An improved local path planning method suitable for multi-robot patrol inspection task

Abstract

The application provides an improved local path planning method suitable for multi-robot inspection tasks, and belongs to the technical field of robot path planning; the method solves the limitations of traditional DWA algorithm in path planning when multiple robots work cooperatively; the application is based on dynamic window method DWA and introduces relative velocity obstacle RVO soft constraint, and comprises the following steps: generating a dynamic window and a candidate trajectory based on dynamic window method DWA; RVO relative velocity obstacle area construction: acquiring the relative velocity and position between robots and other robots in real time, and constructing a relative velocity obstacle area for representing a potential collision velocity area; multi-target evaluation function calculation: adopting a heading angle, velocity, distance and safety to comprehensively score the candidate trajectory; path selection: selecting the candidate trajectory with the highest score as the best path according to the score of the multi-target evaluation function, so that the robot can safely and efficiently approach a target point; and the application is suitable for multi-robot cooperative obstacle avoidance.

Description

TECHNICAL FIELD

[0001] The application provides an improved local path planning method suitable for multi-robot patrol tasks, and belongs to the technical field of robot path planning. BACKGROUND

[0002] In the field of mobile robots, path planning is an important part of robot autonomous navigation and obstacle avoidance. Among various path planning algorithms, the dynamic window approach (DWA) is chosen by many real-time navigation systems due to its fast response to obstacles and relatively low computational cost. The DWA algorithm constructs a dynamic window based on the current speed and acceleration of the robot and other motion characteristics, discretizes the dynamic window into a combination of linear and angular velocities, 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 work.

[0003] The traditional DWA algorithm is mainly based on local environmental perception and does not consider the motion trend of other robots at the same time. When multiple robots perform tasks in the same area, such as simultaneous patrol tasks, there is a high 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 near the same position or path intersection, sudden turns or collisions are likely to occur. In addition, the DWA algorithm is usually more sensitive to static obstacles during execution, while the dynamic avoidance ability for other mobile robots is relatively weak.

[0004] In order to improve the dynamic obstacle avoidance performance of the DWA algorithm in dynamic environments and adapt to the needs of multi-robot collaborative work, the relative velocity obstacle (RVO) becomes an effective strategy. RVO calculates the relative velocity obstacle region (i.e., predicts the potential collision velocity range based on the relative speed and position of other robots) to provide a more forward-looking obstacle avoidance mechanism for robots. RVO is widely used in obstacle avoidance and collaboration in high-density and multi-mobile scenarios, and can better predict and avoid collision risks. SUMMARY

[0005] In order to solve the limitations of the traditional DWA algorithm in path planning for multi-robot collaborative work, the application proposes an improved local path planning method suitable for multi-robot patrol tasks, which introduces RVO soft constraints to make it more suitable for dynamic obstacle avoidance in multi-robot collaborative work.

[0006] The technical solution adopted in this invention is: an improved local path planning method suitable for multi-robot inspection tasks, based on the Dynamic Window Method (DWA) and introducing relative velocity obstacle (RVO) soft constraints, including the following steps:

[0007] Step 1: Confirm the current position coordinates and target point coordinates of each robot, plan the globally optimal path, set the maximum speed and acceleration range of each robot, and 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 Velocity Barrier Zone Construction: Real-time acquisition of the relative velocity and position between the robot and other robots to construct a relative velocity barrier zone to represent potential collision velocity areas;

[0010] Step 4: Calculation of multi-objective evaluation function: The candidate trajectory is comprehensively scored 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 so that the robot can safely and efficiently approach the target point.

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

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

[0014] Furthermore, the candidate trajectory includes multiple path selections from the robot's current position to the next position.

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

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

[0017] (2) Construction of RVO relative velocity obstacle zone: Based on relative velocity and position, a virtual and dynamically changeable RVO velocity obstacle avoidance zone is calculated and generated. The RVO velocity obstacle avoidance zone represents the potential velocity range in which the robot will collide with other robots in the future.

[0018] The RVO relative velocity obstacle zone is adjusted in real time based on relative speed, robot spacing, and relative direction. When a robot approaches other robots, the RVO relative velocity obstacle zone expands to enhance obstacle avoidance.

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

[0020] (1) Heading angle score: Calculated based on the angle between the positive direction at the end of the candidate trajectory 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 the candidate trajectory is within the RVO relative speed barrier zone, the score of the candidate trajectory is reduced to encourage the robot to avoid other robots.

[0024] Furthermore, in step 5, when selecting a path, if the path with the highest score has a collision risk, the candidate trajectories within the dynamic window are recalculated for path replanning to ensure path safety and task continuity.

[0025] Furthermore, it also includes feedback control for path execution. Based on the optimal path selected by the multi-objective evaluation function, the robot begins to execute the task along the selected trajectory. The path execution process includes feedback control of the robot's position and speed.

[0026] Furthermore, during robot inspection, the relative speed, dynamic window, and multi-objective evaluation function are dynamically updated.

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

[0028] Furthermore, the real-time updating of the dynamic window means that the range of the dynamic window is updated in real time as the robot's speed and the surrounding environment change.

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

[0030] The advantages of this invention compared to existing technologies are as follows: By introducing Restricted Variable Valor (RVO) soft constraints into the path planning process of the DWA algorithm, the robot can predict its potential collision speed relative to other robots, thereby limiting possible collision speeds in advance during local path planning. This combination retains the DWA algorithm's ability to consider robot motion constraints while improving inter-robot cooperation and obstacle avoidance safety through RVO soft constraints. This combination is particularly suitable for task-intensive scenarios such as multi-robot collaborative inspection, improving system stability and safety while ensuring inspection efficiency.

[0031] Specifically:

[0032] 1. Improved system safety: This invention uses RVO soft constraints to enable robots to effectively avoid other robots in high-density, multi-robot collaborative environments, reducing the risk of collision.

[0033] 2. Dynamic obstacle avoidance adapting to complex environments: By dynamically updating the dynamic window, RVO relative velocity obstacle zone, and multiple evaluation functions, the robot can adapt to different dynamic environments, significantly improving obstacle avoidance flexibility, and maintaining high obstacle avoidance capability even in narrow or high-density scenes.

[0034] 3. Improve collaborative inspection efficiency: The combination of RVO soft constraints enables each robot to make effective path selection without affecting other robots, thereby improving the overall efficiency and stability of the inspection task. Attached Figure Description

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

[0036] Figure 1 Here is a flowchart 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 A schematic diagram of trajectory generation using the traditional DWA algorithm;

[0039] Figure 4 This is a schematic diagram of the Relative Velocity Barrier Zone (RVO).

[0040] Figure 5 This is a flowchart of the improved local path planning method proposed in this invention. Detailed Implementation

[0041] like Figures 1 to 5As shown, this invention provides an improved local path planning method suitable for multi-robot inspection tasks. The goal is to enhance the safety and collaboration of the improved DWA algorithm in multi-robot systems, ensuring that robots can safely and smoothly complete inspection tasks in dynamic, high-density scenarios, and significantly reducing the risk of collisions between robots. This improved scheme is applicable to multi-robot collaborative inspection, warehousing and logistics, mobile service robots, and other application scenarios, possessing broad applicability and superior obstacle avoidance performance.

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

[0043] Figure 1 The document demonstrates the overall process of the traditional DWA algorithm, including steps such as initialization, dynamic window generation, evaluation function calculation, optimal trajectory selection, and robot motion control. Figure 2 The diagram shows a dynamic window of the traditional DWA algorithm. It shows the dynamic window generated by the DWA algorithm based on the robot's current state and physical constraints. The feasible linear velocity and angular velocity range are limited within the dynamic window to ensure that the motion is planned within a safe range at every moment. Figure 3 This diagram illustrates the trajectory generation of the traditional DWA algorithm, depicting multiple candidate trajectories generated within a dynamic window. It showcases candidate motion paths corresponding to different combinations of speed and angular velocity, providing a basis for the evaluation function to select the optimal path. Figure 4 This is a schematic diagram of the Relative Velocity Obstacle Zone (RVO), which shows the obstacle avoidance area built based on relative velocity. It indicates the speed range that may lead to a collision, enabling the robot to avoid entering the RVO by adjusting its speed. Figure 5 This is a flowchart of the improved local path planning method proposed in this invention. It describes the improved flowchart based on the traditional DWA, and in particular shows the path planning and obstacle avoidance steps after introducing relative velocity obstacle soft constraints to improve safety and obstacle avoidance efficiency in multi-robot collaboration.

[0044] The specific technical solution of the present invention is as follows:

[0045] 1. Trajectory Generation and Optimization Based on DWA: This invention first retains the basic path generation logic of the traditional DWA algorithm, namely, constructing a dynamic window based on the robot's current speed, maximum speed, acceleration, and surrounding environment information, and generating a series of feasible trajectories based on this window. The traditional DWA algorithm uses an evaluation function to select the optimal trajectory from multiple candidate trajectories, ensuring the robot moves forward as safely and efficiently as possible within a local area. This invention, by adjusting the weights of the evaluation function in the traditional DWA algorithm, can find a suitable balance between speed and obstacle avoidance safety, thus laying the foundation for the subsequent introduction of RVO soft constraints.

[0046] 2. Obstacle Avoidance Strategy Introducing RVO Soft Constraints: Based on the improved DWA algorithm trajectory generation, this invention introduces RVO soft constraints during trajectory selection via an evaluation function. Specifically, RVO predicts potential collision velocities by calculating the relative velocity obstacle zone between robots, thereby achieving "soft" obstacle avoidance. This avoids forced obstacle detours by guiding the robot to pre-select appropriate directions and velocities, reducing the likelihood of collisions. This step includes the following sub-steps:

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

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

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

[0050] 3. Improvement of the Multi-Objective Evaluation Function: This invention improves the multi-objective evaluation function of the DWA algorithm, making it a comprehensive evaluation that includes heading angle evaluation, velocity 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 velocity evaluation: Evaluate the orientation and velocity during the trajectory to reach the target position to ensure that the robot reaches the destination as soon as possible.

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

[0053] (3) Safety evaluation (RVO soft constraint): Introducing a safety score based on RVO soft constraints. If a trajectory avoids the RVO relative velocity barrier area while ensuring other evaluations, the trajectory will be selected first, greatly improving the avoidance safety between robots.

[0054] 4. Dynamic Update Mechanism: This invention also features a dynamic update mechanism, ensuring high real-time performance and adaptability in multi-robot systems. As the robot moves and the environmental conditions change, the parameters of the RVO relative velocity obstacle zone and dynamic window are updated in real time, ensuring that each robot can adjust its path based on the latest information and preventing path planning failures due to environmental changes. In each motion cycle, the robot reassesses the position and speed of surrounding robots, and by updating the RVO relative velocity obstacle zone in real time, the obstacle avoidance area and ROV soft constraints are precisely adjusted. This mechanism guarantees the safety and consistency of the robot path in complex environments.

[0055] The workflow of this invention is as follows:

[0056] 1. Path Planning Initialization: Each robot starts the improved DWA algorithm to generate 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 Velocity Obstacle Zone Calculation: The robot acquires real-time velocity and position information from other robots, calculates the relative velocity and position of each robot, and constructs a relative velocity obstacle zone based on this. This zone defines the velocity range for collisions and is the foundation of RVO soft constraints.

[0058] 3. Multi-objective trajectory selection: From the trajectory set, the path with the highest score is selected. Using a multi-objective evaluation function, paths that avoid relative speed obstacle zones are assigned higher scores, ensuring that the selected path meets safety requirements to the greatest extent possible.

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

[0060] The following describes the local path planning of the present invention for multi-robot collaborative inspection tasks in further detail with reference to specific embodiments.

[0061] 1. Implementation Environment and Initialization Settings

[0062] (1) Implementation Environment: In this embodiment, the method of the present invention is tested in a laboratory. The environment is a spacious indoor space with several fixed obstacles such as bookshelves, walls, and tables and chairs, and multiple mobile robots performing inspection tasks are present in the space. The space is a planar environment, and the robots perceive the environment through two-dimensional lidar. The goal is to reach the designated inspection point through path planning, during which they need to avoid other robots and fixed obstacles.

[0063] (2) Initialization settings: including:

[0064] Position and Target: Based on task requirements, provide the robot's current position coordinates and the target point coordinates;

[0065] Global path: The A* algorithm is used to plan the globally optimal path, which serves as the basis for local planning;

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

[0067] Sensor configuration: Turn on the LiDAR to scan and record information about the surrounding environment, including the location of obstacles and the position of 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 this window is dynamically adjusted according to real-time speed, acceleration, and distance to the nearest obstacle. The steps for constructing the dynamic window are as follows:

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

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

[0072] In the formula: v max with w max These are the robot's maximum linear velocity and maximum angular velocity, v. min with w min These are the robot's minimum linear velocity and minimum angular velocity, respectively.

[0073] 2) Due to the limitation of motor torque, there is a constraint on acceleration V. a This refers to the range of the robot's maximum and minimum linear and angular velocities within the sampling time, expressed as follows:

[0074]

[0075] In the formula: v t with w t These represent the robot's current linear velocity and angular velocity, respectively, with Δt being the sampling time. and point

[0076] Do not use linear acceleration and angular acceleration for the robot.

[0077] 3) Safety constraints should be set to avoid collisions when the robot moves, i.e., a certain safe speed should be set for the robot. If the robot can reduce its speed to zero before colliding with an obstacle, that speed set is retained; conversely, if the robot cannot reduce its speed to zero before a collision occurs, resulting in a collision with the obstacle, that speed set is discarded. Safety Constraint V d The expression is as follows:

[0078]

[0079] In the formula: dist(v,w) is the distance between the robot and the nearest obstacle, which is a large constant in the case of no obstacles.

[0080] Based on the above constraints, the robot's dynamic window can be obtained as the intersection V of the three velocity vector spaces. r Its expression is as follows:

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

[0082] In the formula: v max with w max These are the maximum linear velocity and angular velocity of the robot that satisfy all constraints, v min with w min These are the minimum linear velocity and angular velocity of the robot that satisfy all constraints.

[0083] (2) Candidate trajectory generation: Within a dynamic window, multiple different combinations of linear and angular velocities are discretized, and these combinations are used to simulate candidate trajectories. The starting point of each trajectory is the robot's current position, and the combinations of linear and angular velocities are derived from various discrete points 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 traveling along that trajectory in the near future.

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

[0085] (1) Calculation of relative velocity

[0086] Each robot acquires the position and velocity data of other surrounding robots in real time, and calculates the relative velocity vector V using the following formula. rel:

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

[0088] In the formula: V robot V is the current speed of the robot. other The speed of other robots. Relative speed information is used to assess collision risk and construct the RVO relative speed obstacle zone.

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

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

[0091] (3) Application of RVO soft constraints

[0092] When evaluating candidate trajectories, the Relative Velocity Obstacle Zone (RVO) is treated as a "soft" constraint. This means that if the velocity of a trajectory enters the RVO, the safety score of that trajectory will decrease, but it will not be completely excluded. Through this soft constraint approach, the robot is more inclined to avoid other robots when selecting a path, ensuring both overall safety and efficiency.

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

[0094] The evaluation function is used to comprehensively score each candidate trajectory, ensuring that path selection balances 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] In the formula: head(v,w), vel(v,w), dist(v,w) and rvo(v,w) are rating elements, and α, β, γ and λ are the weight coefficients of the rating elements.

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

[0098] (1) Heading Angle Rating

[0099] A score is assigned based on the angle between the positive direction of the candidate trajectory's end and the target point. Let Δθ be the angle between the positive direction of the trajectory's end and the target point; then the heading angle scoring formula is:

[0100]

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

[0102] (2) Speed ​​score

[0103] A score is assigned based on the speed of the candidate trajectory. The faster the speed, the higher the score, ensuring the robot can move towards the target more quickly. The speed scoring formula is:

[0104]

[0105] (3) Distance rating

[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] Candidate trajectories with velocities within the RVO relative velocity barrier zone are assigned lower safety scores. This improves the robot's safety. The safety score formula is:

[0111]

[0112] To ensure a balanced effect among the four ratings and to prevent one rating from having too much influence and weakening or even ignoring the effects of the other ratings, the above four rating formulas are the results after normalization.

[0113] 5. Dynamic update mechanism

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

[0115] (2) Dynamic window parameter update: The dynamic window range is updated in real time as the robot's speed and the surrounding environment change. This enables the improved DWA algorithm to respond quickly and flexibly to environmental changes, balancing travel efficiency and safety, thereby achieving more efficient obstacle avoidance and path planning.

[0116] (3) Weight adjustment of multi-objective evaluation functions: Adjust the weight of evaluation functions under different task requirements. For example, in rapid inspection tasks, increase the weight of speed score; while in complex environments, increase the weight of distance score and RVO soft constraint to ensure safety.

[0117] 6. Path Execution: Based on the optimal path selected by the multi-objective evaluation function, the robot begins to execute the task along the selected trajectory. The path execution process includes feedback control of position and velocity to ensure that the robot moves smoothly along the planned path. If a new obstacle or another robot is detected entering the path area during execution, the improved DWA algorithm will immediately replan the path to avoid collisions.

[0118] The main innovation of this invention lies in introducing relative velocity obstacle (RVO) soft constraints into the traditional DWA (Dynamic Window Algorithm), optimizing the local path planning of multi-robot systems to achieve higher safety and adaptability. Specifically, it includes:

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

[0120] By introducing a soft constraint mechanism of relative velocity barriers (RVO) into the traditional DWA algorithm, the robot's obstacle avoidance capability in dynamic environments is improved. The RVO region defines the potential collision zone 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 its path.

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

[0122] To address the need for multi-robot collaborative work, the introduction of RVO soft constraints effectively reduces the probability of collisions between robots, enabling multi-robot systems to safely perform autonomous inspections or other tasks within the same space. Compared to traditional DWA algorithms that rely solely on static obstacle avoidance, the improvements in this invention make the system more adaptable to dynamically changing requirements in multi-robot environments.

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

[0124] By combining the real-time updates of the dynamic window with the relative position and velocity information of the RVO soft constraint, this invention can dynamically adjust the priority of candidate trajectories at each time step according to the latest environmental information, achieving a high degree of flexibility in path planning and obstacle avoidance, enabling the robot to cope with changes in complex environments more smoothly and naturally.

[0125] 4. Balancing task efficiency and obstacle avoidance safety

[0126] Traditional DWA algorithms typically consider obstacle proximity (heading angle and velocity) and obstacle avoidance distance in their evaluation functions. The innovation of this invention is to integrate RVO soft constraints into the evaluation function, enabling the robot to achieve a balance between obstacle avoidance and travel efficiency, thereby improving the overall system's task completion efficiency.

[0127] Overall, the innovation of this invention lies in improving the traditional DWA algorithm to make it more suitable for complex environments with multiple robots. 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, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions 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 in that: Based on the dynamic window method DWA and the introduction of relative velocity obstacle RVO soft constraint, comprising the following steps: Step 1: confirm the current position coordinates and target point coordinates of each robot, and plan the global optimal path, set the maximum speed and acceleration range of each robot, start the robot scanning and record the surrounding environment information; Step 2: generate dynamic window and candidate trajectory based on dynamic window method DWA; Step 3: RVO relative velocity obstacle region construction: real-time acquisition of the relative velocity and position between the robot and other robots, and construction of a relative velocity obstacle region for representing the potential collision velocity region; Step 4: multi-objective evaluation function calculation: using heading angle, velocity, distance and safety to comprehensively score the candidate trajectory; The rules of heading angle, velocity, distance and safety score in step 4 are as follows: (1) Heading angle score: calculate according to the included angle between the positive direction of the end of the candidate trajectory and the target point, the smaller the included angle, the higher the score; (2) Speed score: calculate according to the speed of the candidate trajectory, the faster the speed, the higher the score; (3) Distance score: calculate according to 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 the candidate trajectory is in the RVO relative velocity obstacle region, the score of the candidate trajectory will be reduced to encourage the robot to avoid other robots; Step 5: path selection: according to the score of the multi-objective evaluation function, select the candidate trajectory with the highest score as the best path, so that the robot can safely and efficiently approach the target point.

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

3. The improved local path planning method for multi-robot inspection tasks of claim 1, wherein: The candidate trajectory generation step in step 2 is to 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 in a multi-robot scenario, the robot can flexibly select a safe and efficient path; Wherein the starting point of each trajectory is the current position of the robot; The candidate trajectory includes multiple path options from the current position of the robot to the next position.

4. The improved local path planning method for multi-robot inspection tasks of claim 1, wherein: The steps of RVO relative velocity obstacle region construction in step 3 are as follows: (1) Relative velocity and position calculation: real-time calculation of the relative velocity and position of each robot with other robots, and establishment of the corresponding relative velocity vector; (2) RVO relative velocity obstacle region construction: based on the relative velocity and position, calculate and generate a virtual RVO velocity obstacle region that can dynamically change, and the RVO velocity obstacle region represents the potential velocity range in which the robot and other robots will collide in the future; The RVO relative velocity obstacle region will be adjusted in real time according to the relative velocity, robot distance and relative direction, and when the robot approaches other robots, the RVO relative velocity obstacle region will expand to enhance the obstacle avoidance effect.

5. The improved local path planning method for multi-robot inspection tasks according to claim 1, wherein: In step 5, when the highest-scored path has a collision risk, path re-planning is performed by re-computing the candidate trajectories within the dynamic window to ensure the safety of the path and the continuity of the task.

6. The improved local path planning method for multi-robot inspection tasks according to any one of claims 1-5, characterized in that: Feedback control of path execution is also included, and the robot starts to execute the task along the selected trajectory according to the best path selected by the multi-objective evaluation function, and the path execution process includes feedback control of the robot position and speed. And when the robot is patrolling, the relative speed, dynamic window and multi-objective evaluation function are dynamically updated.

7. The improved local path planning method for multi-robot inspection tasks according to claim 6, wherein: Real-time updating of the relative speed refers to that in each motion cycle, the robot re-computes the relative speed vector with other robots and adjusts the RVO relative speed obstacle area based on the latest position to ensure that the obstacle avoidance area is always accurate.

8. The improved local path planning method for multi-robot inspection tasks according to claim 6, wherein: Real-time updating of the dynamic window refers to that as the robot speed and the surrounding environment change, the dynamic window range is updated in real time.

9. The improved local path planning method for multi-robot inspection tasks of claim 6, wherein: Real-time updating of the multi-objective evaluation function refers to adjusting the weights of each evaluation function under different task requirements.

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