Improved local path planning method suitable for multi-robot inspection task

Abstract

The invention provides an improved local path planning method suitable for a multi-robot inspection task, and belongs to the technical field of robot path planning. The limitation of path planning during cooperative work of multiple robots in a traditional DWA algorithm is solved; the method is based on a dynamic window method DWA and introduces relative velocity obstacle RVO soft constraint, and comprises the following steps: generating a dynamic window and candidate tracks based on the dynamic window method DWA; constructing an RVO relative speed obstacle area: acquiring the relative speed and position between the robot and other robots in real time, and constructing a relative speed obstacle area for representing a potential collision speed area; calculating a multi-target evaluation function: comprehensively scoring the candidate tracks by adopting course angles, speeds, distances and safety; path selection: selecting the candidate trajectory with the highest score as the optimal path according to the score of the multi-target evaluation function, so that the robot safely and efficiently approaches the target point; the method is suitable for multi-robot cooperative obstacle avoidance.

Description

Technical Field

[0001] The invention provides an improved local path planning method suitable for multi-robot inspection tasks, belonging to the technical field of robot path planning. Background Art

[0002] In the field of mobile robots, path planning is an important part of the robot's autonomous navigation and obstacle avoidance. Among various path planning algorithms, the Dynamic Window Approach (DWA) has become the choice of many real-time navigation systems because of its ability to quickly respond to obstacles and low computational cost. The DWA algorithm constructs a dynamic window based on the robot's current motion characteristics such as speed and acceleration, discretizes the dynamic window into a combination of linear velocity and angular velocity, and predicts candidate paths within a given time range. Finally, the optimal path is selected through an evaluation function. However, the DWA algorithm has the following problems in dynamic environments, especially in complex scenarios of multi-robot collaborative operations.

[0003] The traditional DWA algorithm is mainly based on local environmental perception and does not consider the movement trends of other robots at the same time. When multiple robots perform tasks in the same field, such as simultaneous inspection tasks, there is a greater possibility of interaction between robots. Since the DWA algorithm only plans in a local range and lacks the ability to predict relative speed and relative position, when multiple robots move close to the same position or at the intersection of paths, they are prone to sudden turns or collisions. In addition, during execution, the DWA algorithm is usually more sensitive to static obstacles and has relatively weak dynamic avoidance capabilities for other mobile robots.

[0004] In order to improve the dynamic obstacle avoidance performance of the DWA algorithm in a dynamic environment and adapt to the needs of multi-robot collaborative work, relative velocity obstacle (RVO) has become an effective strategy. RVO provides a more forward-looking obstacle avoidance mechanism for robots by calculating the relative velocity obstacle area (i.e., predicting the potential collision velocity range based on the relative speed and position with other robots). RVO is widely used in obstacle avoidance and collaboration in high-density and multi-mobile scenes, and can better predict and avoid collision risks. Summary of the invention

[0005] In order to solve the limitations of the traditional DWA algorithm in path planning when multiple robots work together, the present invention proposes an improved local path planning method suitable for multi-robot inspection tasks. By introducing RVO soft constraints, it is made more adaptable to the dynamic obstacle avoidance requirements in multi-robot collaborative work.

[0006] The technical solution adopted by the present invention is: an improved local path planning method suitable for multi-robot inspection tasks, based on the dynamic window method DWA and introducing the relative speed obstacle RVO soft constraint, comprising the following steps:

[0007] Step 1: Confirm the current position coordinates and target point coordinates of each robot, plan the global optimal path, set the maximum speed and acceleration range of each robot, start the robot to scan and record the surrounding environment information;

[0008] Step 2: Generate dynamic windows and candidate trajectories based on the dynamic window method DWA;

[0009] Step 3: RVO relative speed obstacle zone construction: Obtain the relative speed and position between the robot and other robots in real time, and construct a relative speed obstacle zone to represent the potential collision speed area;

[0010] Step 4: Calculation of multi-objective evaluation function: Comprehensively score the candidate trajectories using heading angle, speed, distance and safety;

[0011] Step 5: Path selection: Based on the score of the multi-objective evaluation function, select the candidate trajectory with the highest score as the best path to enable the robot to approach the target point safely and efficiently.

[0012] Furthermore, the dynamic window in step 2 is constructed based on the robot's maximum speed, maximum acceleration, current speed, and the nearest obstacle position limit to determine the range of linear velocity and angular velocity allowed for the robot at the next moment, so that the dynamic window adapts to environmental changes and satisfies constraints.

[0013] Furthermore, the candidate trajectory generation step in step 2 is: generating multiple discrete linear velocity and angular velocity combinations within the dynamic window range, and simulating different trajectory effects according to the above combinations, so as to ensure that the robot can flexibly select a safe and efficient path in a multi-robot scenario; wherein the starting point of each trajectory is the current position of the robot;

[0014] Furthermore, the candidate trajectory includes multiple path options from the current position of the robot to the next position.

[0015] Furthermore, the steps of constructing the RVO relative velocity obstacle zone in step 3 are as follows:

[0016] (1) Relative speed and position calculation: Calculate the relative speed and position of each robot with respect to other robots around it in real time, and establish the corresponding relative speed vector;

[0017] (2) RVO relative speed obstacle zone construction: Based on the relative speed and position, a virtual RVO speed obstacle avoidance zone that can change dynamically is calculated and generated. The RVO speed obstacle avoidance zone represents the potential speed range in which the robot may collide with other robots in the future.

[0018] The RVO relative speed obstacle zone will be adjusted in real time according to the relative speed, robot spacing and relative direction. When the robot approaches other robots, the RVO relative speed obstacle zone will expand to enhance the obstacle avoidance effect.

[0019] Furthermore, the rules for heading angle, speed, distance and safety score in step 4 are as follows:

[0020] (1) Heading angle score: calculated based on the angle between the positive direction of the candidate trajectory end and the target point. The smaller the angle, the higher the score.

[0021] (2) Speed ​​score: calculated based on the speed of the candidate trajectory. The faster the speed, the higher the score.

[0022] (3) Distance score: calculated based on the distance between the candidate trajectory and the nearest obstacle. The greater the distance, the higher the score.

[0023] (4) Safety score: If the speed of a candidate trajectory is within the RVO relative speed obstacle zone, the score of the candidate trajectory is reduced to encourage the robot to avoid other robots.

[0024] Furthermore, when performing path selection in step 5, when the path with the highest score has a collision risk, the path is replanned by recalculating the candidate trajectories within the dynamic window to ensure the safety of the path and the continuity of the task.

[0025] Furthermore, it also includes feedback control of path execution. According to the best path selected by the multi-objective evaluation function, the robot starts to perform the task along the selected trajectory. The path execution process includes feedback control of the robot's position and speed.

[0026] And when the robot is inspecting, the relative speed, dynamic window and multi-objective evaluation function are dynamically updated.

[0027] Furthermore, the real-time update of relative speed means that in each motion cycle, the robot recalculates the relative speed vector with other robots and adjusts the RVO relative speed obstacle zone based on the latest position to ensure that the obstacle avoidance area is always accurate.

[0028] Furthermore, the real-time update of the dynamic window means that as the robot speed and the surrounding environment change, the dynamic window range will be updated in real time.

[0029] Furthermore, the real-time update of the multi-objective evaluation function refers to adjusting the weights of each evaluation function under different task requirements.

[0030] The beneficial effects of the present invention over the prior art are as follows: by introducing RVO soft constraints in the path planning process of the DWA algorithm, the robot can predict its potential collision speed relative to other robots, thereby limiting the possible collision speed in advance during local path planning. This combination not only retains the ability of the DWA algorithm to consider the robot's motion constraints, but also improves the collaboration and obstacle avoidance safety between robots through RVO soft constraints. This combination is particularly suitable for task-intensive scenarios such as multi-robot collaborative inspections, which improves the stability and safety of the system while ensuring inspection efficiency.

[0031] Specifically:

[0032] 1. Improve system safety: Through RVO soft constraints, the present invention enables the robot to effectively avoid other robots in a high-density, multi-robot collaborative environment, reducing the risk of collision.

[0033] 2. Dynamic obstacle avoidance to adapt to complex environments: By dynamically updating the dynamic window, RVO relative speed obstacle zone and multiple evaluation functions, the robot can adapt to different dynamic environments and significantly improve its avoidance flexibility, especially in narrow or high-density scenes, it can still maintain a high obstacle avoidance capability.

[0034] 3. Improve collaborative inspection efficiency: The combination of RVO soft constraints enables each robot to make effective path selection without affecting other robots, improving the overall efficiency and stability of the inspection task. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The present invention will be further described below in conjunction with the accompanying drawings:

[0036] Figure 1 This is the flow chart of the traditional DWA algorithm;

[0037] Figure 2 This is a schematic diagram of the dynamic window of the traditional DWA algorithm;

[0038] Figure 3 Generate a schematic diagram for the traditional DWA algorithm trajectory;

[0039] Figure 4 It is a schematic diagram of relative velocity obstacle area (RVO);

[0040] Figure 5 This is a flow chart of the improved local path planning method proposed in the present invention. DETAILED DESCRIPTION

[0041] like Figures 1 to 5As shown, the present invention provides an improved local path planning method suitable for multi-robot inspection tasks, the goal of which is to improve the safety and coordination of the improved DWA algorithm in a multi-robot system, ensure that the robots can complete inspection tasks safely and smoothly in dynamic and high-density scenarios, and significantly reduce the risk of collision between robots. This improved solution is suitable for application scenarios such as multi-robot collaborative inspection, warehousing and logistics, and mobile service robots, and has wide applicability and superior obstacle avoidance performance.

[0042] The traditional DWA algorithm and the improved algorithm of the present invention are described in detail below with reference to the accompanying drawings.

[0043] Figure 1 The overall process of the traditional DWA algorithm is shown in the figure, including initialization, dynamic window generation, evaluation function calculation, optimal trajectory selection and robot motion control. Figure 2 The schematic diagram of the dynamic window of the traditional DWA algorithm is shown, showing the dynamic window generated in the DWA algorithm according to the current state and physical limitations of the robot. The feasible linear velocity and angular velocity range are limited within the dynamic window to ensure that the motion is planned within the safe range at every moment; Figure 3 A schematic diagram of the trajectory generation of the traditional DWA algorithm is shown, which depicts multiple candidate trajectories generated in the dynamic window range, and displays the candidate motion paths corresponding to different speed and angular velocity combinations, providing a basis for the evaluation function to select the optimal path. Figure 4 This is a schematic diagram of the relative velocity obstacle area (RVO), which shows the obstacle avoidance area built based on relative velocity and indicates the speed range that may cause a collision, so that the robot can avoid entering the RVO relative velocity obstacle area by adjusting its speed. Figure 5 This is a flowchart of the improved local path planning method proposed in the present invention, which describes the flowchart improved by the present invention based on the traditional DWA, and especially shows the path planning and obstacle avoidance processing steps after introducing the relative speed obstacle soft constraint, so as to improve the safety and obstacle avoidance efficiency in multi-robot collaboration.

[0044] The specific technical solutions of the present invention are as follows:

[0045] 1. Trajectory generation and optimization based on DWA: The present invention first retains the basic path generation logic of the traditional DWA algorithm, that is, a dynamic window is constructed according to the robot's current speed, maximum speed and acceleration and surrounding environment information, and a series of feasible trajectories are generated on this basis. The traditional DWA algorithm uses an evaluation function to select the best trajectory from multiple candidate trajectories to ensure that the robot moves as safely and efficiently as possible within a local range. By adjusting the evaluation function weights of the traditional DWA algorithm, the present invention can find an appropriate balance between speed and obstacle avoidance safety, thereby laying the foundation for the subsequent introduction of RVO soft constraints.

[0046] 2. Obstacle avoidance strategy with RVO soft constraints: Based on the improved trajectory generation of the DWA algorithm, the present invention introduces RVO soft constraints in the trajectory selection process through the evaluation function. Specifically, RVO predicts the potential collision speed by calculating the relative speed obstacle zone between robots, thereby achieving "soft" obstacle avoidance, that is, avoiding the robot from being forced to detour, and reducing the possibility of collision by guiding it to select the appropriate direction and speed in advance. This step includes the following sub-steps:

[0047] (1) Relative speed and position calculation: Calculate the relative speed and position of each robot with respect to other robots around it in real time and establish its relative speed vector.

[0048] (2) RVO relative speed obstacle zone construction: Based on the relative speed and position, a virtual RVO speed obstacle avoidance zone is calculated and generated. This zone changes dynamically and is updated at any time according to the changes in parameters such as the distance, speed, and orientation between the robots.

[0049] (3) Soft constraint effect: The RVO soft constraint is combined with the evaluation function of the improved DWA algorithm to give a lower score to the trajectory whose speed is in the RVO relative speed obstacle area. In this way, the path whose speed is not in the RVO relative speed obstacle area is preferentially selected among the candidate trajectories, ensuring that the robot completes the path planning without interfering with other robots.

[0050] 3. Improvement of multi-objective evaluation function: The present invention improves the multi-objective evaluation function of the DWA algorithm to include a comprehensive evaluation of heading angle evaluation, speed evaluation, distance evaluation and safety evaluation. After adding the RVO soft constraint, the evaluation function is allocated according to the following logic:

[0051] (1) Heading angle evaluation and speed evaluation: Evaluate the direction and speed of the trajectory when reaching the target position to ensure that the robot reaches the destination as quickly as possible.

[0052] (2) Distance evaluation: Based on the distance judgment of obstacles in the traditional DWA algorithm, a path farther away from obstacles is selected to improve the obstacle avoidance performance.

[0053] (3) Safety evaluation (RVO soft constraint): The safety score of RVO soft constraint is introduced. If a trajectory avoids the RVO relative speed obstacle area while ensuring other evaluations, this trajectory will be selected first, which greatly improves the avoidance safety between robots.

[0054] 4. Dynamic update mechanism: The present invention also has a dynamic update mechanism to ensure high real-time performance and adaptability in multi-robot systems. As the robot moves and the environmental state changes, the parameters of the RVO relative speed obstacle zone and the dynamic window will be updated in real time to ensure that each robot can adjust the path according to the latest information to prevent the path planning from failing due to environmental changes. In each movement cycle, the robot will re-evaluate the position and speed of the surrounding robots, and by updating the RVO relative speed obstacle zone in real time, the obstacle avoidance area and ROV soft constraints can be accurately adjusted. This mechanism ensures the safety and consistency of the robot path in complex environments.

[0055] The workflow of the present invention is as follows:

[0056] 1. Path planning initialization: Each robot starts the improved DWA algorithm and generates an initial set of trajectories based on environmental perception. During the generation process, the safety and efficiency of the candidate trajectories are calculated and evaluated.

[0057] 2. RVO relative speed obstacle zone calculation: The robot obtains the speed and position information of other robots in real time, calculates the relative speed and position of each robot, and builds a relative speed obstacle zone based on this. This zone is used to define the speed range of collision and is the basis of RVO soft constraints.

[0058] 3. Multi-objective trajectory screening: In the trajectory set, select the path with the highest score. Through the multi-objective evaluation function, the path that avoids the relative speed obstacle area is given a higher score to ensure that the selected path can meet the safety requirements to the greatest extent.

[0059] 4. Path execution and dynamic update: The robot performs inspection tasks along the selected path, continuously updates the RVO relative speed obstacle area during the movement, and adjusts the path in real time.

[0060] The local path planning of the present invention for multi-robot collaborative inspection tasks is further described below based on specific embodiments.

[0061] 1. Implementation environment and initialization settings

[0062] (1) Implementation environment: This embodiment tests the method of the present invention in a laboratory. The environment is a spacious indoor venue, which is distributed with several fixed obstacles such as bookcases, walls, tables and chairs, and there are multiple mobile robots performing inspection tasks in the venue. The venue is a planar environment. The robot perceives the environment through a two-dimensional laser radar. The goal is to reach the designated inspection point through path planning, and it needs to avoid other robots and fixed obstacles during the process.

[0063] (2) Initialization settings: including:

[0064] Position and target: According to the task requirements, the robot's current position coordinates and target point coordinates are given;

[0065] Global path: The global optimal path is planned through the A* algorithm as the basis for local planning;

[0066] Speed ​​and acceleration limits: Define the maximum speed and acceleration range of each robot to ensure that it does not exceed physical constraints when executing path planning;

[0067] Sensor configuration: Turn on the LiDAR to scan and record the surrounding environment information, including the location of obstacles and other robots.

[0068] 2. Dynamic window generation and candidate trajectory generation

[0069] (1) Dynamic window generation: A dynamic window is constructed based on the robot’s maximum speed, acceleration, current speed, and distance to the nearest obstacle. The size of the window is dynamically adjusted based on the real-time speed, acceleration, and distance limit to the nearest obstacle. The steps for constructing a dynamic window are as follows:

[0070] 1) Due to the limitations of the robot's own conditions, there is a constraint on the speed V s . That is, the range of the robot's linear velocity and angular velocity, and its expression is as follows:

[0071] V s ={(v,w)|v∈[v min ,v max ]∩w∈[w min ,w max ]};

[0072] Where: v max With w max are the maximum linear velocity and maximum angular velocity of the robot, v min With w min are the minimum linear velocity and minimum angular velocity of the robot respectively.

[0073] 2) Due to the limitation of motor torque, there is a constraint on acceleration V a , that is, the range of the maximum linear velocity, angular velocity and minimum linear velocity, angular velocity that the robot can reach within the sampling time, and its expression is as follows:

[0074]

[0075] Where: v t With w t are the current linear velocity and angular velocity of the robot respectively, Δt is the sampling time, and point

[0076] are the linear acceleration and angular acceleration of the robot.

[0077] 3) The robot should be set with a safety constraint to avoid collision when moving, that is, the robot is set with a certain safety speed. If the robot can reduce the speed to zero before colliding with an obstacle, the speed group will be retained; on the contrary, if the robot cannot reduce the speed to zero before the collision occurs, resulting in a collision with an obstacle, the speed group will be discarded. Safety constraint V d The expression is as follows:

[0078]

[0079] Where: dist(v,w) is the distance between the robot and the nearest obstacle. In the absence of obstacles, it is a large constant.

[0080] According to the above constraints, the dynamic window of the robot can be obtained as the intersection V of the three velocity vector spaces. r , which is expressed as follows:

[0081] V r =V s ∩V a ∩V d ={(v,w)|v∈[v min ,v max ]∩w∈[w min ,w max ]};

[0082] Where: v max With w max are the maximum linear velocity and angular velocity of the robot that meets all constraints, v min With w min are the minimum linear velocity and angular velocity of the robot that meets all constraints.

[0083] (2) Candidate trajectory generation: Within the dynamic window, multiple different linear velocity and angular velocity combinations are discretized and used to simulate candidate trajectories. The starting point of each trajectory is the current position of the robot, and the linear velocity and angular velocity combination comes from each discrete point within the dynamic window. To ensure that the generated trajectory can both reach the target and avoid obstacles, the generation process of each trajectory simulates the effect of the robot moving along the trajectory in the short term in the future.

[0084] 3. Introduction of RVO soft constraints and construction of relative velocity obstacle zone

[0085] (1) Relative speed calculation

[0086] Each robot obtains the position and speed data of other robots around it in real time and calculates the relative speed vector V using the following formula rel:

[0087] V rel =V robot -V other ;

[0088] Where: V robot is the current speed of the robot, V other is the speed of other robots. The relative speed information is used to evaluate the collision risk and construct the RVO relative speed obstacle zone.

[0089] (2) Construction of RVO relative velocity obstacle zone

[0090] Based on the relative speed and position, the RVO relative speed obstacle zone is constructed. The RVO relative speed obstacle zone is determined by the relative distance, relative speed and direction of travel between robots, and forms an obstacle avoidance area with a certain offset angle and distance: if the relative distance is closer, the obstacle zone will be larger to ensure effective obstacle avoidance in high-risk areas. The obstacle zone is dynamically adjusted and kept updated in real time.

[0091] (3) Application of RVO soft constraints

[0092] When evaluating candidate trajectories, the RVO relative speed obstacle area is considered as a "soft" constraint. That is, if the speed of a trajectory enters the RVO relative speed obstacle area, the safety score of the trajectory will be reduced, but it will not be completely excluded. Through this soft constraint method, the robot is more inclined to avoid other robots when selecting a path, ensuring overall safety while also ensuring efficiency.

[0093] 4. Improved multi-objective evaluation function:

[0094] The evaluation function is used to comprehensively score each candidate trajectory to ensure that the path selection takes into account both efficiency and safety. The improved multi-objective evaluation function is as follows:

[0095] G'(v,w)=α·head(v,w)+β·vel(v,w)+γ·dist(v,w)+λ·rvo(v,w);

[0096] Where head(v,w), vel(v,w), dist(v,w) and rvo(v,w) are scoring factors, and α, β, γ and λ are weight coefficients of the scoring factors.

[0097] The improved multi-objective evaluation function consists of the following scoring elements:

[0098] (1) Heading angle score

[0099] The score is assigned according to the angle between the positive direction of the candidate trajectory end and the target point. Assuming that the angle between the positive direction of the trajectory end and the target point is Δθ, the heading angle score formula is:

[0100]

[0101] In the formula: The smaller the angle, the higher the score, ensuring that the robot can move in the target direction.

[0102] (2) Speed ​​score

[0103] The candidate trajectory is scored based on its speed. The faster the speed, the higher the score, ensuring that the robot can move towards the target faster. The speed scoring formula is:

[0104]

[0105] (3) Distance score

[0106] To avoid collisions with fixed obstacles, the distance score is evaluated based on the distance between the candidate trajectory and the nearest obstacle.

[0107] The greater the distance, the higher the score. The distance score formula is:

[0108]

[0109] (4) Safety score (RVO soft constraint)

[0110] A lower safety score is assigned to candidate trajectories whose speed is in the RVO relative speed obstacle zone. This improves the safety of the robot. The safety score formula is:

[0111]

[0112] In order to ensure that the effects of the four scores are balanced and to prevent the influence of one score from being too great and weakening or even ignoring the effects of other scores, the above four scoring formulas are the results of normalization.

[0113] 5. Dynamic update mechanism

[0114] (1) Real-time update of relative speed: In each motion cycle, the robot recalculates the relative speed vector with other robots and adjusts the RVO relative speed obstacle zone based on the latest position to ensure that the obstacle avoidance area is always accurate. The relative speed update can adapt to real-time environmental changes and ensure the effectiveness of obstacle avoidance.

[0115] (2) Dynamic window parameter update: As the robot speed and surrounding environment change, the dynamic window range will be updated in real time, enabling the improved DWA algorithm to respond quickly and flexibly to environmental changes, balance travel efficiency and safety, and thus achieve more efficient obstacle avoidance and path planning.

[0116] (3) Weight adjustment of multi-objective evaluation function: The weight of the evaluation function is adjusted according to different task requirements. For example, in a fast inspection task, the weight of the speed score is increased; in a complex environment, the weight of the distance score and the RVO soft constraint is increased to ensure safety.

[0117] 6. Path execution: Based on the best path selected by the multi-objective evaluation function, the robot starts to execute the task along the selected trajectory. The path execution process includes feedback control of position and speed to ensure that the robot moves smoothly along the planned path. If new obstacles or other robots enter the path area during the execution process, the improved DWA algorithm will immediately re-plan the path to avoid collision.

[0118] The main innovation of the present invention is to introduce the relative velocity obstacle (RVO) soft constraint into the traditional DWA (dynamic window algorithm), and optimize the local path planning of the multi-robot system to achieve higher safety and adaptability. Specifically, it includes:

[0119] 1. Introduce RVO soft constraints to enhance obstacle avoidance capabilities

[0120] By introducing the relative velocity obstacle (RVO) soft constraint mechanism into the traditional DWA algorithm, the robot's obstacle avoidance capability in a dynamic environment is improved. The RVO region defines the potential collision area caused by relative velocity. By constraining the RVO region in the candidate trajectory evaluation, the robot can flexibly avoid dynamic obstacles, especially other mobile robots, while efficiently planning the path.

[0121] 2. Improve the safety of multi-robot collaboration

[0122] In response to the needs of multi-robot collaborative work, the introduction of RVO soft constraints effectively reduces the probability of collision between robots, allowing the multi-robot system to safely perform autonomous inspections or other tasks in the same space. Compared with the traditional DWA algorithm based only on static obstacle avoidance, the improvement of the present invention makes the system more adaptable to dynamic changes in a multi-robot environment.

[0123] 3. Real-time dynamic adjustment of trajectory planning

[0124] By combining the real-time update of the dynamic window and the relative position and velocity information of the RVO soft constraints, the present invention can dynamically adjust the priority of candidate trajectories according to the latest environmental information at each time step, achieving high flexibility in path planning and obstacle avoidance, allowing the robot to respond to changes in complex environments more smoothly and naturally.

[0125] 4. Balance mission efficiency and obstacle avoidance safety

[0126] The evaluation function of the traditional DWA algorithm usually considers the target proximity (heading angle and speed) and obstacle avoidance distance. The innovation of the present invention integrates the RVO soft constraint into the evaluation function, so that the robot can strike a balance between obstacle avoidance and travel efficiency, thereby improving the task completion efficiency of the entire system.

[0127] In general, the innovation of the present invention is to improve the traditional DWA algorithm to make it more suitable for the complex environment of multi-robot systems. By introducing RVO soft constraints, the safety and flexibility of robot path planning are improved.

[0128] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. An improved local path planning method suitable for multi-robot inspection tasks, characterized by: Based on the dynamic window method DWA and introducing the relative speed barrier RVO soft constraint, the following steps are included: Step 1: Confirm the current position coordinates and target point coordinates of each robot, plan the global optimal path, set the maximum speed and acceleration range of each robot, start the robot to scan and record the surrounding environment information; Step 2: Generate dynamic windows and candidate trajectories based on the dynamic window method DWA; Step 3: RVO relative speed obstacle zone construction: Obtain the relative speed and position between the robot and other robots in real time, and construct a relative speed obstacle zone to represent the potential collision speed area; Step 4: Calculation of multi-objective evaluation function: Comprehensively score the candidate trajectories using heading angle, speed, distance and safety; Step 5: Path selection: Based on the score of the multi-objective evaluation function, select the candidate trajectory with the highest score as the best path to enable the robot to approach the target point safely and efficiently.

2. The improved local path planning method for multi-robot inspection tasks according to claim 1 is characterized in that: The dynamic window in step 2 is constructed based on the robot's maximum speed, maximum acceleration, current speed, and the nearest obstacle position limit to determine the range of linear velocity and angular velocity allowed by the robot at the next moment, so that the dynamic window adapts to environmental changes and satisfies constraints.

3. The improved local path planning method suitable for multi-robot inspection tasks according to claim 1 is characterized in that: The candidate trajectory generation step in step 2 is: generate multiple discrete linear velocity and angular velocity combinations within the dynamic window range, and simulate different trajectory effects according to the above combinations to ensure that the robot can flexibly select a safe and efficient path in a multi-robot scenario; the starting point of each trajectory is the current position of the robot; The candidate trajectory includes multiple path options from the robot's current position to the next position.

4. The improved local path planning method for multi-robot inspection tasks according to claim 1 is characterized in that: The steps for constructing the RVO relative velocity obstacle zone in step 3 are as follows: (1) Relative speed and position calculation: Calculate the relative speed and position of each robot with respect to other robots around it in real time, and establish the corresponding relative speed vector; (2) Construction of RVO relative speed obstacle zone: Based on the relative speed and position, a virtual RVO speed obstacle avoidance zone that can be changed dynamically is calculated and generated. The RVO speed obstacle avoidance zone represents the potential speed range in which the robot may collide with other robots in the future. The RVO relative speed obstacle zone will be adjusted in real time according to the relative speed, robot spacing and relative direction. When the robot approaches other robots, the RVO relative speed obstacle zone will expand to enhance the obstacle avoidance effect.

5. The improved local path planning method for multi-robot inspection tasks according to claim 1 is characterized in that: The rules for heading angle, speed, distance and safety score in step 4 are as follows: (1) Heading angle score: calculated based on the angle between the positive direction of the candidate trajectory end and the target point. The smaller the angle, the higher the score. (2) Speed ​​score: Calculated based on the speed of the candidate trajectory. The faster the speed, the higher the score. (3) Distance score: calculated based on the distance between the candidate trajectory and the nearest obstacle. The greater the distance, the higher the score. (4) Safety score: If the speed of a candidate trajectory is within the RVO relative speed obstacle zone, the score of the candidate trajectory is reduced to encourage the robot to avoid other robots.

6. The improved local path planning method for multi-robot inspection tasks according to claim 1, characterized in that: In step 5, when performing path selection, if the path with the highest score has a collision risk, the path is replanned by recalculating the candidate trajectory within the dynamic window to ensure the safety of the path and the continuity of the task.

7. An improved local path planning method suitable for multi-robot inspection tasks according to any one of claims 1 to 6, characterized in that: It also includes feedback control of path execution. According to the best path selected by the multi-objective evaluation function, the robot starts to perform the task along the selected trajectory. The path execution process includes feedback control of the robot's position and speed. And when the robot is inspecting, the relative speed, dynamic window and multi-objective evaluation function are dynamically updated.

8. The improved local path planning method for multi-robot inspection tasks according to claim 7 is characterized in that: Real-time update of relative speed means that in each motion cycle, the robot recalculates the relative speed vector with other robots and adjusts the RVO relative speed obstacle zone based on the latest position to ensure that the obstacle avoidance area is always accurate.

9. The improved local path planning method applicable to multi-robot inspection tasks according to claim 7, characterized in that: Real-time update of the dynamic window means that as the robot speed and surrounding environment change, the dynamic window range will be updated in real time.

10. The improved local path planning method applicable to multi-robot inspection tasks according to claim 7, characterized in that: Real-time updating of multi-objective evaluation functions means adjusting the weights of each evaluation function under different task requirements.

Images