard the point. Use a bidirectional search strategy to determine the distance between the two expansion trees and whether there are obstacles between them. Only when the distance is less than a certain value and there are no obstacles in between, stop the expansion and obtain the path. Otherwise, swap the random trees T1 and T2.

[0048] Step S5: Pruning strategy;

[0049] The random trees T1 and T2 are optimized using an optimization path function, and the target path T, consisting of an expanded list and an expanded tree, is finally obtained.

[0050] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A highly environmentally adaptable path planning method for mobile robots based on IDBVSB-P-RRT, characterized in that, Includes the following steps: Step S1: Sampling phase; First, define two expansion trees V1 and V2, two expansion lists E1 and E2, and a list Xsoln. V1 is initialized with qstart, V2 with qgoal, and E1 and E2 are initially set to 0. Xsoln stores the path length. Before the algorithm runs, the RRT algorithm is run 100 times, and the average length is used to initialize Xsoln. Next, the robot's path search range is restricted by selecting the smallest path in Xsoln as the major axis of the ellipse sampling region, limiting the search range to an elliptical area. A pre-designed sampling function (DBVSB Sample) is used to sample within the elliptical area, calculating a random point qrand. This sampling function guides the random tree to expand towards the unobstructed area. Finally, the point qnearest, which is closest to the random point qrand, is found in the known tree's point set. Step S2: Extension phase; The predefined IP_steer function is used for expansion to obtain an expansion point qnew. Specifically, this expansion function employs an adaptive step-size expansion strategy, determining the variable step size based on the number and size of obstacles within the elliptical region. The specific adaptive step size can be expressed as: Where n represents the number of obstacles within the ellipse, R step R represents the step size of the robot moving within the elliptical area. ne and R se R represents the relative number ratio of obstacles within the ellipse and the ratio of obstacle spatial areas, respectively. ne and R se The calculations are as follows: Where n obse and s obse S represents the number of obstacles and the total area of ​​obstacles within the ellipse, respectively. total This represents the area within the ellipse. Furthermore, the introduction of an improved gravity calculation strategy enables mobile robots to avoid getting stuck in dangerous obstacle zones. Specifically, gravity calculation is expressed as: Where λ is the gravitational coefficient, D(q, q goal D(q, q) is the distance to the target point within the elliptical region. obs ) is the distance to the center of the obstacle within the elliptical region. D obs It is a high-risk area near obstacles within the elliptical region, and also the range of the obstacle's repulsive force. Step S3: Collision detection phase; Check if qnearest and qnew collide. If they collide, start sampling again. If they don't collide, use the extended point qnew as the center and find a set of neighboring points Qnear within a certain range. Treat each neighboring point in set Qnear as the parent node of qnew. If replacing the parent node results in a shorter path length, replace the parent node of qnew (ChooseParent). Child nodes are also updated in the same way (Reware). Step S4: Connection phase; Observe whether the expansion point qnew is within the elliptical region. If it is, keep the point; otherwise, discard the point. Use a bidirectional search strategy to determine the distance between the two expansion trees and whether there are obstacles between them. Only when the distance is less than a certain value and there are no obstacles in between, stop the expansion and obtain the path. Otherwise, swap the random trees T1 and T2. Step S5: Pruning strategy; The random trees T1 and T2 are optimized using an optimization path function, and the target path T, consisting of an expanded list and an expanded tree, is finally obtained.

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