A multi-robot path planning system, method and storage medium

Abstract

The application discloses a multi-robot path planning system and method and a storage medium, and 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 to generate a time-space corridor and a yielding priority of a robot; generating a local communication group with each robot as a center; and adjusting a moving speed of the robot according to exchanged information of the local communication group and the yielding priority. The application relates to the technical field of robot control, and solves the technical problems that a traditional path planning method is prone to causing robot collision and low task execution efficiency in a multi-robot dense scene.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of robot control, and relates to path planning technology, in particular to a multi-robot path planning system and method and storage medium. BACKGROUND

[0002] With the wide application of robot technology in the fields of warehouse logistics and industrial manufacturing, multi-robot collaborative work has become the key to improving efficiency. Path planning, as the core of the multi-robot system, determines the efficiency of task execution and the safe operation state of the equipment.

[0003] Traditional path planning methods mainly focus on collision-free path generation in two-dimensional space. In the scenario where multiple robots enter a low power state at the same time, the planning result with only spatial distance as the optimization target may cause the low power robot to be powered off midway due to path congestion or excessive time consumption, or be forced to interrupt high priority tasks due to not prioritizing driving to the charging station, resulting in an imbalance between task execution and power safety.

[0004] When the planned paths intersect, the existing method usually triggers automatic obstacle avoidance directly. However, the two robots with intersecting paths may also have different task priorities, and the low priority robot should be set to actively avoid or slow down in advance, otherwise the high priority task robot will be forced to interrupt or delay, exacerbating the disorder of overall task execution.

[0005] Moreover, the existing method mainly relies on a central controller to collect the states of all robots and plan paths uniformly. In a high-density robot scenario with dynamic changes, the central controller needs to handle massive data interaction and calculation, which may cause communication delay and decision lag. SUMMARY

[0006] The present application aims to at least solve one of the technical problems existing in the prior art; for this purpose, the present application proposes a multi-robot path planning system and method and storage medium to solve the technical problem that traditional path planning methods easily cause robot collision and low task execution efficiency in a multi-robot dense scenario.

[0007] To achieve the above-mentioned purpose, the first aspect of the present application provides a multi-robot path planning method, comprising:

[0008] constructing a dynamic energy field map based on the obtained position coordinates, speed, remaining power of the robots, preset task priority, distance between the robots and the charging station, distance between the robots and the target point, and robot spacing;

[0009] generating a time-space corridor and a yielding priority of the robots based on the dynamic energy field map using an improved A-star search algorithm; wherein the time-space corridor represents the planned path of the robots;

[0010] establish a local communication group, and adjust the moving speed of the robot according to the exchanged information of the local communication group and the yielding priority.

[0011] It should be noted that the A-star search algorithm is A-star Algorithm.

[0012] Further, the construction of the dynamic energy field map comprises:

[0013] dividing the working area into a plurality of equal grid units, and each grid unit comprises a basic terrain attribute and a dynamic field strength value, to obtain a dynamic energy field map; wherein the basic terrain attribute is used to identify whether the grid unit can be passed through;

[0014] The dynamic field strength value comprises a task gravitational field, a power repulsive field and a traffic pressure field; wherein,

[0015] The calculation formula of the task gravitational field is:

[0016] , represents a predefined task priority, represents the distance between the robot and the target point, represents a constant for preventing the denominator from being zero, and i represents the index of the robot;

[0017] The calculation formula of the power repulsive field is:

[0018] , represents the distance between the robot i and the charging station, β and γ represent adjustment coefficients, and E i represents the remaining power of the robot i;

[0019] The calculation formula of the traffic pressure field is:

[0020] , represents a safe collision avoidance radius, represents the distance between the robot i and the robot j;

[0021] The calculation formula of the dynamic field strength value is:

[0022] , , , respectively represent each weight coefficient, and =1,​​​​​​ 、 、 are greater than zero, and the dynamic field strength value is updated according to a preset time interval.

[0023] The task gravitational field is positively correlated with the task priority and negatively correlated with the target distance, so as to ensure that high-priority tasks and close-range targets obtain stronger attraction; the power repulsive field is enhanced with the decrease of the power and the shortening of the distance to the charging station, so as to guide the low-power robot to preferentially drive to the charging station; the traffic pressure field calculates the congestion degree based on the distance between robots and the safety radius, and the closer the distance, the greater the pressure. Through the quantitative modeling of the multidimensional physical field, the task demand, the device state and the dynamic traffic constraint are converted into a calculable field strength value, so that the path planning can simultaneously consider the task execution efficiency, the power safety and the obstacle avoidance demand.

[0024] Further, the step of generating the space-time corridor comprises:

[0025] The space-time node of the space-time corridor is defined as: the coordinates (x, y) of the robot, the current time t, and the current remaining power E i of the robot, which constitute a four-dimensional space vector: (x, y, t, E i );

[0026] The total cost of the space-time node is calculated by the improved A-star search algorithm, and the expected arrival time and the expected remaining power of the robot moving to the new node are calculated based on the neighborhood space;

[0027] The conditional constraint rule is defined based on the total cost, the expected arrival time and the expected remaining power of the adjacent nodes, the new nodes meeting the constraint are retained, and the space-time path node sequence of the robot i is obtained:

[0028] ; wherein, represents the space-time coordinate, represents the expected arrival time of the robot to the path point , and p = 1, 2, …, n;

[0029] The preset space buffer and the preset time window of all space-time coordinates in the space-time path are merged to obtain the space-time corridor CR i of the robot i:

[0030] ; wherein, s represents the radius of the preset space buffer, represents the half-width time length of the preset time window.

[0031] Further, the definition of the conditional constraint rule comprises:

[0032] The total cost f(n) of each space-time node is defined as:

[0033] , obtaining an improved total cost function; wherein, represents the dynamic field strength value of the grid cell to which the robot i belongs at time t, represents the preset task deadline;

[0034] Define the new node based on the 8-neighborhood spatial expansion, and calculate the Euclidean distance of the robot moving to the new node to obtain the moving distance d m ;

[0035] According to the formula, the predicted arrival time t pre of the robot to the new node and the predicted remaining power E pre are calculated:

[0036] , ; wherein, v represents the preset speed, E th represents the preset power threshold, κ1 represents the speed energy consumption coefficient, κ2 represents the acceleration energy consumption coefficient, and a represents the acceleration;

[0037] Define the conditional constraint rule as: discard the new node whose predicted remaining power is less than the preset minimum power threshold or whose predicted arrival time is greater than the preset task deadline, to obtain the screened new node;

[0038] At each step of node expansion, the node with the lowest total cost in the screened new node is retained, and a space-time coordinate sequence is formed with the predicted arrival time.

[0039] The total cost calculation formula is the core of the improved A-star search algorithm, which includes the spatial distance cost, the time remaining cost, and the dynamic field strength cost. Among them, the spatial distance cost reflects the path length, the time remaining cost embodies the urgency of the task deadline, and the dynamic field strength cost includes the implementation environment constraints, such as the additional cost of congestion or low power robots, so that the robot can preferentially select a path that is both short and “compliant” in a strict task deadline and real-time changing environment, such as avoiding high congestion periods / areas, to improve the timeliness and environmental adaptability of planning.

[0040] Further, the calculation formula of the concession priority P i is:

[0041] ; wherein, represents the full power value of the robot, represents the gradient vector of the traffic pressure field, represents the module length, , , respectively represent the weight coefficients, and the sum is 1.

[0042] In the calculation formula of the yielding priority, the robot with lower power, higher traffic pressure gradient of the area and higher task priority has higher yielding priority, and needs other robots with lower yielding priority to yield. The quantitative solution is provided for the multi-robot path conflict, so that the robot with high power, low task priority or in non-congestion area actively yields, and the robot with urgent need for charging, high priority task or in congestion core area is prioritized.

[0043] Further, the construction of the local communication group comprises:

[0044] In the robot as the center, the neighbor robots are searched within the radius R, and the local communication group is constructed.

[0045] The exchanged information of the local communication group comprises: the unique identifier, coordinates, current speed, remaining power, yielding priority and space-time path node sequence of the neighbor robots.

[0046] Further, the adjustment of the moving speed of the robot comprises:

[0047] The space-time path node sequence is traversed, and if there is , the conflict node is marked; wherein, , represent the space-time coordinates in the space-time path node sequence of the current robot and the neighbor robot, respectively.

[0048] The number of neighbor robots with yielding priority higher than that of the current robot is counted, and the high-priority neighbor number is obtained, and the average speed of the neighbor robots with yielding priority lower than that of the current robot is counted, and the low-priority average speed is obtained.

[0049] Whether the yielding priority of the neighbor robot with the conflict node with the current robot is higher than that of the current robot is compared; if yes, the current robot adjusts the speed according to the speed adjustment formula; if no, the neighbor robot adjusts the speed according to the speed adjustment formula.

[0050] The speed adjustment formula is: ; wherein, represents the current speed of the current robot j, λ represents the deceleration coefficient, α represents the neighbor number smoothing factor, μ represents the speed synchronization coefficient, represents the adjusted speed of the current robot j.

[0051] When the high-priority neighbor exists, the current robot will proportionally reduce the speed according to the number of the high-priority neighbor, and the more the number of the high-priority neighbor robot , the greater the speed reduction range of the current robot; and The current robot's speed will be guided to the average speed of the low-priority neighbor, reducing the collision risk and the frequency of path re-planning.

[0052] The second aspect of the application provides a multi-robot path planning system, comprising:

[0053] An energy field construction module is configured to collect position coordinates, speed and remaining power of the robots, and construct a dynamic energy field map in combination with task priority, distance between the robots and charging stations, distance between the robots and target points, and robot spacing;

[0054] A path planning module is configured to generate a time-space corridor and yielding priority of the robots based on the dynamic energy field map by using an A-star search algorithm; wherein the time-space corridor represents a planned path of the robots.

[0055] A speed adjustment module is configured to establish a local communication group, and adjust the moving speed of the robots according to exchanged information and yielding priority of the local communication group.

[0056] The third aspect of the application provides a storage medium having a computer program stored thereon, wherein the computer program is executed by a processor to implement the steps of the multi-robot path planning method according to the first aspect, and specifically comprises:

[0057] Collecting position coordinates, speed and remaining power of the robots, and constructing a dynamic energy field map in combination with task priority, distance between the robots and charging stations, distance between the robots and target points, and robot spacing;

[0058] Generating a time-space corridor and yielding priority of the robots based on the dynamic energy field map by using an A-star search algorithm; wherein the time-space corridor represents a planned path of the robots.

[0059] Establishing a local communication group, and adjusting the moving speed of the robots according to exchanged information and yielding priority of the local communication group.

[0060] Compared with the prior art, the application has the following beneficial effects:

[0061] The dynamic energy field map constructed by the application integrates a task gravitational field, a power repulsive field and a traffic pressure field, and converts task priority, device state (remaining power) and real-time traffic congestion state into calculable field strength values, so that the path planning can simultaneously consider task execution efficiency, power safety and obstacle avoidance requirements.

[0062] Meanwhile, the time-space corridor generated based on four-dimensional time-space nodes is incorporated into task deadline and power consumption constraints by using an improved A-star search algorithm, so as to ensure that the planned path not only meets the space collision-free requirement, but also avoids time window overlap or power shortage, and can improve the feasibility and timeliness of path planning in complex scenarios.

[0063] 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.

[0064] 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

[0065] 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.

[0066] Figure 1 This is a schematic diagram of the framework of a multi-robot path planning system provided by the present invention;

[0067] Figure 2 A flowchart illustrating a multi-robot path planning method provided by the present invention;

[0068] Figure 3 This is a schematic diagram of the process for constructing a spacetime corridor provided by the present invention. Detailed Implementation

[0069] 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.

[0070] 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.

[0071] An energy field construction module is configured to construct a dynamic energy field map based on the acquired position coordinates, speed and remaining power of the robots, in combination with preset task priorities, distances between the robots and the charging stations, distances between the robots and the target points, and robot spacings.

[0072] A path planning module is configured to calculate parameters in the dynamic energy field map by using an improved A-star search algorithm to generate a time-space corridor and a yielding priority of the robots, wherein the time-space corridor represents a planned path of the robots, and the improved A-star search algorithm is obtained by improving the A-star search algorithm based on the distances between the robots and the target points, preset task deadlines, and dynamic field strength values obtained from the dynamic energy field.

[0073] A speed adjustment module is configured to establish a local communication group with each robot as a center, and to adjust a moving speed of the robot according to exchanged information and the yielding priority of the local communication group, wherein the local communication group is used for local communication among multiple robots in the group.

[0074] It should be noted that the path planning system further includes a data acquisition module configured to acquire, in real time, position coordinates, speed, remaining power, task priorities, distances to the charging stations, distances to the target points, and robot spacings of the robots by using a laser radar, a GPS positioning system, and a power sensor.

[0075] To solve the path conflict and low efficiency of the multiple robots in a dense operation scenario due to task priorities, power states and traffic congestion, an embodiment of the present application provides a multiple robot path planning method, which realizes cooperative optimization of path planning and real-time speed adjustment through multi-dimensional field strength modeling and a distributed cooperative mechanism.

[0076] As shown in FIG. 1, Figure 2 the multiple robot path planning method provided by the embodiment of the present application includes the following steps:

[0077] S1, acquiring robot state data and constructing a basic map.

[0078] S101, hardware initialization and parameter configuration: starting a sensor system (such as a laser radar, an inertial measurement unit IMU) and a communication module of each robot, and initializing system parameters, such as a grid size of 0.5m x 0.5m, a communication radius R = 10m, a safety distance = 1.5m, etc.

[0079] S102, environment perception and basic map construction: the data acquisition module generates an environment grid map G(x, y) through a simultaneous localization and mapping (SLAM) technology, and marks obstacle areas as 1 and passable areas as 0, to obtain the basic terrain attributes of each grid unit. The basic map is the basis for constructing the dynamic energy field map, and defines the basic unit attributes of each grid unit.

[0080] S103, task allocation and priority setting: according to the task type, such as important transportation, emergency transportation, and regular transportation, the task priority Q of different robots is set task ∈[1, 5], and the task deadline of the current task is set according to the target distance and the task urgency, to obtain the preset task deadline; wherein the task priority division can be as shown in Table 1:

[0081] Table 1, task priority level division example table:

[0082]

[0083] S2, constructing a dynamic energy field map based on a basic map. Specifically, it includes:

[0084] S201, work area gridding and attribute initialization: the work area is divided into uniform grids G(x, y), and each grid stores basic terrain attributes and field strength values and time stamps. It should be noted that the grid size needs to be adjusted according to the robot size and motion accuracy, and too small will increase the calculation amount, and too large will reduce the path accuracy. The default range of grid size is 100m-500m.

[0085] S202, calculating multi-dimensional field strength values:

[0086] Task attractive field : reaction to task attraction, the calculation formula is , wherein represents the predefined task priority, represents the distance between the robot and the target point, represents the prevention zero constant, which is a very small constant, used to prevent the denominator from being zero, usually 0.0001, and i represents the robot index;

[0087] Electricity repulsive field : guide low-power robots to the charging station, the calculation formula is , wherein represents the distance between the robot and the charging station, β represents the adjustment coefficient, and γ represents the distance attenuation coefficient, which are determined through practical experience, and the default values are β=0.5 and γ=0.2, E irepresents the remaining power of the robot i;

[0088] traffic pressure field : reflects the congestion degree of the region, and the calculation formula is , represents the safety collision avoidance radius, represents the distance between the robot i and the robot j.

[0089] S203, synthesizing the dynamic field strength value: calculating the total dynamic field strength value through a weighted fusion formula, and updating according to a preset time interval (for example, 5s): , , , respectively represent each weight coefficient, and + + =1, , , are not zero, and the default value is: , , , which can also be dynamically adjusted according to the scene.

[0090] S3, using the improved A-star search algorithm to calculate the parameters in the dynamic energy field map, generating the time-space corridor and the yielding priority of the robot.

[0091] As shown in Figure 3 , step S3 can specifically include:

[0092] S301, calculating the total cost of the time-space node through the improved A-star search algorithm, and calculating the predicted arrival time and the predicted remaining power of the robot moving to the new node based on the neighborhood space.

[0093] Define the four-dimensional time-space node and the cost function time-space node as (x, y, t, E i ), wherein t is the time stamp, and E is the remaining power. The total cost function of the improved A-star search algorithm is: ; wherein, represents the dynamic field strength value of the robot i in the grid unit to which the current time t belongs, represents the preset task deadline;

[0094] Define the neighborhood space expansion based on the new node, and calculate the Euclidean distance of the robot moving to the new node to obtain the moving distance d m ; wherein, the neighborhood space represents the adjacent space range that can be directly moved to on the dynamic energy field map with the current time-space node as the center, for example, the neighborhood space can adopt an 8-neighborhood expansion strategy, that is, each node can be expanded to 8 adjacent grid nodes in up, down, left, right and four diagonal directions.

[0095] In the node expansion process, the expected arrival time t of the robot to the new node is calculated according to the formula pre and the expected residual power E pre : , ; wherein v represents a preset speed, E th represents a preset power threshold, the power unit is J, κ1 represents a speed energy consumption coefficient, the unit is J / m, that is, joule / meter, κ2 represents an acceleration energy consumption coefficient, the unit is J·s / m, that is, joule×second / meter, which are determined based on practical experience, and a represents acceleration.

[0096] It should be noted that in an engineering scenario, the battery "power" often refers to "electric energy", which can be expressed in joules (J) or kilowatt-hours (kWh), so in this application, the power is in units of joules.

[0097] S302, the new node with the expected residual power less than the preset minimum power threshold or the expected arrival time greater than the task deadline is discarded, and the screened new node is obtained.

[0098] S303, in each step of node expansion, the node with the lowest total cost in the screened new node is retained, and a space-time coordinate sequence is formed with the expected arrival time: , wherein represents the space-time coordinate, represents the expected arrival time of the robot to the path point , and p=1,2,…,n.

[0099] S304, the preset space buffer and the preset time window of all space-time coordinates in the space-time path are combined to obtain the space-time corridor CR i of the robot i.

[0100] The generated path node sequence is expanded to a space-time corridor through a space buffer s=0.5m and a time window =3s to obtain the space-time corridor CR i of the robot i: ; wherein s represents the radius of the preset space buffer, represents the half-width time length of the preset time window.

[0101] It should be noted that if the screened new node with the lowest total cost is multiple, it can be further determined according to the shortest time principle, or it can be determined according to the shortest distance or the most residual power principle, and the specific screening rule can be set by the staff.

[0102] Exemplarily, assuming that there is an existing two-dimensional grid map with a size of 4x4, the initial coordinates of the robot are (0, 0), the robot needs to move to (3, 3), and the initial power is 100. Therefore,

[0103] The initial node of the robot is: , the target point is (3, 3), and the preset task deadline is assumed to be 10 seconds.

[0104] The preset parameters are: speed v = 1 m / s, preset power threshold E th = 20, speed energy consumption coefficient = 1, and acceleration a = 0 (simplified energy consumption calculation).

[0105] The cost function weights are: = 0.4, = 0.3, = 0.3; and the dynamic field strength value of all passable grids is assumed to be 0.5 for convenience and simplification of calculation.

[0106] Then, the generation steps of the space-time corridor include:

[0107] Taking the node expansion of the first step and the second step as examples.

[0108] First step node expansion: first, expand the 8-neighbor nodes from the initial coordinates (0, 0, 0), and the coordinates that can be moved include (1, 0), (0, 1), and (1, 1). Calculate the predicted arrival time and the remaining power of each node:

[0109] Candidate node 1 (1, 0): moving distance d m = 1 m, initial time t0= 0, predicted arrival time t pre = t0+ d m / v = 0 + 1 / 1 = 1 s, predicted remaining power = 100 - 1 x 1 x 1 = 99, distance to the target point in the total cost formula is ≈ 3.606 m, therefore, the total cost f(n) = 0.4 x 3.606 + 0.3 x (10 - 1) + 0.3 x 0.5 ≈ 1.442 + 2.7 + 0.15 = 4.292.

[0110] It should be noted that, since the total cost of candidate node 1 is calculated at this time, t in the total cost calculation formula represents the time when the robot moves to candidate node 1, i.e., the predicted time t pre to reach candidate node 1.

[0111] Candidate node 2 (0, 1): symmetric to candidate node 1, the calculation results are the same.

[0112] ​​Candidate node 3 (1,1): moving distance m, estimated arrival time s, estimated remaining power , distance to target point m, total cost f(n) = 0.4 x 2.828 + 0.3 x (10-1.414) + 0.3 x 0.5 = 1.131 + 2.576 + 0.15 = 3.857;

[0113] Filter and sort by cost: no node power is lower than or time exceeds , so all are retained; and after sorting by total cost, the lowest cost node (1,1,1.414) is filtered and added to the path.

[0114] Second step node expansion: starting from coordinates (1,1), expand 8-neighbor nodes, excluding nodes already expanded in the first step, to obtain movable coordinates including: (1,2), (0,2), (2,0), (2,1), (2,2); similarly, continue to calculate the estimated arrival time and remaining power of each node, filter out nodes with power lower than or time exceeding , and the remaining nodes are sorted by total cost to obtain the node with the lowest total cost in this step to join the path.

[0115] Assume the spatiotemporal path node sequence is: Then expand the spatial buffer and time window for each spatiotemporal coordinate to obtain the spatiotemporal corridor.

[0116] It should be noted that the spatiotemporal corridor is the result of path planning for the robot, which upgrades the traditional "space collision-free" goal to a multi-dimensional optimization of "spatiotemporal-power-task priority", effectively solving the problem of low efficiency caused by task conflict, insufficient power, and traffic congestion in dense operation scenarios, and combining efficiency and engineering practicality.

[0117] S4, establish a local communication group and adjust the speed in real time according to the exchange information and concession priority of the local communication group.

[0118] Avoid path conflicts through local coordination to ensure the passage of high-priority robots.

[0119] S401, local communication group establishment and information exchange.

[0120] Each robot searches for neighbor robots within a radius R = 10m centered on itself to establish a local communication group. Broadcast state information Message periodically through MQTT protocol: ; wherein ID represents the unique identifier of the neighbor robot, (x,y) represents the coordinates of the neighbor robot, and denote the linear and angular velocity of the neighbor robot, E denotes the remaining energy, path denotes the sequence of spatiotemporal path nodes, P denotes the yielding priority, denotes the timestamp.

[0121] It is to be noted that the yielding priority P i is calculated based on the task gravitational field, the energy repulsive field and the traffic pressure field, and the formula is:

[0122] ; wherein, E i denotes the remaining energy of the robot i, denotes the full energy value of the robot, denotes the gradient vector of the traffic pressure field, denotes the module length, , , denote the weight coefficients of each term, and the sum is 1, which is determined based on practical experience, and the default value is: = 0.3, = 0.4, = 0.3, to ensure that the robot with low energy, high task priority or in the congested area obtains a higher priority.

[0123] S402, conflict detection and priority comparison:

[0124] The spatiotemporal corridor of all robots in the local communication group is traversed, and if there is an overlapping area, i.e. ; then (x, y, t) is marked as a conflict node; wherein, CR i denotes the spatiotemporal corridor of the robot i, CR j denotes the spatiotemporal corridor of the robot j.

[0125] S403, dynamic speed adjustment and conflict resolution

[0126] The number of neighbor robots with a yielding priority higher than the yielding priority of the current robot is counted, obtaining the high-priority neighbor number , and the average speed of the neighbor robots with a yielding priority lower than the yielding priority of the current robot is counted, obtaining the low-priority average speed .

[0127] If a conflict with a high-priority neighbor is detected, the current robot adjusts the speed according to the following formula:

[0128] ; wherein, denotes the current speed of the current robot j, represents the adjustment speed of the current robot j, represents the deceleration coefficient, represents the neighbor number smoothing factor, 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.

[0129] For example, when 2 high-priority neighbors are detected, and the yielding priority of the neighbor robot of the conflict node is also higher than the current robot, assuming that the current robot speed = 1.5 m / s, the average speed of the low-priority neighbor = 1.2 m / s, the current robot speed is reduced to:

[0130] , and the speed is adjusted by the average speed of the low-priority neighbor, so as to maintain the overall operation rhythm of the system while avoiding conflicts with high-priority neighbors, and to ensure the coordination and efficiency of multi-robot path planning.

[0131] Through the above steps, the balance among the task execution efficiency, power safety, and obstacle avoidance demand of the multi-robot is achieved, which is suitable for high-density scenarios such as warehouse logistics and emergency rescue, and ensures the efficient cooperation and robustness in a dynamic environment.

[0132] The embodiments of the present application also provide a computer readable storage medium including instructions, which, when executed on a computer, cause the computer to perform each of the above methods.

[0133] The embodiments of the present application also provide a computer program product including instructions, which, when executed on a computer, cause the computer to perform each of the above methods.

[0134] The embodiments of the present application also provide a chip, which includes a processor and an interface circuit, the interface circuit is coupled with the processor, the processor is used to run a computer program or instructions to implement the above method, and the interface circuit is used to communicate with other modules outside the chip.

[0135] In the above embodiments, all or part can be realized by software, hardware, firmware or any combination thereof. When realized by software programs, all or part can be realized in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, the computer program instructions produce all or part of the processes or functions described in the embodiments of the present application. The computer can be a general-purpose computer, a special-purpose computer, a computer network or other programmable devices. The computer instructions can be stored in a computer readable storage medium or transferred from one computer readable storage medium to another, for example, the computer instructions can be transferred from one website, computer, server or data center to another through wired (such as coaxial cable, optical fiber, digital subscriber line, DSL) or wireless (such as infrared, wireless, microwave, etc.) mode. The computer readable storage medium can be any available medium that can be accessed by a computer or include one or more data storage devices / modules integrated with a server, data center, etc. The available medium can be a magnetic medium (such as a floppy disk, a hard disk, a magnetic tape), an optical medium (such as a DVD), or a semiconductor medium (such as a solid state disk, SSD), etc.

[0136] Some data in the above formula is calculated by removing the dimension, and the formula is obtained by software simulation of a large amount of collected data to obtain a formula closest to the real situation; The preset parameters and the preset threshold in the formula are set by a person skilled in the art according to the actual situation or obtained by a large amount of data simulation.

[0137] Working principle of the present application:

[0138] Collect the state of the robot, divide the working area into grids, construct a dynamic energy field map containing task gravity field, power repulsion field and traffic pressure field, and quantify task demand, device state and real-time traffic constraints;

[0139] Based on the dynamic energy field map, an improved A-star search algorithm is used to generate a "time-space corridor". Each path node contains spatial coordinates, expected arrival time and remaining power, and by filtering out nodes with insufficient power or exceeding time, a feasible path is formed that takes into account space, time and power constraints.

[0140] According to the power state of the robot, the task priority and the traffic pressure of the area, the concession priority is calculated to determine the passing order in conflict;

[0141] The robots share the state and space-time path through local communication groups. When a path conflict is detected, the low-priority robot actively adjusts the speed to give way to the high-priority robot, to avoid collision and reduce the frequency of re-planning.

[0142] The above embodiments are only used to illustrate the technical method of the present application but not to limit the present application. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present application can be modified or equivalently replaced without departing from the spirit and scope of the technical method of the present application.

Claims

1. A path planning method for multiple robots, characterized by, The method comprises the following steps: constructing a dynamic energy field map based on the obtained position coordinates, speed, remaining power of the robots, preset task priority, distance between the robots and the charging station, distance between the robots and the target point, and robot spacing; calculating the parameters in the dynamic energy field map by using an improved A-star search algorithm to generate a time-space corridor and a yielding priority of the robots; wherein the time-space corridor represents a planned path of the robots; the improved A-star search algorithm is based on the distance between the robots and the target point, preset task deadline, and dynamic field strength value obtained from the dynamic energy field to improve a total cost function in the A-star search algorithm; generating a local communication group centered on each robot, which is used for local communication of multiple robots centered on each robot; adjusting the moving speed of the robots according to the exchanged information and the yielding priority of the local communication group; wherein the step of constructing the dynamic energy field map comprises the following steps: dividing the working area into a plurality of equal grid units; determining a task attractive field of each grid unit based on the task priority and the distance between the robots and the target point in the grid unit; determining an electric quantity repulsive field of each grid unit based on the remaining power and the distance between the robots and the charging station in the grid unit; determining a traffic pressure field of each grid unit based on the robot spacing in the grid unit; weighting and summing the task attractive field, the electric quantity repulsive field, and the traffic pressure field of the grid unit to determine a dynamic field strength value of each grid unit; obtaining a basic terrain attribute of each grid unit; the basic terrain attribute is used to identify whether the grid unit is passable; determining the dynamic field strength value and the basic terrain attribute of each grid unit in the dynamic energy field map; the step of generating the time-space corridor comprises the following steps: calculating the total cost of the time-space node by using the improved A-star search algorithm, and calculating the predicted arrival time and the predicted remaining power of the robot moving to a new node based on the neighborhood space; wherein the neighborhood space represents an adjacent spatial range that can be directly moved to on the dynamic energy field map with the current time-space node as the center; determining a conditional constraint rule for constraining the total cost, the predicted arrival time, and the predicted remaining power of the adjacent node; The new node satisfying the conditional constraint rule is reserved to obtain the spatiotemporal path node sequence of the robot i: ; wherein, ∈ represents a spatiotemporal coordinate, represents the predicted arrival time of the robot to the path point , 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 : ; wherein s represents a radius of a preset spatial buffer, represents a half-width duration of a preset time window; The yielding priority is used to represent the yielding sequence of the robots in the multi-robot scene, and the yielding priority P i The calculation formula is as follows: ; wherein, represents a full charge value of the robot, represents a gradient vector of the traffic pressure field, represents a module length, , , respectively represent each weight coefficient, and and are 1; E i represents a remaining charge of the robot i, represents a task gravitational field.

2. The path planning method for multiple robots according to claim 1, wherein The task gravitational field The calculation formula is: , represents a predefined task priority of the robot i, represents a distance of the robot i to the target point, represents a prevention zero constant for preventing a denominator from being zero; The electric quantity repulsive force field The calculation formula is: , represents the distance of the robot i from the charging station, β, γ represent adjustment coefficients, E i represents the remaining power of the robot i; The traffic pressure field The calculation formula is: , represents a safe collision avoidance radius, represents the distance between robot i and robot j; the dynamic field strength value The calculation formula is: ; wherein , , respectively represent each weight coefficient, and + + = 1, , , are all greater than zero, and the dynamic field strength value is updated according to a preset time interval.

3. The path planning method for multiple robots according to claim 1, wherein the step of reserving the new node satisfying the conditional constraint rule comprises the following steps: defining the total cost f(n) of each time-space node as: ; wherein t represents time, represents a dynamic field strength value of the grid cell to which the robot i belongs at time t, represents a preset task deadline, θ1, θ2, θ3 represent weight coefficients, and the sum is 1; Define new node based on 8-neighborhood spatial expansion, and calculate the Euclidean distance of the robot moving to the new node to get the moving distance d m ; The robot moves to the new node at the predicted arrival time t pre And the predicted remaining power E pre The calculation formula is respectively: , ; wherein t current represents the current time, v represents the preset speed, E i represents the remaining power of the robot i, E th represents the preset power threshold, κ1 represents the speed energy consumption coefficient, κ2 represents the acceleration energy consumption coefficient, and a represents the acceleration; the conditional constraint rule is: discarding the new node with the predicted remaining power less than the preset minimum power threshold or the predicted arrival time greater than the preset task deadline to obtain a screened new node; in each step of node expansion, the node with the lowest total cost in the screened new node is reserved, and a time-space coordinate sequence is formed with the predicted arrival time.

4. The path planning method for multiple robots according to claim 1, wherein the step of constructing the local communication group comprises the following steps: searching for neighbor robots within a radius R with the robot as the center to construct a local communication group; the exchanged information of the local communication group comprises: unique identifier, coordinates, current speed, remaining power, yielding priority, and time-space path node sequence of the neighbor robots.

5. The path planning method for multiple robots according to claim 4, wherein the step of adjusting the moving speed of the robots comprises the following steps: traversing the sequence of spatiotemporal path nodes, if there exists then marking as a conflict node; wherein denotes a spatiotemporal coordinate in the sequence of spatiotemporal path nodes of the current robot j, denotes a spatiotemporal coordinate in the sequence of spatiotemporal path nodes of the neighbor robot k; the number of neighbor robots whose yield priority is higher than the yield priority of the current robot, to obtain a high-priority neighbor number and the average speed of neighbor robots whose yield priority is lower than the yield priority of the current robot, to obtain a low-priority average speed ; comparing whether the yielding priority of the neighbor robot in conflict with the current robot is higher than the yielding priority of the current robot; if yes, the current robot adjusts the speed according to the speed adjustment formula; if no, the neighbor robot adjusts the speed according to the speed adjustment formula; the speed adjustment formula is: ; wherein, represents the current speed of the current robot j, l represents a deceleration coefficient, a represents a neighbor number smoothing factor, m represents a speed synchronization coefficient, represents the adjusted speed of the current robot j.

6. A multi-robot path planning system, characterized by, comprising: an energy field construction module, configured to construct a dynamic energy field map based on the acquired position coordinates, speed, remaining power of the robots, preset task priority, distance between the robots and the charging station, distance between the robots and the target point, and robot spacing; the dynamic energy field is used to represent the relationship between one or more of the acquired parameters, and the construction of the dynamic energy field map comprises: dividing the working area into a plurality of equal grid units; determining the task attractive field of each grid unit based on the task priority and the distance between the robots and the target point in the grid unit; determining the power repulsive field of each grid unit based on the remaining power and the distance between the robots and the charging station in the grid unit; determining the traffic pressure field of each grid unit based on the robot spacing in the grid unit; determining the dynamic field strength value of each grid unit by weighted sum of the task attractive field, the power repulsive field and the traffic pressure field of the grid unit; acquiring the basic terrain attribute of each grid unit; the basic terrain attribute is used to identify whether the grid unit can pass; determining the dynamic field strength value and the basic terrain attribute of each grid unit in the dynamic energy field map; a path planning module, configured to calculate the parameters in the dynamic energy field map by using an improved A-star search algorithm to generate a time-space corridor and a yielding priority of the robots; wherein, the time-space corridor represents the planned path of the robots, and the generation step comprises: calculating the total cost of the time-space node by the improved A-star search algorithm, and calculating the predicted arrival time and the predicted remaining power of the robot moving to a new node based on the neighborhood space; wherein, the neighborhood space represents the adjacent space range that can be directly moved to on the dynamic energy field map with the current time-space node as the center; determining the conditional constraint rule for constraining the total cost, the predicted arrival time and the predicted remaining power of the adjacent node; The new node satisfying the conditional constraint rule is reserved to obtain the spatiotemporal path node sequence of the robot i: ; wherein, ∈ represents a spatiotemporal coordinate, represents the predicted arrival time of the robot to the path point , p = 1, 2, …, n; merge all the space-time coordinates in the space-time path with the preset space buffer and the preset time window to obtain a space-time corridor CR of the robot i i :​ ; wherein s represents a radius of a preset spatial buffer, represents a half-width duration of a preset time window; the improved A-star search algorithm is improved based on the distance between the robots and the target point, the preset task deadline and the dynamic field strength value obtained by the dynamic energy field; The yielding priority is used to represent the yielding sequence of the robots in the multi-robot scene, and the yielding priority P i The calculation formula is as follows: ; wherein, represents a full charge value of the robot, represents a gradient vector of the traffic pressure field, represents a module length, , , respectively represent each weight coefficient, and and are 1; E i represents a remaining charge of the robot i, represents a task gravitational field; a speed adjustment module, configured to establish a local communication group for each robot, and adjust the moving speed of the robot according to the exchange information and the yielding priority of the local communication group; the local communication group is used for local communication of a plurality of robots centered on each robot.

7. A storage medium characterized by The computer program is stored in the memory and is executed by the processor to implement the path planning method of the plurality of robots according to any one of claims 1-5.

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