A continuous space path planning method for mobile robots in narrow passages

Abstract

The application discloses a kind of mobile robot continuous space path planning methods for narrow lane, belong to mobile robot path planning field, by constructing the kinematic model of mobile robot;And based on model framing the feasible range of narrow lane and the range is a quadrilateral;According to the four vertices of quadrangle, two short sides are determined to generate the central axis, four transition points between the central axis and the long side are set on the short side, and the inclination angle is calculated with two transition points on the same side of the central axis;Based on the relationship between the relationship between the yaw angle when entering narrow lane and the inclination angle is judged based on the travel rule, when the relationship satisfies the condition of entering narrow lane, the two transition points corresponding to the inclination angle are inserted into the task sequence;Based on the path planning algorithm of multiple mobile robots in continuous space, a conflict-free path is planned from the starting position through the two transition points in the task sequence to the target position.

Description

Technical Field

[0001] The present invention belongs to the field of mobile robot path planning, and in particular relates to a continuous space path planning method for a mobile robot facing narrow lanes. Background Art

[0002] With the rapid development of artificial intelligence (AI) technology, robots are demonstrating significant advantages across various fields. Mobile robots, with their high efficiency, low cost, high stability, precision, and flexibility, have become a crucial transportation tool in logistics warehousing, flexible manufacturing, container terminals, and other fields, gaining widespread adoption. Path planning, a key underlying technology for controlling robots from a starting point to a target location, has prompted researchers to delve into the problem of multi-agent path finding (MAPF).

[0003] The MAPF problem involves planning paths for multiple agents, with the key constraint that all agents can simultaneously follow the paths derived from the policy without colliding with each other. Early research on the MAPF algorithm focused on improving classic path planning algorithms, including Dijkstra's algorithm, A* algorithm, D*Lite algorithm, artificial potential field algorithm, heuristic swarm optimization algorithm, and reinforcement learning. As research deepened, optimal solvers specifically for the MAPF problem were developed, including those based on A*, reduction, cost-growth tree, and conflict search. Furthermore, a number of suboptimal solvers with higher efficiency have been developed, including the Enhanced CBS algorithm (ECBS), the Explicit Estimation CBS algorithm (EECBS), the Priority Search-Based Solver (PBS), and the Large Neighborhood Search-Based Multi-Agent Path Planning Algorithm (MAPF-LNS2). These algorithms trade optimality for speed, better meeting the real-time requirements of practical industrial scenarios and offering improved performance in some specialized scenarios. The priority-based suboptimal solver (PBS), one of the mainstream approaches, is a two-layer search algorithm. The high-level search layer is responsible for conflict detection, constructing a priority binary tree, and implementing a best-first local search strategy. The low-level search layer utilizes a single-agent path planning algorithm to plan the shortest path for each agent that satisfies its priorities. To meet the requirements of multi-agent path planning in continuous space, it is also necessary to consider the kinematic and geometric constraints of the agents. A car-like CBS algorithm has been proposed for continuous space. This algorithm solves conflict-free path planning for agents conforming to the Ackerman model in free space. This algorithm takes into account the shape characteristics of the agents and the planned paths do not require secondary optimization, making them directly applicable to real-world scenarios. Based on this algorithm, some researchers have replaced the CBS algorithm with the PBS algorithm to improve the overall solution speed. The Ackerman model is a geometric model used to describe the steering characteristics of a car. It is based on the Ackerman steering geometry principle, which states that when a car turns, the turning radius of the inner tire is smaller than that of the outer tire to achieve more stable driving.

[0004] Logistics warehousing is one of the most important applications for mobile robots. Shelf layout is typically optimized based on the warehouse area. To maximize the use of limited space, shelves are often arranged densely, significantly reducing the range of feasible paths for mobile robots and frequently facing conflicts with oncoming vehicles in narrow aisles. The aforementioned algorithms are prone to deadlock when faced with this particular problem. Mobile robots using the Ackerman kinematic model typically have seven fixed motion units: forward left turn, forward straight ahead, forward right turn, reverse left turn, reverse straight ahead, reverse right turn, and wait. However, in narrow aisles, the space available for robot movement is extremely limited, making it often impossible to fully execute these predefined motion units, resulting in the inability to avoid oncoming vehicles. In practical planning, this limitation often leads to conflicts between two mobile robots in the middle of narrow aisles. Some algorithms solve this problem by having one robot back out of the aisle to make way for the other, while others, due to deadlock, cannot provide an effective solution. Some people have analyzed the "selfish" behavior of the PBS algorithm in resolving conflicts, that is, low-priority agents unconditionally avoid high-priority agents, and proposed an adversarial conflict resolution strategy. By scaling the geometric constraints of the agents, a more selfless conflict resolution method between agents can be achieved. This method requires continuous iteration of the scaling factor until the original geometric constraints are restored. The setting of the initial value of the scaling factor needs to be considered, and the number of iterations will also affect the overall solution speed. Summary of the Invention

[0005] To address the shortcomings of the existing technology and achieve the goals of reducing path planning complexity, improving path planning speed, and ensuring path planning success rate, the present invention adopts the following technical solutions:

[0006] A continuous spatial path planning method for a mobile robot in narrow lanes includes the following steps:

[0007] Step 1: Construct a kinematic model of the mobile robot;

[0008] Step S2: Obtaining map information and obstacle information in the mobile robot's motion area. This is done by using the mobile robot's onboard laser radar or depth camera to define a quadrilateral that forms a feasible range for the narrow alleyway, obtaining the coordinates of the four vertices of the quadrilateral, and inputting them in the form of a YAML configuration file.

[0009] Step S3: Based on the coordinate positions of the four vertices, the front and rear short sides in the direction of travel are determined. The central axis within the feasible range is obtained by connecting the midpoints of the two short sides. Based on the distance from the central axis to the two long sides on its left and right sides, four transition points between the central axis and the long sides are set on the front and rear short sides. The transition points are used to limit the driving trajectory of the mobile robot in narrow alleys. The inclination angle is calculated using the two transition points on the same side of the central axis. The positions of the four transition points are determined according to the driving rules.

[0010] Step S4: Based on the driving trajectory of the mobile robot in the single mobile robot non-conflict path, the relationship between the yaw angle when entering the narrow lane and the tilt angle is determined according to the driving rules. If the relationship satisfies the narrow lane entry condition, two transition points corresponding to the tilt angle are inserted into the task sequence;

[0011] Step S5: Based on the continuous space multi-mobile robot path planning algorithm, a conflict-free path is planned from the starting position through two transition points in the task sequence and finally reaches the target position.

[0012] Furthermore, in step 1, the mobile robot is modeled according to its Ackerman kinematic model, a global map coordinate system and a robot local coordinate system are established for the movement area of ​​the mobile robot, and the starting posture state and target posture state of each mobile robot are set according to the coordinate system; the number of mobile robots is an integer greater than or equal to 2.

[0013] Furthermore, by discretizing time, the posture state of the mobile robot at time t is obtained:

[0014]

[0015] Among them, S t represents the position state of the mobile robot at time t that satisfies the Ackerman kinematic equation, θ represents the yaw angle of the robot, that is, the angle difference between the global map coordinate system and the robot's local coordinate system, T s represents the time interval between time t and time t-1, v represents the linear velocity of the robot, L represents the distance between the front and rear wheels of the robot, Lb represents the distance from the rear axle to the rear of the vehicle, Lf represents the distance from the rear axle to the front of the vehicle, Ф represents the steering angle of the robot's front wheels. When the steering angle is fixed at Ф, the angular velocity of the robot is v / L×tanФ.

[0016] Furthermore, in step S2, the coordinate positions of the four vertices are A=(x1, y1), B=(x2, y2), C=(x3, y3), and D=(x4, y4); in step S3, the coordinate positions of the four transition points are:

[0017]

[0018] The tilt angle is:

[0019]

[0020] If the denominator of the tilt angle formula is 0, the angle is 90°.

[0021] Furthermore, in step S4, according to the rule of driving to the right, if the absolute value of the difference between the yaw angle and the tilt angle is less than 90°, two transition points corresponding to the tilt angle are inserted before the target position in the task sequence.

[0022] Furthermore, in step S5, after obtaining the complete task sequence, the multi-mobile robot path planning algorithm is called to sequentially calculate the path from the starting position to the first transition point, the path from the first transition point to the second transition point, and the path from the second transition point to the target position.

[0023] Furthermore, in step S5, a high-level multi-agent path planner performs conflict detection on the obstacle avoidance path of each mobile robot, and adds all detected conflicts to a conflict set. The high-level multi-agent path planner establishes a priority binary tree PCT and performs a depth-first search on it. Each node on the binary tree PCT stores a priority set, a path cost, and a path of each agent. All conflicts are aggregated by traversing the path of each agent, where the priority set and path set are inherited from the parent node, and new priorities are added and new paths are covered.

[0024] The strategies for filtering conflicts include partial expansion of sub-nodes, prioritizing the resolution of target position conflicts, and adaptive influence priority. Appropriate conflicts are selected to construct priorities. The priority set planning starts with an empty set. The planned path is the shortest path set for each agent when other agents are ignored. Based on the path set, it is determined whether the agent intends to pass through the narrow alley and its driving direction in the narrow alley, thereby filtering the corresponding two transition points and inserting them into the task sequence.

[0025] Furthermore, in step S5, the low-level single-agent path planner adopts a multi-label A* algorithm to process the single-agent path planning of multiple tasks. Each task is assigned a corresponding label according to its order in the task sequence, and a label of the current task is set globally. Each time a task is completed, the label will be updated to the label of the next task until all tasks are completed. The low-level path planner can reasonably solve the multi-task path planning problem caused by the insertion of transition points.

[0026] Furthermore, in step S5, the low-level single-agent path planner adopts the SIPP algorithm based on the multi-label A* algorithm to solve the multi-task path planning problem. The SIPP algorithm is based on discrete space, and the safety intervals of all map cells need to be calculated before each planning. Therefore, the overall search latitude is expanded from the original four dimensions to five dimensions; in order to adapt to the continuous space, it is necessary to maintain a region list to store the areas with safety intervals, and calculate the safety intervals of the neighboring areas according to the region list each time the neighboring areas are expanded; the SIPP algorithm is based on the safety interval and uses a five-dimensional search space (x; y; θ; t; s) to plan the path of a single mobile robot, where x, y, and θ are continuous, representing the robot's coordinate position (x, y) and posture θ; t is discrete, representing time; s is a set of time intervals, representing the safe time interval in which the current robot posture will not collide; each task in the agent task sequence is assigned a label corresponding to the order, and the label is used to identify the currently executed task and the number of remaining tasks that need to be executed.

[0027] Furthermore, in step S5, the actions of the expandable path nodes of the Ackermann model mobile robot supported by the low-level single-agent path planner include: forward left turn, forward straight ahead, forward right turn, backward left turn, backward straight ahead, backward right turn and waiting in place.

[0028] The advantages and beneficial effects of the present invention are:

[0029] The present invention solves the problem of directional conflicts in narrow alleys by setting corresponding transition points based on a given feasible range of narrow alleys and using the transition points to limit the driving trajectory of the mobile robot in the narrow alleys. The overall algorithm architecture is simple, and the path planning success rate and solution speed are high. It can provide the conflict-free path planning results of the robot cluster in narrow alleys in a relatively short time. The trajectory planned by the algorithm does not need to undergo secondary optimization and can be directly applied to actual industrial scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 is a flow chart of a method in an embodiment of the present invention.

[0031] Figure 2 Schematic diagram of the movement variation of a mobile robot in a kinematic model based on an Ackerman model in an embodiment of the present invention.

[0032] Figure 3 Schematic diagram of the feasible range and transition points framed in a corridor environment in an embodiment of the present invention.

[0033] Figure 4a This is one of the schematic diagrams of the execution of path planning for multiple mobile robots in a simulation environment in an embodiment of the present invention.

[0034] Figure 4b This is the second schematic diagram of the execution of path planning for multiple mobile robots in a simulation environment according to an embodiment of the present invention. DETAILED DESCRIPTION

[0035] The following describes the specific embodiments of the present invention in detail with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only used to illustrate and explain the present invention and are not intended to limit the present invention.

[0036] In order to achieve efficient multi-agent path planning in narrow lanes, the embodiments of the present invention provide the following conditions that the multi-agent path planning algorithm must meet:

[0037] (1) The algorithm has a simple architecture and high solving efficiency. It can provide planning results in less than 30 seconds in a 50-square-meter test map with 16 agents, and the planning success rate is over 90%.

[0038] (2) The test maps include two types: corridors and mazes. Both involve narrow alleys and conform to the placement habits of logistics warehouse shelves. The width of the narrow alleys is just enough for two mobile robots to pass through in parallel.

[0039] (3) The algorithm needs to support path planning in continuous space, that is, the planned path trajectory does not need to undergo secondary optimization and can be directly applied to actual industrial scenarios.

[0040] Based on the above conditions, the present invention provides a continuous space path planning method for mobile robots in narrow lanes, such as Figure 1 As shown, the following steps are included:

[0041] Step S1: Model the mobile robot according to its Ackermann kinematic model. A global map coordinate system and a local robot coordinate system are established for the mobile robot's motion area. The starting and target pose states of each mobile robot are set based on the coordinate systems. The number of mobile robots is an integer greater than or equal to 2.

[0042] like Figure 2 As shown in Figure 1, a global map coordinate system is established with the lower left corner of the test map as the origin, the east direction as the X-axis, and the north direction as the Y-axis. A local coordinate system is established with the center of the mobile robot's rear axle as the origin, the front direction of the mobile robot as the X' axis, and the left direction of the mobile robot as the Y' axis. The angular difference between the global map coordinate system and the robot's local coordinate system is θ.

[0043] By discretizing time, the position and posture state of the robot at time t can be obtained.

[0044]

[0045] Among them, S t The robot’s position at time t satisfies the Ackerman kinematic equation, θ is the robot’s yaw angle (the angle difference between the global map coordinate system and the robot’s local coordinate system), T s is the time interval between time t and time t-1, v is the linear velocity of the robot, L is the distance between the front and rear wheels of the robot, Lb is the distance from the rear axle to the rear of the vehicle, Lf is the distance from the rear axle to the front of the vehicle, Ф is the steering angle of the robot's front wheels, and when the steering angle is fixed at Ф, the angular velocity of the robot is v / L×tanФ.

[0046] Step S2: Obtain map information and obstacle information of the mobile robot's motion area. The acquisition method includes using the mobile robot's onboard laser radar or onboard depth camera to frame the feasible range of the narrow alley, and the range is a quadrilateral. It is necessary to obtain the coordinate positions of the four vertices of the feasible range and input them in the form of a YAML configuration file. The coordinate positions of the four vertices are A=(x1,y1), B=(x2,y2), C=(x3,y3), and D=(x4,y4), as shown in the following example: Figure 3 shown.

[0047] Step S3: Set four transition points according to the feasible range of the narrow alley to limit the driving trajectory of the mobile robot in the narrow alley.

[0048] Based on the four coordinate positions of the feasible range of the given narrow lane, the two short sides can be determined. By connecting the midpoints of the two short sides, a central axis within the feasible range can be obtained. That is, the central axis connects the midpoints of the two short sides of the quadrilateral. Then, based on the distance from the central axis to the two long sides, four transition points between the central axis and the long sides are obtained. The four transition points must all be located on the short sides. The coordinate positions of the four transition points can be calculated as follows:

[0049]

[0050] Calculate the tilt angle using two transition points on the same side of the central axis:

[0051]

[0052] If the denominator is 0, the angle is 90°. According to the traffic rule of driving on the right, determine the positions of the four transition points.

[0053] The present invention can efficiently solve the problem of opposite-direction conflicts in narrow alleys whose width only allows two mobile robots to pass safely in parallel. According to the traffic rule of driving on the right, the feasible range of narrow alleys is framed, and corresponding transition points are set. The number of transition points is 4. The feasible range is defined as a quadrilateral, whose position is uniquely determined on the map by the coordinates of the four vertices. The central axis connecting the midpoints of the two short sides of the quadrilateral can be obtained through the coordinates of the four vertices, and the four transition points evenly distributed on both sides of the central axis can be obtained. The role of the transition point is to enable the intelligent agent to drive according to the established route in the narrow alley, avoid opposite-direction conflicts in the alley, thereby improving the solver's solution efficiency in dealing with narrow alleys and greatly reducing the probability of deadlock.

[0054] Step S4: According to the driving trajectory of the mobile robot in the single mobile robot conflict-free path, two transition points are selected and inserted into the task sequence.

[0055] Each mobile robot calls the low-level single-agent path planning algorithm to calculate a conflict-free path while ignoring other mobile robots. Two transition points are selected based on its driving trajectory when passing through narrow alleys and inserted into the mobile robot's task sequence.

[0056] If the conflict-free path passes through the feasible range of the narrow alley, it is necessary to determine the relationship between the yaw angle at the initial entry into the narrow alley and the tilt angle of the transition point. If the absolute value of the difference between the yaw angle and one of the tilt angles is less than 90°, the corresponding two transition points are inserted before the target position in the task sequence.

[0057] Step S5: Utilize the multi-mobile robot path planning algorithm based on continuous space to plan a conflict-free path from the starting position through two transition points and finally to the target position.

[0058] Specifically, after obtaining the complete task sequence, the multi-mobile robot path planning algorithm is called to sequentially calculate the path from the starting position to the first transition point, the path from the first transition point to the second transition point, and the path from the second transition point to the target position.

[0059] The high-level multi-agent path planner performs conflict detection on the obstacle avoidance paths of each mobile robot and adds all detected conflicts to the conflict set. The high-level multi-agent path planner establishes a priority binary tree (PCT) and performs a depth-first search on it. Each node on the PCT stores the priority set, path cost, and path of each agent. All conflicts are collected by traversing the path of each agent, where the priority set and path set are inherited from the parent node and new priorities are added and new paths are covered. The strategies for filtering conflicts include partial expansion of child nodes, priority resolution of target position conflicts, and adaptive influence priority. The present invention selects appropriate conflicts according to the strategy to construct priorities. The priority set planning starts with an empty set, so the planned path is the shortest path set for each agent ignoring other agents. Based on this path set, it can be judged whether the agent intends to pass through the narrow alley and determine its driving direction in the narrow alley, so as to reasonably filter the corresponding two transition points and insert them into the task sequence.

[0060] The multi-robot path planning algorithm is based on a priority search, where priorities are used to resolve conflicts between mobile robots. The Ackermann model supported by the low-level single-agent path planner allows for expandable path node actions for mobile robots, including forward left turn, forward straight ahead, forward right turn, reverse left turn, reverse straight ahead, reverse right turn, and wait. The SIPP algorithm, based on the multi-label A* algorithm, is used to solve the multi-task path planning problem. The SIPP algorithm is based on a discrete space and requires calculating the safe margins of all map cells before each planning step. Therefore, the overall search dimension is expanded from four to five dimensions. To accommodate continuous space, a list of regions with calculated safe margins is maintained. Each time a neighboring region is expanded, the safe margins of neighboring regions are calculated based on the list. Based on the safe margins, the algorithm uses a five-dimensional search space (x; y; θ; t; s) for path planning for a single mobile robot. x, y, and θ are continuous and represent the robot's position and posture; t is discrete and represents time; and s is a set of time intervals representing the safe time interval during which the current robot's posture will not collide. Each task in the agent's task sequence is assigned a label corresponding to the order, and the label is used to identify the currently executed task and the number of tasks remaining to be executed.

[0061] The multi-label A* algorithm can handle multi-task single-agent path planning problems. Each task is assigned a label according to its order in the task sequence. The algorithm sets a global label for the current task. After each task is completed, the label is updated to the label of the next task until all tasks are completed. This low-level path planner can effectively solve the multi-task path planning problem caused by the insertion of transition points.

[0062] This invention solves the path planning problem for multiple mobile robots that meet the Ackermann kinematic model in narrow lanes. Each mobile robot has an independent starting and target pose. The proposed narrow lane feasible range definition and transition point setting guidance can efficiently and quickly resolve conflicts in narrow lanes. The planned path trajectories conform to the Reeds-Shepp curve, which enables reverse parking of vehicles.

[0063] The narrow lane conflict resolution strategy proposed in this paper mainly relies on the definition of the feasible range and the calculation of the transition point, such as Figure 2 As shown, the central axis is determined based on the coordinates of the four vertices within the feasible range, and the coordinate positions of the four transition points are calculated. The transition points are then divided into two groups along the central axis. The inclination angle of each transition point is calculated based on its coordinate position, ultimately determining the position and posture of each transition point. A low-level single-agent path planning algorithm is used to calculate a conflict-free path for each agent, ignoring other agents. Based on the agent's position when entering the feasible range of a narrow alleyway, the corresponding transition point is inserted before the target position of the task sequence. The transition points guide the mobile robot throughout the planning process, regulating its trajectory in narrow alleyways in accordance with traffic regulations requiring right-of-way driving, reducing the possibility of conflicts within the alleyway, and improving the algorithm's solution efficiency.

[0064] The present invention was tested in a simulation environment, and the test results are as follows: Figure 4a 、 Figure 4b In a simulation environment, the present invention can simultaneously plan paths for 16 2m×3m Ackermann model agents on a 50m×50m corridor map, with a planning time of less than 30 seconds and a success rate greater than 95%. It can also simultaneously plan paths for 12 2m×3m Ackermann model agents on a 50m×50m maze map, with a planning time of less than 15 seconds and a success rate greater than 95%.

[0065] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. 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 make equivalent replacements for some or all of the technical features therein. 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. A continuous space path planning method for a mobile robot in narrow lanes, characterized by The steps include: Step S1: constructing a kinematic model of the mobile robot; Step S2: Frame a quadrilateral formed by the feasible range of the narrow lane and obtain the four vertices of the quadrilateral; Step S3: Determine the front and rear edges in the direction of travel based on the four vertices, obtain the central axis within the feasible range by connecting the midpoints of the two edges, and set four transition points between the central axis and the left and right edges on the front and rear edges based on the distances from the central axis to the left and right edges. The transition points are used to limit the driving trajectory of the mobile robot in the narrow alleyway, where the inclination angle is calculated based on the two transition points on the same side of the central axis. Step S4: Based on the driving trajectory of the mobile robot in the single mobile robot non-conflict path, the relationship between the yaw angle when entering the narrow lane and the tilt angle is determined according to the driving rules. If the relationship satisfies the narrow lane entry condition, two transition points corresponding to the tilt angle are inserted into the task sequence; Step S5: Based on the multi-mobile robot path planning algorithm in continuous space, a conflict-free path is planned from the starting position through two transition points in the task sequence and finally reaches the target position.

2. The method for continuous spatial path planning of a mobile robot in narrow lanes according to claim 1, characterized in that: In step S1, the mobile robot is modeled according to its Ackermann kinematic model, a global map coordinate system and a robot local coordinate system are established for the movement area of ​​the mobile robot, and the starting posture state and target posture state of each mobile robot are set according to the coordinate system; the number of mobile robots is an integer greater than or equal to 2.

3. The method for continuous spatial path planning of a mobile robot in a narrow lane according to claim 2, characterized in that: By discretizing time, the posture state of the mobile robot at time t is obtained: Among them, S t represents the position state of the mobile robot at time t that satisfies the Ackerman kinematic equation, θ represents the yaw angle of the robot, that is, the angle difference between the global map coordinate system and the robot's local coordinate system, T s represents the time interval between time t and time t-1, v represents the linear velocity of the robot, L represents the distance between the front and rear wheels of the robot, Ф represents the steering angle of the front wheels of the robot. When the steering angle is fixed at Ф, the angular velocity of the robot is v / L×tanФ.

4. The method for continuous spatial path planning of a mobile robot in a narrow lane according to claim 1, characterized in that: In step S2, the coordinate positions of the four vertices are A=(x1, y1), B=(x2, y2), C=(x3, y3), and D=(x4, y4); in step S3, the coordinate positions of the four transition points are: The tilt angle is: If the denominator of the tilt angle formula is 0, the angle is 90°.

5. The method for continuous spatial path planning of a mobile robot in a narrow lane according to claim 1, characterized in that: In step S4, according to the rule of driving to the right, if the absolute value of the difference between the yaw angle and the tilt angle is less than 90°, two transition points corresponding to the tilt angle are inserted before the target position in the task sequence.

6. The method for continuous spatial path planning of a mobile robot in a narrow lane according to claim 1, characterized in that: In step S5, after obtaining the complete task sequence, the multi-mobile robot path planning algorithm is called to sequentially calculate the path from the starting position to the first transition point, the path from the first transition point to the second transition point, and the path from the second transition point to the target position.

7. The method for continuous spatial path planning of a mobile robot in narrow lanes according to claim 1, characterized in that: In step S5, a high-level multi-agent path planner performs conflict detection on the obstacle avoidance path of each mobile robot, and adds all detected conflicts to a conflict set. The high-level multi-agent path planner establishes a priority binary tree and performs a depth-first search on it. Each node in the binary tree stores a priority set, a path cost, and a path of each agent. All conflicts are aggregated by traversing the path of each agent, where the priority set and path set are inherited from the parent node, and new priorities are added and new paths are covered. The strategies for filtering conflicts include partial expansion of sub-nodes, prioritizing the resolution of target position conflicts, and adaptive influence priority. Appropriate conflicts are selected to construct priorities. The priority set planning starts with an empty set. The planned path is the shortest path set for each agent when other agents are ignored. Based on the path set, it is determined whether the agent intends to pass through the narrow alley and its driving direction in the narrow alley, thereby filtering the corresponding two transition points and inserting them into the task sequence.

8. The method for continuous spatial path planning of a mobile robot in a narrow lane according to claim 1, characterized in that: In step S5, the low-level single-agent path planner uses a multi-label A* algorithm to process the single-agent path planning of multiple tasks. Each task is assigned a corresponding label according to its order in the task sequence. A label for the current task is set globally. Every time a task is completed, the label will be updated to the label of the next task until all tasks are completed.

9. The method for continuous spatial path planning of a mobile robot in narrow lanes according to claim 8, characterized in that: In step S5, the low-level single-agent path planner adopts the SIPP algorithm based on the multi-label A* algorithm; in order to adapt to continuous space, it is necessary to maintain a region list to store the regions with obtained safety intervals, and calculate the safety intervals of the neighboring regions according to the region list each time the neighboring regions are expanded; the SIPP algorithm is based on the safety interval and uses a five-dimensional search space (x; y; θ; t; s) to plan the path of a single mobile robot, where x, y, and θ are continuous, representing the robot's coordinate position (x, y) and posture θ; t is discrete, representing time; s is a set of time intervals, representing the safe time interval in which the current robot posture will not collide; each task in the agent task sequence is assigned a label corresponding to the order, and the label is used to identify the currently executed task and the number of remaining tasks that need to be executed.

10. The method for continuous spatial path planning of a mobile robot in a narrow lane according to claim 1, characterized in that: In step S5, the actions of the expandable path nodes of the Ackermann model mobile robot supported by the low-level single-agent path planner include: forward left turn, forward straight ahead, forward right turn, backward left turn, backward straight ahead, backward right turn and waiting in place.

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