Multi-robot path planning system and method and storage medium

Abstract

The invention discloses a multi-robot path planning system and method and a storage medium. The method comprises the following steps: constructing a dynamic energy field map; calculating parameters in the dynamic energy field map by using an improved A star search algorithm, and generating space-time corridors and yielding priorities of robots; generating a local communication group by taking each robot as a center; and adjusting the moving speed of the robot according to the exchange information of the local communication group and the yielding priority. The invention relates to the technical field of robot control, and solves the technical problems that a traditional path planning method easily causes robot collision and is low in task execution efficiency in a multi-robot dense scene.

Description

Technical Field

[0001] This invention belongs to the field of robot control and relates to path planning technology, specifically a multi-robot path planning system, method and storage medium. Background Technology

[0002] With the widespread application of robotics technology in warehousing and logistics, industrial manufacturing, and other fields, collaborative operations among multiple robots have become crucial for improving efficiency. Path planning, as a core component of multi-robot systems, determines task execution efficiency and the safe operation of equipment.

[0003] Traditional path planning methods mainly focus on generating collision-free paths in two-dimensional space. In scenarios where multiple robots simultaneously enter a low-battery state, planning results that only optimize spatial distance may cause low-battery robots to lose power midway due to path congestion or excessive time consumption, or be forced to interrupt high-priority tasks because they do not prioritize heading to charging stations, resulting in an imbalance between task execution and battery safety.

[0004] When planned paths intersect, existing methods typically trigger automatic obstacle avoidance directly. However, the two robots crossing paths may have different task priorities. The lower-priority robot should be programmed to actively avoid obstacles or slow down in advance; otherwise, the higher-priority robot may be forced to interrupt or be delayed, exacerbating the disorder in the overall task execution.

[0005] Furthermore, existing methods mainly rely on a central controller to collect the status of all robots and plan paths in a unified manner. When the robot density is high and the scene is dynamically changing, the central controller needs to handle massive data interaction and calculation, which can easily cause communication delays and decision lags. Summary of the Invention

[0006] The present invention aims to solve at least one of the technical problems existing in the prior art; to this end, the present invention proposes a multi-robot path planning system, method and storage medium to solve the technical problem that traditional path planning methods are prone to robot collisions and low task execution efficiency in dense multi-robot scenarios.

[0007] To achieve the above objectives, a first aspect of the present invention provides a multi-robot path planning method, comprising: A dynamic energy field map is constructed based on the acquired robot's position coordinates, speed, remaining battery power, preset task priority, distance between the robot and the charging station, distance between the robot and the target point, and distance between robots. An improved A-star search algorithm is used to generate a spatiotemporal corridor and a robot's concession priority based on a dynamic energy field map; wherein, the spatiotemporal corridor represents the robot's planned path; Establish a local communication group and adjust the robot's movement speed based on the exchanged information and concession priorities within the local communication group.

[0008] It should be noted that the A* search algorithm is also known as the A-star algorithm.

[0009] Furthermore, the construction of the dynamic energy field map includes: The working area is divided into several equally sized grid cells, and each grid cell includes basic terrain attributes and dynamic field strength values ​​to obtain a dynamic energy field map; among them, the basic terrain attributes are used to identify whether the grid cell is passable; The dynamic field strength values ​​include the mission gravitational field, the electric repulsion field, and the traffic pressure field; among which... The mission's gravitational field The calculation formula is: , This indicates a predefined task priority. This indicates the distance between the robot and the target point. The constant for preventing division by zero is a very small constant used to prevent the denominator from being zero; i represents the robot index. The electric repulsion field The calculation formula is: , E represents the distance between robot i and the charging station, β and γ represent adjustment coefficients, and E i This indicates the remaining battery power of robot i; The traffic pressure field The calculation formula is: , Indicates the safe collision avoidance radius. This represents the distance between robot i and robot j; The dynamic field strength value The calculation formula is: , , , These represent the weight coefficients for each item, and + + =1, , , All values ​​are greater than zero, and the dynamic field strength values ​​are updated according to a preset time interval.

[0010] The task gravitational field is positively correlated with task priority and negatively correlated with target distance, ensuring that high-priority tasks and nearby targets receive stronger attraction. The energy repulsion field strengthens as energy levels decrease and the distance to charging stations shortens, guiding low-energy robots to prioritize charging stations. The traffic pressure field calculates congestion levels based on robot spacing and safety radius, with greater pressure the closer the robots are. Through quantitative modeling of multi-dimensional physical fields, task requirements, equipment status, and dynamic traffic constraints are transformed into calculable field strength values, enabling path planning to simultaneously consider task execution efficiency, energy safety, and obstacle avoidance requirements.

[0011] Furthermore, the steps for generating the spatiotemporal corridor include: The spatiotemporal nodes of the spatiotemporal corridor are defined as: the robot's coordinates (x, y), the current time t, and the current remaining battery power E. i The four-dimensional space vector formed by: (x,y,t,E) i ); The total cost of the spatiotemporal node is calculated using the improved A-star search algorithm, and the estimated arrival time and estimated remaining power of the robot are calculated based on the neighborhood space. Based on the total cost, estimated arrival time, and estimated remaining battery power of adjacent nodes, conditional constraint rules are defined, and new nodes that satisfy the constraints are retained to obtain the spatiotemporal path node sequence of robot i: ;in, Represents spacetime coordinates, Indicates the robot's path point The estimated arrival time, p=1,2,…,n; All spacetime coordinates in the spacetime path The preset spatial buffer and preset time window are merged to obtain the spatiotemporal corridor CR of robot i. i : Where s represents the radius of the preset space buffer. This indicates the half-width duration of the preset time window.

[0012] Furthermore, the definition of the condition constraint rules includes: Define the total cost f(n) for each spatiotemporal node as: This yields the improved total cost function; where, This represents the dynamic field strength value of the grid cell to which robot i belongs at time t. Indicates the preset task deadline; Define a new node based on an 8-neighborhood space expansion, and calculate the Euclidean distance d from the robot's movement to the new node. m ; The estimated arrival time t of the robot to the new node is calculated using the formula. pre and the estimated remaining power E pre : , ; Where v represents the preset speed, E th κ1 represents the preset power consumption threshold, κ2 represents the speed energy consumption coefficient, and a represents the acceleration energy consumption coefficient. Define the conditional constraint rule as follows: discard new nodes whose expected remaining power is less than the preset minimum power threshold or whose expected arrival time is greater than the preset task deadline, and obtain the filtered new nodes; At each node expansion step, the node with the lowest total cost among the new nodes after filtering is retained, and a spatiotemporal coordinate sequence is formed with the expected arrival time.

[0013] The total cost calculation formula is the core of the improved A* search algorithm, which includes spatial distance cost, remaining time cost, and dynamic field strength cost. Among them, spatial distance cost reflects the path length, remaining time cost reflects the urgency of the task deadline, and dynamic field strength cost incorporates the additional cost of implementation environment constraints, such as congestion or low battery robot costs. This allows the robot to prioritize the shortest and "compliant" path in scenarios with strict task deadlines and real-time environmental changes, such as avoiding high-congestion periods / areas, thus improving the timeliness and environmental adaptability of the planning.

[0014] Furthermore, the concession priority P i The calculation formula is: ;in, This indicates the robot's full battery level. The gradient vector represents the traffic pressure field. Indicates the modulus length. , , These represent the weight coefficients of each item, and their sum is 1.

[0015] In the formula for calculating concession priority, robots with lower battery levels, higher traffic pressure gradients in their area, and higher task priorities have higher concession priority, requiring other robots with lower concession priority to yield to them. This provides a quantitative solution for multi-robot path conflicts, enabling robots with high battery levels, low task priorities, or located in non-congested areas to proactively yield, prioritizing the passage of robots urgently needing charging, performing high-priority tasks, or located in congested core areas.

[0016] Furthermore, the construction of the local communication group includes: Centered on the robot, search for neighboring robots within a radius R and establish a local communication group; The information exchanged by the local communication group includes: the unique identifier of the neighboring robot, coordinates, current speed, remaining battery power, concession priority, and spatiotemporal path node sequence.

[0017] Furthermore, adjusting the robot's moving speed includes: Traverse the spatiotemporal path node sequence, if it exists If a node is identified as a conflict node, then it is marked as such; where, , These represent the spatiotemporal coordinates in the spatiotemporal path node sequences of the current robot and its neighboring robots, respectively. Count the number of neighboring robots with a higher concession priority than the current robot to obtain the number of high-priority neighbors. It also calculates the average speed of neighboring robots with a concession priority lower than the current robot's concession priority, thus obtaining the low-priority average speed. ; Compare the concession priority of neighboring robots that have conflicting nodes with the current robot to see if it is higher than the concession priority of the current robot; if yes, the current robot adjusts its speed according to the speed adjustment formula; if no, the neighboring robots adjust their speed according to the speed adjustment formula. The speed adjustment formula is as follows: ;in, Let represent the current speed of robot j, λ represent the deceleration coefficient, α represent the neighbor number smoothing factor, and μ represent the speed synchronization coefficient. This indicates the current adjustment speed of robot j.

[0018] When high-priority neighbors exist, the current robot will reduce its speed proportionally to the number of high-priority neighbor robots. The more [number of robots], the greater the current decrease in robot speed; and It will guide the current robot's speed to approach the average speed of its low-priority neighbors, reducing the risk of collisions and the frequency of path replanning.

[0019] A second aspect of the present invention provides a multi-robot path planning system, comprising: The energy field construction module is used to collect the robot's position coordinates, speed, and remaining power, and combine them with task priority, distance between the robot and the charging station, distance between the robot and the target point, and distance between robots to construct a dynamic energy field map; The path planning module is used to generate a spatiotemporal corridor and the robot's concession priority based on a dynamic energy field map using the A* search algorithm; wherein, the spatiotemporal corridor represents the robot's planned path; The speed adjustment module is used to establish local communication groups and adjust the robot's movement speed according to the exchanged information and concession priorities of the local communication groups.

[0020] A third aspect of the present invention provides a storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of a multi-robot path planning method as described in the first aspect above, specifically including: Collect the robot's location coordinates, speed, and remaining battery power, and combine them with task priority, distance between the robot and the charging station, distance between the robot and the target point, and distance between robots to construct a dynamic energy field map; The A-Star search algorithm is used to generate a spatiotemporal corridor and the robot's concession priority based on a dynamic energy field map; wherein, the spatiotemporal corridor represents the robot's planned path; Establish a local communication group and adjust the robot's movement speed based on the exchanged information and concession priorities within the local communication group.

[0021] Compared with the prior art, the beneficial effects of the present invention are: The constructed dynamic energy field map integrates the task's gravitational field, the energy repulsion field, and the traffic pressure field, transforming task priority, equipment status (remaining power), and real-time traffic congestion status into calculable field strength values. This enables path planning to simultaneously consider task execution efficiency, power safety, and obstacle avoidance requirements.

[0022] Meanwhile, based on the spatiotemporal corridor generated by the four-dimensional spatiotemporal nodes, the improved A-star search algorithm incorporates constraints such as task deadline and power consumption to ensure that the planned path not only meets the requirements of no spatial collision, but also avoids overlapping time windows or insufficient power, thereby improving the feasibility and timeliness of path planning in complex scenarios.

[0023] By combining a concession priority quantification model with local communication groups, an efficient collaborative mechanism is achieved whereby low-priority robots proactively yield to high-priority robots. The speed adjustment formula dynamically adjusts the movement speed based on the number of high-priority neighbors, which avoids disorderly competition when multiple robot paths intersects and reduces energy waste and decision lag caused by frequent sudden stops / accelerations.

[0024] Furthermore, local communication groups do not require global central control; real-time speed coordination can be achieved solely through local information exchange within a radius R, significantly reducing communication load and computational complexity. This makes them suitable for high-density robot swarm scenarios such as warehousing and logistics, and emergency rescue. In addition, quantified conflict resolution rules significantly improve system robustness, ensuring that multiple robots maintain high efficiency and reliability in task execution in dynamically changing environments. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 This is a schematic diagram of the framework of a multi-robot path planning system provided by the present invention; Figure 2 A flowchart illustrating a multi-robot path planning method provided by the present invention; Figure 3 This is a schematic diagram of the process for constructing a spacetime corridor provided by the present invention. Detailed Implementation

[0027] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0028] The multi-robot path planning method provided in this application embodiment can be applied to, for example... Figure 1 The multi-robot path planning system shown is an example. Figure 1 As shown, the system includes: an energy field construction module, a path planning module, and a speed adjustment module.

[0029] The energy field construction module is used to construct a dynamic energy field map based on the acquired robot's position coordinates, speed, remaining battery power, and in combination with preset task priorities, distance between the robot and the charging station, distance between the robot and the target point, and robot spacing. The path planning module is used to calculate the parameters in the dynamic energy field map using the improved A-Star search algorithm, and generate the spatiotemporal corridor and the robot's concession priority; where the spatiotemporal corridor represents the robot's planned path; the improved A-Star search algorithm is obtained by improving the A-Star search algorithm based on the distance between the robot and the target point, the preset task deadline, and the dynamic field strength value obtained from the dynamic energy field; The speed adjustment module is used to establish a local communication group centered on each robot, and to adjust the robot's movement speed according to the exchanged information and concession priority of the local communication group; wherein, the local communication group is used for local communication among multiple robots within the group.

[0030] It should be noted that the path planning system also includes a data acquisition module, which is used to obtain the position coordinates, speed, remaining power, task priority, distance to charging station, distance to target point and distance between adjacent robots in real time through LiDAR, GPS positioning system and power sensor.

[0031] To address the path conflicts and inefficiencies caused by task priorities, battery status, and traffic congestion among multiple robots in intensive work scenarios, this application provides a multi-robot path planning method. This method achieves coordinated optimization of path planning and real-time speed adjustment through multi-dimensional field strength modeling and a distributed collaboration mechanism.

[0032] like Figure 2 As shown, the multi-robot path planning method provided in this application includes the following steps: S1. Acquire robot status data and build a basic map.

[0033] S101. Hardware Initialization and Parameter Configuration: Each robot starts its sensor system (such as LiDAR, Inertial Measurement Unit, IMU) and communication module, and initializes system parameters, such as grid size 0.5m × 0.5m, communication radius R = 10m, and safety distance. =1.5m, etc.

[0034] S102. Environmental Perception and Basic Map Construction: The data acquisition module generates an environmental raster map G(x,y) using Simultaneous Localization and Mapping (SLAM) technology, marking obstacle areas as 1 and passable areas as 0, thus obtaining the basic terrain attributes of each raster unit. The basic map is the foundation for constructing the dynamic energy field map and defines the basic unit attributes of each raster unit.

[0035] S103. Task Assignment and Priority Setting: Based on the task type, such as important transportation, emergency transportation, and routine transportation, set different task priorities for the robots. task The task deadlines are set manually based on the distance to the target and the urgency of the task, ∈[1,5], to obtain the preset task deadlines; the task priority division is shown in Table 1: Table 1, Example of Task Priority Level Classification:

[0036] S2 constructs a dynamic energy field map based on the base map. Specifically, it includes: S201. Work Area Meshing and Attribute Initialization: The work area is divided into uniform grids G(x,y), each storing basic terrain attributes, field strength values, and a timestamp. It should be noted that the grid size needs to be adjusted according to the robot's dimensions and motion accuracy; too small a grid will increase computational load, while too large a grid will reduce path accuracy. The default grid size range is 100m-500m.

[0037] S202. Calculate the multidimensional field strength value: Mission Gravity Field The attractiveness of the reaction task is calculated using the following formula: , This indicates a predefined task priority. This indicates the distance between the robot and the target point. This represents the zero-prevention constant, which is an extremely small constant used to prevent the denominator from being zero. It is usually 0.0001, and i represents the robot index. Electric repulsion field The formula for guiding a robot with low battery to a charging station is as follows: , This represents the distance between the robot and the charging station. β represents the adjustment coefficient, and γ represents the distance attenuation coefficient, both determined through practical experience. The default values ​​are β=0.5 and γ=0.2. i This indicates the remaining battery power of robot i; Traffic pressure field : Reflects the degree of regional congestion, calculated using the following formula: , Indicates the safe collision avoidance radius. This represents the distance between robot i and robot j.

[0038] S203. Synthetic Dynamic Field Strength Value: The total dynamic field strength value is calculated using a weighted fusion formula and updated according to a preset time interval (e.g., 5 seconds). , , , These represent the weight coefficients for each item, and + + =1, , , All are non-zero, and the default value is: , , It can also be dynamically adjusted according to the scenario.

[0039] S3. Utilize the improved A-star search algorithm to calculate the parameters in the dynamic energy field map, and generate the spatiotemporal corridor and the robot's concession priority.

[0040] like Figure 3 As shown, step S3 may specifically include: S301. Calculate the total cost of the spatiotemporal node using the improved A-star search algorithm, and calculate the estimated arrival time and estimated remaining power of the robot when moving to the new node based on the neighborhood space.

[0041] Define a four-dimensional spatiotemporal node and a cost function. The spatiotemporal node is represented as (x, y, t, E). i ), where t is the timestamp and E is the remaining battery power. The total cost function of the improved A-satellite search algorithm is: ;in, This represents the dynamic field strength value of the grid cell to which robot i belongs at the current time t. Indicates the preset task deadline; Define a new node based on its neighborhood space expansion, and calculate the Euclidean distance d from the robot's movement to the new node. m ; Among them, the neighborhood space represents the adjacent spatial range that can be directly moved to and reached on the dynamic energy field map with the current spatiotemporal node as the center. For example, the neighborhood space can adopt an 8-field expansion strategy, that is, each node can expand to 8 adjacent grid nodes in the directions of up, down, left, right and four diagonals. During node expansion, the estimated arrival time t of the robot to the new node is calculated according to the formula. pre and the estimated remaining power E pre : , ; Where v represents the preset speed, E th The preset power threshold is represented by the power unit J. κ1 represents the speed energy consumption coefficient, with the unit being J / m, i.e., joules per meter. κ2 represents the acceleration energy consumption coefficient, with the unit being J·s / m, i.e., joules × seconds per meter. All of these are determined based on practical experience. a represents acceleration.

[0042] It should be noted that in engineering scenarios, battery “charge” often refers to “electrical energy”, which can be expressed in joules (J) or kilowatt-hours (kWh). Therefore, in this application, the charge is expressed in joules.

[0043] S302, discard new nodes whose expected remaining power is less than the preset minimum power threshold or whose expected arrival time is greater than the task deadline, and obtain the filtered new nodes.

[0044] S303, during each node expansion step, retain the node with the lowest total cost among the filtered new nodes, and form a spatiotemporal coordinate sequence with the expected arrival time: ,in, Represents spacetime coordinates, Indicates the robot's path point The estimated arrival times, p=1,2,…,n.

[0045] S304, merge the preset spatial buffer and preset time window of all spatiotemporal coordinates in the spatiotemporal path to obtain the spatiotemporal corridor CR of robot i. i .

[0046] The generated path node sequence is passed through a spatial buffer s=0.5m and a time window. =3s is expanded into a spacetime corridor, resulting in the spacetime corridor CR of robot i. i : Where s represents the radius of the preset space buffer. This indicates the half-width duration of the preset time window.

[0047] It should be noted that if there are multiple new nodes with the lowest total cost after screening, they can be further determined according to the principle of shortest time, or according to the principle of shortest distance or most remaining power. The specific screening rules can be set by the staff.

[0048] For example, suppose we have a 4×4 two-dimensional grid map, the robot's initial coordinates are (0,0), it needs to move to (3,3), and its initial battery power is 100. Therefore, The robot's initial node is: Target point: (3,3), assuming a preset task deadline. =10 seconds.

[0049] Preset parameters: speed v = 1 m / s, preset battery threshold E th =20, speed energy consumption coefficient =1, acceleration a=0 (simplifying energy consumption calculation); Cost function weights: =0.4, =0.3, =0.3; and assume the dynamic field strength value of all passable grids. =0.5, which simplifies calculations.

[0050] Therefore, the steps for generating a spacetime corridor include: Take the node expansion in the first and second steps as an example.

[0051] Step 1: Node Expansion. Starting from the initial coordinates (0,0,0), expand to 8 neighboring nodes. The nodes can move to coordinates (1,0), (0,1), and (1,1). Calculate the estimated arrival time and remaining battery power for each node. Candidate node 1 (1,0): movement distance d m=1m, initial time t0=0, estimated arrival time t pre =t0+d m / v=0+1 / 1=1s, estimated remaining battery power =100-1×1×1=99, in the total cost formula, the distance to the target point ≈3.606m, therefore, the total cost f(n) = 0.4 × 3.606 + 0.3 × (10-1) + 0.3 × 0.5 ≈ 1.442 + 2.7 + 0.15 = 4.292; It should be noted that since the total cost of candidate node 1 is being calculated at this point, t in the total cost calculation formula represents the time it takes for the robot to move to candidate node 1, i.e., the estimated time t to reach candidate node 1. pre .

[0052] Candidate node 2(0,1): Symmetric to candidate node 1, the calculation result is the same; Candidate node 3(1,1): distance moved m, estimated arrival time s, estimated remaining power Distance from the target point m, the total cost f(n) = 0.4 × 2.828 + 0.3 × (10 - 1.414) + 0.3 × 0.5 ≈ 1.131 + 2.576 + 0.15 = 3.857; Filtering and cost ranking: No node power is lower than or time exceeds Therefore, all nodes are retained; and after sorting by total cost, the node with the lowest cost (1,1,1.414,) is selected and added to the path.

[0053] The second step is node expansion: Starting from coordinates (1,1), expand 8 neighboring nodes, excluding those already expanded in the first step, resulting in movable coordinates including (1,2), (0,2), (2,0), (2,1), and (2,2); similarly, continue calculating the estimated arrival time and remaining power of each node, filtering out those with less than [time / power]... or time exceeds The nodes are sorted according to their total cost, and the node with the lowest total cost in this step is added to the path.

[0054] Assume the spatiotemporal path node sequence is as follows: Then, for each spatiotemporal coordinate, the spatial buffer and time window are expanded to obtain the spatiotemporal corridor.

[0055] It should be noted that the spatiotemporal corridor is the result of the robot's path planning. It upgrades the traditional path planning goal of "no spatial collision" to a multi-dimensional optimization of "spatiotemporal-power-task priority". It effectively solves the problem of low efficiency caused by multiple robots in intensive operation scenarios due to task conflicts, insufficient power and traffic congestion, and has both efficiency and engineering practicality.

[0056] S4. Establish a local communication group and adjust the speed in real time based on the exchanged information and concession priority of the local communication group.

[0057] By avoiding path conflicts through local coordination, the passage of high-priority robots can be guaranteed.

[0058] S401, Local communication group establishment and information exchange.

[0059] Each robot searches for neighboring robots within a radius R = 10m, centered on itself, and establishes a local communication group. It periodically broadcasts status messages (Messages) via the MQTT protocol. Where ID represents the unique identifier of the neighboring robot, and (x,y) represents the coordinates of the neighboring robot. and Let represent the linear velocity and angular velocity of the neighboring robot, respectively; E represent the remaining battery power; path represent the spatiotemporal path node sequence; and P represent the concession priority. Represents a timestamp.

[0060] It should be noted that the concession priority P of the i-th robot i The formula is calculated based on the mission's gravitational field, electric repulsion field, and traffic pressure field: Among them, E i This indicates the remaining battery power of robot i. This indicates the robot's full battery level. The gradient vector represents the traffic pressure field. Indicates the modulus length. , , These represent the weight coefficients of each item, and their sum is 1. The default value is determined based on practical experience. =0.3, =0.4, =0.3, ensuring that robots with low battery, high task priority, or in congested areas receive higher priority.

[0061] S402. Collision Detection and Priority Comparison: Traverse the spatiotemporal corridors of all robots within the local communication group. If there are overlapping regions, i.e.: Then (x,y,t) is marked as a conflict node; where CR i CR represents the spacetime corridor of robot i. j This represents the spacetime corridor of robot j.

[0062] S403, Dynamic Speed ​​Adjustment and Conflict Resolution Count the number of neighboring robots with a higher concession priority than the current robot to obtain the number of high-priority neighbors. It also calculates the average speed of neighboring robots with a concession priority lower than the current robot's concession priority, thus obtaining the low-priority average speed. ; If a conflict with a high-priority neighbor is detected, the robot adjusts its speed according to the following formula: ;in, This represents the current speed of robot j. λ represents the current adjustment speed of robot j, α represents the deceleration coefficient, α represents the neighbor number smoothing factor, and μ represents the speed synchronization coefficient. Each coefficient is determined based on practical experience, and the default values ​​are: λ=0.2, α=1, μ=0.1.

[0063] For example, when two high-priority neighbors are detected, and the neighboring robot with the conflicting node also has a higher concession priority than the current robot, assuming the current robot's speed... =1.5m / s, the average speed of low-priority neighbors =1.2m / s, then the current robot speed drops to: The system uses the average speed of low-priority neighbors for speed synchronization adjustment, which avoids conflicts with high-priority neighbors while maintaining the overall operating rhythm of the system and ensuring the coordination and efficiency of multi-robot path planning.

[0064] Through the above steps, a balance is achieved between task execution efficiency, power safety, and obstacle avoidance requirements for multiple robots, making it suitable for high-density scenarios such as warehousing and logistics and emergency rescue, ensuring efficient collaboration and robustness in dynamic environments.

[0065] This application also provides a computer-readable storage medium including instructions that, when run on a computer, cause the computer to perform each of the methods described above.

[0066] This application also provides a computer program product containing instructions that, when run on a computer, cause the computer to perform each of the methods described above.

[0067] This application also provides a chip including a processor and an interface circuit. The interface circuit is coupled to the processor. The processor is used to run computer programs or instructions to implement the above-described method. The interface circuit is used to communicate with other modules outside the chip.

[0068] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented using software programs, implementation can be, in whole or in part, in the form of a computer program product. This computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another via wired (e.g., coaxial cable, fiber optic, digital subscriber line, DSL) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or containing one or more data storage devices / modules such as servers and data centers that can be integrated with the medium. The available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid-state disks, or SSDs).

[0069] Some of the data in the above formula are calculated by removing dimensions and taking their numerical values. The formula is the closest to the real situation obtained by software simulation of a large amount of collected data. The preset parameters and preset thresholds in the formula are set by those skilled in the art according to the actual situation or obtained through simulation of a large amount of data.

[0070] Working principle of the invention: Collect robot status data, divide the work area into grids, construct a dynamic energy field map containing task gravitational field, electric repulsive field and traffic pressure field, and quantify task requirements, equipment status and real-time traffic constraints. Based on a dynamic energy field map, an improved A-satellite search algorithm is used to generate a "spatiotemporal corridor". Each path node includes spatial coordinates, estimated arrival time, and remaining power. By filtering and discarding nodes with insufficient power or exceeding the timeout, a feasible path is formed that takes into account spatial, temporal, and power constraints.

[0071] Based on the robot's battery status, task priority, and traffic pressure in the area, the concession priority is calculated to determine the passage order in case of conflict. The robots share their state and spatiotemporal paths through local communication groups. When a path conflict is detected, the low-priority robot actively adjusts its speed to give way to the high-priority robot, thus avoiding collisions and reducing the frequency of replanning.

[0072] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A path planning method for multiple robots, characterized in that, include: A dynamic energy field map is constructed based on the acquired robot's position coordinates, speed, remaining battery power, preset task priority, distance between the robot and the charging station, distance between the robot and the target point, and distance between robots. An improved A-Star search algorithm is used to calculate parameters in a dynamic energy field map to generate a spatiotemporal corridor and a robot's concession priority; wherein, the spatiotemporal corridor represents the robot's planned path; the improved A-Star search algorithm is obtained by improving the total cost function in the A-Star search algorithm based on the distance between the robot and the target point, the preset task deadline, and the dynamic field strength value obtained from the dynamic energy field; A local communication group is generated centered on each robot, and the local communication group is used for local communication among multiple robots centered on each robot. The robot's movement speed is adjusted based on the exchanged information and concession priorities of the local communication group.

2. The multi-robot path planning method according to claim 1, characterized in that, The construction of the dynamic energy field map includes: The work area is divided into several equally sized grid units; Based on the task priority in the grid cell and the distance between the robot and the target point, the task gravitational field of each grid cell is determined. Based on the remaining charge in the grid cell and the distance between the robot and the charging station, the charge repulsion field of each grid cell is determined; Based on the robot spacing in the grid cells, the traffic pressure field of each grid cell is determined; The dynamic field strength value of each grid cell is determined by weighted summation of the task gravitational field, electric repulsion field, and traffic pressure field. Obtain the basic terrain attributes of each grid cell; the basic terrain attributes are used to identify whether the grid cell is passable. Determine the dynamic field strength value and basic terrain attributes of each grid cell in the dynamic energy site map.

3. The multi-robot path planning method according to claim 2, characterized in that, The mission's gravitational field The formula for calculation is: , This indicates the predefined task priority for robot i. This represents the distance between robot i and the target point. This represents a zero-prevention constant, used to prevent the denominator from being zero; The electric repulsion field The formula for calculation is: , E represents the distance between robot i and the charging station, β and γ represent adjustment coefficients, and E i This indicates the remaining battery power of robot i; The traffic pressure field The formula for calculation is: , Indicates the safe collision avoidance radius. This represents the distance between robot i and robot j; The dynamic field strength value The formula for calculation is: ;in, , , These represent the weight coefficients for each item, and + + =1, , , All values ​​are greater than zero, and the dynamic field strength value is updated according to a preset time interval.

4. The multi-robot path planning method according to claim 2, characterized in that, The steps for generating the spacetime corridor include: The total cost of the spatiotemporal node is calculated using an improved A-star search algorithm, and the estimated arrival time and estimated remaining power of the robot to move to the new node are calculated based on the neighborhood space; wherein, the neighborhood space represents the adjacent spatial range that can be directly reached on the dynamic energy field map with the current spatiotemporal node as the center. Determine the conditional constraint rules used to constrain the total cost, estimated arrival time, and estimated remaining power of adjacent nodes; Retaining the new nodes that satisfy the conditional constraints, we obtain the spatiotemporal path node sequence of robot i: ;in, ∈ Represents spacetime coordinates, Indicates the robot's path point The estimated arrival time, p=1,2,…,n; All spacetime coordinates in the spacetime path The preset spatial buffer and preset time window are merged to obtain the spatiotemporal corridor CR of robot i. i : Where s represents the radius of the preset space buffer. This indicates the half-width duration of the preset time window.

5. The multi-robot path planning method according to claim 4, characterized in that, The retention of new nodes that satisfy the conditional constraint rules includes: Define the total cost f(n) for each spatiotemporal node as: Where t represents time, This represents the dynamic field strength value of the grid cell to which robot i belongs at time t. This represents the preset task deadline, and θ1, θ2, and θ3 represent the weight coefficients of each item, with a sum of 1. Define a new node based on an 8-neighborhood space expansion, and calculate the Euclidean distance d from the robot's movement to the new node. m ; The estimated arrival time t of the robot moving to the new node pre and the estimated remaining power E pre The calculation formulas are as follows: ， ； Among them, t current This represents the current time, v represents the preset speed, and E represents the current speed. i E represents the remaining battery power of robot i. th κ1 represents the preset power consumption threshold, κ2 represents the speed energy consumption coefficient, and a represents the acceleration energy consumption coefficient. The conditional constraint rule is: discard new nodes whose expected remaining power is less than the preset minimum power threshold or whose expected arrival time is greater than the preset task deadline, and obtain the filtered new nodes. At each node expansion step, the node with the lowest total cost among the new nodes after filtering is retained, and a spatiotemporal coordinate sequence is formed with the expected arrival time.

6. The multi-robot path planning method according to claim 3, characterized in that, The concession priority is used to characterize the order in which robots make concessions in a multi-robot scenario, and the concession priority P i The formula for calculation is: ;in, This indicates the robot's full battery level. The gradient vector represents the traffic pressure field. Indicates the modulus length. , , These represent the weight coefficients of each item, and their sum is 1.

7. The multi-robot path planning method according to claim 4, characterized in that, The construction of the local communication group includes: Centered on the robot, search for neighboring robots within a radius R and establish a local communication group; The information exchanged by the local communication group includes: the unique identifier of the neighboring robot, coordinates, current speed, remaining battery power, concession priority, and spatiotemporal path node sequence.

8. The multi-robot path planning method according to claim 7, characterized in that, Adjusting the robot's moving speed includes: Traverse the spatiotemporal path node sequence, if it exists Then Nodes marked as conflicting nodes; among them, This represents the spatiotemporal coordinates in the spatiotemporal path node sequence of the current robot j. Represents the spatiotemporal coordinates in the spatiotemporal path node sequence of neighboring robot k; Count the number of neighboring robots with a higher concession priority than the current robot to obtain the number of high-priority neighbors. It also calculates the average speed of neighboring robots with a concession priority lower than the current robot's concession priority, thus obtaining the low-priority average speed. ; Compare the concession priority of neighboring robots that have conflicting nodes with the current robot to see if it is higher than the concession priority of the current robot; if yes, the current robot adjusts its speed according to the speed adjustment formula; if no, the neighboring robots adjust their speed according to the speed adjustment formula. The speed adjustment formula is as follows: ;in, Let represent the current speed of robot j, λ represent the deceleration coefficient, α represent the neighbor number smoothing factor, and μ represent the speed synchronization coefficient. This indicates the current adjustment speed of robot j.

9. A multi-robot path planning system, characterized in that, include: The energy field construction module is used to build a dynamic energy field map based on the acquired robot's position coordinates, speed, remaining battery power, preset task priority, distance between the robot and the charging station, distance between the robot and the target point, and robot spacing. The dynamic energy field is used to characterize the relationship between one or more of the acquired parameters; The path planning module is used to calculate parameters in the dynamic energy field map using an improved A-star search algorithm to generate a spatiotemporal corridor and the robot's concession priority; wherein, the spatiotemporal corridor represents the robot's planned path; the improved A-star search algorithm is obtained by improving the A-star search algorithm based on the distance between the robot and the target point, the preset task deadline, and the dynamic field strength value obtained from the dynamic energy field; The speed adjustment module is used to establish a local communication group for each robot and adjust the robot's movement speed according to the exchanged information and concession priority of the local communication group; the local communication group is used for local communication between multiple robots centered on each robot.

10. A storage medium, characterized in that, The system contains a computer program that, when executed by a processor, implements a multi-robot path planning method as described in each of claims 1-8.

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