WO2023019740A1 - 一种基于多智能体的协同运输方法及其系统 - Google Patents

一种基于多智能体的协同运输方法及其系统 Download PDF

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WO2023019740A1
WO2023019740A1 PCT/CN2021/128092 CN2021128092W WO2023019740A1 WO 2023019740 A1 WO2023019740 A1 WO 2023019740A1 CN 2021128092 W CN2021128092 W CN 2021128092W WO 2023019740 A1 WO2023019740 A1 WO 2023019740A1
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agent
leader
agents
transportation system
cooperative
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French (fr)
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刘小旭
卢鑫
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深圳技术大学
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/40—Business processes related to the transportation industry
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G06Q10/047—Optimisation of routes or paths, e.g. travelling salesman problem
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
    • G06Q10/083—Shipping
    • G06Q10/0833—Tracking

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  • the invention relates to the technical field of robots and artificial intelligence, in particular to a multi-agent-based cooperative transportation method and a cooperative transportation system applying the method.
  • the main purpose of the present invention is to provide a multi-agent-based collaborative transportation method that can satisfy a limited number of agents in complex scenarios, and can match the number of agent nodes according to the required load capacity to complete material transportation.
  • Another object of the present invention is to provide a multi-agent based collaborative transportation system applied to the above collaborative transportation method.
  • a kind of collaborative transportation method based on multi-agent comprises according to the quantity of intelligent body and formation requirement, constructs the multi-agent cooperative transportation system with leader, and completes formation task;
  • the intelligent body of the intelligent body cooperative transportation system performs path planning and obstacle avoidance;
  • the intelligent body of the multi-agent cooperative transportation system performs cooperative tracking and transportation;
  • the leader calculates the target coordinate point of the path planning, it runs to the The next coordinate point, and send action instructions to the follower agent, the follower agent follows the leader in real time according to the action instructions, and the depth and level information between the follower agent and the tracking target is collected by the depth camera, and real-time based on the collected information.
  • detecting various physical characteristics of the transport target object and sending the detected data to the ground station; the ground station according to the received data Calculate the number n of agents that need to be allocated, send the number n of agents to the leader that communicates with it and establish a local area network, where n-1 follower agents including identification numbers are connected in the local area network.
  • the construction of a multi-agent cooperative transportation system with a leader includes: after the leader receives the number n of agents, taking the leader as the origin of the coordinates, and calculating each follower according to the required number n of agents The specific position of the agent in the multi-agent cooperative transportation system; send formation instructions to the follower agents numbered 1 to n-1 in the local area network, and calculate the GPS coordinate position of each follower agent formation; receive the coordinate position signal According to the coordinate control, each follower agent arrives at the designated coordinate position in the order of numbers 1 to n-1, completes the formation construction, and establishes a multi-agent collaborative transportation system.
  • the construction of a multi-agent cooperative transportation system with a leader also includes: waiting for each follower agent to move to a designated coordinate position, wherein, after each follower agent arrives at a designated coordinate position, it will send to The leader sends an arrival command; when the leader confirms that all following agents have arrived at the specified coordinate position, it sends a formation completion command to the ground station and waits for the next step; among them, the leader and the ground station always communicate through remote wireless transmission , to feed back the GPS coordinates of the current system location in real time.
  • the path planning and obstacle avoidance for the intelligent body of the multi-agent cooperative transportation system includes: the ground station sends the target coordinate point of the transportation to the leader, and the leader plans the movement path L according to the target coordinate point , and split the motion path L into several intermediate coordinate points and the time to reach the intermediate coordinate points; calculate the flight speed of the leader, and control the leader to move to the target coordinate point.
  • the path planning and obstacle avoidance for the intelligent body of the multi-agent cooperative transportation system also includes: during the flight, the leader reads the sensor data of each node in the local area network following the intelligent body, After multiple sensor data, the position of surrounding obstacles is detected by fusing the information to complete the obstacle avoidance action.
  • the splitting of the movement path L into several intermediate coordinate points and the time to reach the intermediate coordinate point includes: after the leader receives the target coordinate point, the current coordinate is fitted by an interpolation algorithm The path curve with the target coordinate point, and take an intermediate coordinate point (X j , Y j , Z j ) every k distance and the time T j to reach the intermediate coordinate point.
  • the calculation of the flight speed of the leader includes: calculating the movement of the leader from the coordinates (X j-1 , Y j-1 , Z j-1 ) to the coordinates (X j , Y j , Z j ) velocity v j , v j is expressed as formula (1):
  • l j (X j , Y j , Z j ),
  • is the coordinates (X j-1 , Y j-1 , Z j-1 ) to (X j , Y j , Z j ) modulus length.
  • d p, q is the depth information between the agents with serial numbers p and q read by the depth camera
  • l p, q is the horizontal width position information between the agents with serial numbers p and q.
  • the actual C p,q read by the depth camera is compared with the theoretical C p,q , and the speed of following the agent is adjusted so that the relative positions of each agent can remain unchanged;
  • v l after is the speed in the horizontal direction after the update adjustment
  • v d after is the speed in the depth direction after the update adjustment
  • K p is a proportional parameter
  • T i is an integral parameter
  • T d is a differential parameter.
  • the present invention provides a multi-agent-based collaborative transportation system, which uses the above-mentioned multi-agent-based collaborative transportation method for collaborative transportation, which includes: a ground station, a multi-agent collaborative Transportation system, the multi-agent cooperative transportation system communicates with the ground station through remote wireless transmission.
  • the multi-agent cooperative transportation system is used for ad hoc network communication to share state information, and determine the target path and target based on the shared state information
  • the motion state is used for path planning and obstacle avoidance
  • the ground station is used to receive, store and manage the working data sent by the multi-agent cooperative transportation system.
  • each agent node in the cooperative control method proposed by the present invention has high stand-alone intelligence, has the ability to communicate in an ad hoc network and plan paths and motion states, and can realize distributed multi-master cooperative communication, and according to different According to the environment and mission requirements, the formation flight behavior decision is made, so as to realize the control of the multi-agent system cooperatively tracking the target agent, which has stronger coordination and autonomous optimization.
  • the present invention is mainly aimed at the scenario where the load of a single machine cannot meet the actual transportation needs, and it is necessary to solve the transportation of overweight materials, the establishment of communication between multiple agents is cumbersome, and the delay of control signals seriously affects the stability of the agent system.
  • the multi-agent system Problems such as poor reliability can meet the requirements for transportation of materials of different weights, and improve the delay of intelligent body control signals in complex scenes, effectively improving the efficiency and accuracy of UAV operations.
  • Fig. 1 is a flow chart of an embodiment of a collaborative transportation method based on multi-agents in the present invention.
  • Fig. 2 is a flowchart of multi-agent formation in an embodiment of a multi-agent-based collaborative transportation method of the present invention.
  • FIG. 3 is a schematic diagram of a multi-agent formation in an embodiment of a multi-agent-based collaborative transportation method according to the present invention.
  • Fig. 4 is a flow chart about multi-agent path planning in an embodiment of a multi-agent-based collaborative transportation method of the present invention.
  • FIG. 5 is a flow chart of multi-agent cooperative tracking in an embodiment of a multi-agent-based cooperative transportation method in the present invention.
  • FIG. 6 is a schematic diagram of multi-agent cooperative tracking in an embodiment of a multi-agent-based cooperative transportation method according to the present invention.
  • FIG. 7 is a schematic diagram of a control circuit for multi-agent cooperative tracking in an embodiment of a multi-agent-based cooperative transportation method according to the present invention.
  • Fig. 8 is a schematic structural diagram of the collaborative transportation of UAVs in an embodiment of a multi-agent-based collaborative transportation method in the present invention.
  • Fig. 9 is a schematic diagram of an embodiment of a collaborative transportation system based on multi-agents in the present invention.
  • a kind of collaborative transportation method based on multi-agent of the present invention comprises the following steps:
  • Step S1 according to the number of agents and formation requirements, build a multi-agent cooperative transportation system with a leader, and complete the formation task.
  • Step S2 performing path planning and obstacle avoidance on the agents of the multi-agent cooperative transportation system.
  • Step S3 using the cooperative tracking and transportation algorithm to perform cooperative tracking and transportation on the agents of the multi-agent cooperative transportation system.
  • step S1 before constructing the multi-agent cooperative transportation system with a leader, it is also performed: detecting various physical characteristics of the transport target object, and sending the detected data to the ground station; the ground station according to the received Calculate the number n of agents that need to be allocated for data calculation, send the number n of agents to the leader that communicates with it and establish a local area network, where n-1 follower agents including identification numbers are connected in the local area network.
  • step S1 the construction of a multi-agent cooperative transportation system with a leader includes:
  • the leader After the leader receives the number n of agents, take the leader as the coordinate origin, and calculate the specific position of each follower agent in the multi-agent cooperative transportation system according to the required number n of agents.
  • Each follower agent that receives the coordinate position signal arrives at the designated coordinate position in sequence according to the coordinate control in the order of numbers 1 to n-1, completes the formation of the formation, and establishes a multi-agent collaborative transportation system.
  • step S1 the construction of a multi-agent collaborative transportation system with a leader also includes:
  • the leader When the leader confirms that all following agents have arrived at the specified coordinates, they send formation completion instructions to the ground station and wait for the next step.
  • the leader and the ground station always communicate through remote wireless transmission, and the GPS coordinates of the current system location are fed back in real time.
  • the path planning and obstacle avoidance for the intelligent body of the multi-agent cooperative transportation system include:
  • the ground station sends the target coordinate point of transportation to the leader, and the leader plans the motion path L according to the target coordinate point, and splits the motion path L into several intermediate coordinate points and the time to reach the intermediate coordinate point.
  • step S2 the intelligent body of the multi-agent cooperative transportation system is carried out path planning and obstacle avoidance, and also includes:
  • the leader reads the sensor data of each node in the local area network following the agent, and after fusing multiple sensor data, it detects the position of surrounding obstacles by fusing the information to complete the obstacle avoidance action.
  • the motion path L is split into several intermediate coordinate points and the time to reach the intermediate coordinate points, including:
  • the leader After receiving the target coordinate point, the leader uses the interpolation algorithm to fit the path curve between the current coordinates and the target coordinate point, and takes an intermediate coordinate point (X j , Y j , Z j ) every k distance and arrives at the The time T j of the intermediate coordinate point.
  • the flight speed of the leader is calculated, including:
  • l j (X j , Y j , Z j ),
  • is the coordinates (X j-1 , Y j-1 , Z j-1 ) to (X j , Y j , Z j ) modulus length.
  • step S3 when the leader calculates the target coordinate point of the path planning, it runs to the next coordinate point through coordinate control, and sends an action instruction to the follower agent, and the follower agent follows the action instruction
  • the leader follows the leader's movement in real time
  • the depth and level information between the following agent and the tracking target is collected by the depth camera, and the speed of the following agent is corrected in real time according to the collected information.
  • control circuit of the multi-agent cooperative tracking of this embodiment includes a PID controller and a depth camera, and when the formation is established, the interval position C between each agent is calculated p,q , expressed as formula (2):
  • d p, q is the depth information between the agents with serial numbers p and q read by the depth camera
  • l p, q is the horizontal width position information between the agents with serial numbers p and q.
  • the actual C p,q read by the depth camera is compared with the theoretical C p,q, and the speed of following agents is adjusted so that the relative positions of each agent can be kept unchanged.
  • v l after is the speed in the horizontal direction after the update adjustment
  • v d after is the speed in the depth direction after the update adjustment
  • K p is a proportional parameter
  • T i is an integral parameter
  • T d is a differential parameter.
  • the intelligent body in this embodiment is preferably a drone.
  • the intelligent body in this embodiment can also be other intelligent bodies such as unmanned vehicles or self-driving cars.
  • the cooperative transportation method of the present invention mainly includes establishing a formation algorithm, path planning and cooperative tracking transportation algorithm, wherein, as shown in Figure 2, establishing a formation algorithm mainly includes the following steps:
  • the transportation target object P is placed in the designated area S, and the designated area S obtains various physical characteristics of the target object P (such as mass M, structure, etc.) through various data collection methods, and sends the obtained data to the ground station.
  • various physical characteristics of the target object P such as mass M, structure, etc.
  • T u0 , T u1 , ..., T un are the maximum load weights of drones with serial numbers from 0 to n that have been obtained in advance
  • M is the quality of transport materials
  • Each slave UAV has an identification number, from 1 to n-1.
  • the main drone U 0 When the main drone U 0 receives the data from the ground station, take the main drone U 0 as the coordinate origin, and calculate the specific position of each drone in the multi-agent transportation system according to the number n of drones needed.
  • the master drone U 0 sends formation instructions to the drones numbered 1 to n-1 in the local area network, and will calculate the GPS coordinate position (X i ,Y i ,Z i ), the formation rules are shown in Figure 3, the main UAV U 0 is always located in the upper left corner, and the number represents the serial number of the UAV, the even number is on the left side of the formation, and the odd number is on the right side, increasing from top to bottom; when n-1 is an even number, place n-1 drones at the geometric center of the formation periphery, and finally send the coordinate position to each slave drone U i through the local area network.
  • the slave UAV U i controls the slave UAV U i to the designated coordinate position according to the coordinate control in the order of numbers 1 to n-1, and completes the construction of the formation and establishes a collaborative multi-agent system.
  • each slave UAV U i arrives at the designated coordinate position, it will send an arrival command to the master UAV U 0 , which is judged by the master UAV U 0
  • all slave UAVs U i arrive at the specified coordinate position, send formation completion instructions to the ground station, and wait for the next step.
  • the main drone U 0 and the ground station always communicate with each other through remote wireless transmission (such as RoLa, Zigbee, GPRS, etc.), and feed back the GPS coordinates of the current system location in real time.
  • remote wireless transmission such as RoLa, Zigbee, GPRS, etc.
  • path planning is carried out, as shown in Figure 4, which includes the following steps:
  • the ground station receives the GPS coordinate position of the transport target point, and the ground station sends the GPS coordinate position of the target point to the main UAV U 0.
  • U 0 uses an interpolation algorithm to fit the current coordinate and target Coordinate path curve, and take an intermediate coordinate point (X j , Y j , Z j ) every k distance and the time T j to reach the intermediate point.
  • the master UAV U 0 reads the sensor data of each node in the local area network from the UAV U i , such as reading the lidar sensor data of each UAV U i , and after fusing multiple sensor data, the information is fused to Detect the position of surrounding obstacles and complete obstacle avoidance.
  • the cooperative tracking algorithm is used to carry out collaborative tracking, which includes the following steps:
  • the master UAV U 0 After the master UAV U 0 is controlled by the coordinates, it sends motion instructions along the x-axis, y-axis, and z-axis to the slave UAV U i through the local area network at a certain sending frequency.
  • the interval position C p,q between the agents is specified, as shown in the above formula (2).
  • the UAV reads the actual C p,q through the depth camera and compares it with the theoretical C p,q, and adjusts the speed of the slave UAV so that the relative positions of the UAVs can remain unchanged.
  • each agent node in the cooperative control method proposed by the present invention has high stand-alone intelligence, has the ability to communicate in an ad hoc network and plan paths and motion states, and can realize distributed multi-master cooperative communication, and according to different According to the environment and mission requirements, the formation flight behavior decision is made, so as to realize the control of the multi-agent system cooperatively tracking the target agent, which has stronger coordination and autonomous optimization.
  • the present invention is mainly aimed at the scenario where the load of a single machine cannot meet the actual transportation needs, and it is necessary to solve the transportation of overweight materials, the establishment of communication between multiple agents is cumbersome, and the delay of control signals seriously affects the stability of the agent system.
  • the multi-agent system Problems such as poor reliability can meet the requirements for transportation of materials of different weights, and improve the delay of intelligent body control signals in complex scenes, effectively improving the efficiency and accuracy of UAV operations.
  • a multi-agent-based collaborative transportation system uses the above-mentioned multi-agent-based collaborative transportation method for collaborative transportation, which includes: ground station, multi-agent cooperative transportation System, the multi-agent cooperative transportation system communicates with the ground station through remote wireless transmission.
  • the multi-agent cooperative transportation system is used for ad hoc network communication to share state information, and determine the target path and target movement according to the shared state information state for path planning and obstacle avoidance, and the ground station is used to receive, store and manage the working data sent by the multi-agent cooperative transportation system.

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Abstract

本发明提供一种基于多智能体的协同运输方法及其系统，该方法包括根据智能体的数量和编队要求，构建具有领导者的多智能体协同运输系统，并完成编队任务；对多智能体协同运输系统的智能体进行路径规划和避障；对多智能体协同运输系统的智能体进行协同跟踪运输；其中，当领导者计算出路径规划的目标坐标点时，通过坐标控制运行至下一个坐标点，并向跟随智能体发送动作指令，跟随智能体根据动作指令实时跟随领导者运动，由深度相机采集跟随智能体与跟踪目标之间的深度和水平信息，并根据采集的信息实时修正跟随智能体的速度。本发明可以避免复杂场景中智能体控制信号的延迟问题，有效提高了无人机作业效率和精度，具有更强的协同性和自主寻优性。

Description

一种基于多智能体的协同运输方法及其系统 技术领域
本发明涉及机器人和人工智能技术领域，尤其涉及一种基于多智能体的协同运输方法以及应用该方法的协同运输系统。
背景技术
随着计算机视觉、人工智能以及控制技术的快速发展，以无人机、无人车、自动驾驶汽车等为代表的智能体逐渐在国民经济建设和国家安全保障方面发挥巨大作用。近年来，空中运输技术不断成熟，轻量级、短距离的无人机运输也逐渐兴起，主要应用于快递配送，城际运输等，由于中国人口密度较大，出于安全性的考虑，该运输方式还没有被广泛使用，但是在国际上已经有了很多成熟的案例，如德国邮政的Parcelcopter倾转旋翼无人机、Amazon提供的PrimeAir无人机速送等。
但是，轻量级单体无人机的载荷能力有限，为了扩大轻量级无人机的载荷能力，多无人机协同运输的概念被提出，引发了许多研究者的研究热潮，多无人机协同运输相比于单无人机运输有更大的难度和挑战，要求多无人机高度的协同性和控制的准确性，一旦有所偏差，很可能会导致整个系统坠毁、运输物资损毁。
发明内容
本发明的主要目的是提供一种在复杂场景下能满足有限数量智能体，可以按照需求负载能力，匹配智能体节点数，以完成物资运输的基于多智能体的协同运输方法。
本发明的另一目的是提供一种应用于上述协同运输方法的基于多智能体的协同运输系统。
为了实现上述主要目的，本发明提供的一种基于多智能体的协同运输方法，包括根据智能体的数量和编队要求，构建具有领导者的多智能体协同运输系统，并完成编队任务；对多智能体协同运输系统的智能体进行路 径规划和避障；对多智能体协同运输系统的智能体进行协同跟踪运输；其中，当领导者计算出路径规划的目标坐标点时，通过坐标控制运行至下一个坐标点，并向跟随智能体发送动作指令，跟随智能体根据动作指令实时跟随领导者运动，由深度相机采集跟随智能体与跟踪目标之间的深度和水平信息，并根据采集的信息实时修正跟随智能体的速度。
进一步的方案中，在构建具有领导者的多智能体协同运输系统之前，还执行：检测运输目标物体的各类物理特征，并将检测到的数据发送给地面站；地面站根据接收到的数据计算所需分配的智能体数量n，将智能体数量n发送至与其通讯的领导者并建立局域网，其中，在局域网内连接有n-1台包含识别序号的跟随智能体。
更进一步的方案中，所述构建具有领导者的多智能体协同运输系统，包括：在领导者接收智能体数量n后，以领导者为坐标原点，根据需要的智能体数量n计算出各个跟随智能体在多智能体协同运输系统中的具体位置；向局域网内编号为1至n-1号的跟随智能体发送编队指令，计算各个跟随智能体编队中的GPS坐标位置；接收到坐标位置信号的各个跟随智能体根据坐标控制按编号1至n-1的顺序依次到达指定坐标位置，完成编队的搭建，建立多智能体协同运输系统。
更进一步的方案中，所述构建具有领导者的多智能体协同运输系统，还包括：等待各个跟随智能体移动到指定坐标位置，其中，每个跟随智能体到达指定坐标位置后，均会向领导者发送到达指令；当领导者确定所有跟随智能体到达指定坐标位置后，向地面站发送编队完成指令，等待下一步操作；其中，领导者与地面站之间始终通过远程无线传输方式进行通讯，实时反馈当前系统所在位置的GPS坐标。
更进一步的方案中，所述对多智能体协同运输系统的智能体进行路径规划和避障，包括：地面站向领导者发送运输的目标坐标点，领导者根据目标坐标点规划出运动路径L，并将运动路径L拆分成若干个中间坐标点和达到该中间坐标点的时间；计算领导者的飞行速度，控制领导者移动至 目标坐标点。
更进一步的方案中，所述对多智能体协同运输系统的智能体进行路径规划和避障，还包括：在飞行过程中，领导者读取局域网内各节点跟随智能体的传感器数据，在融合多个传感器数据后，通过融合信息来检测周围障碍物位置，以完成避障动作。
更进一步的方案中，所述将运动路径L拆分成若干个中间坐标点和达到该中间坐标点的时间，包括：领导者在接收到目标坐标点后，通过插值法算法拟合出当前坐标与目标坐标点的路径曲线，并每隔k距离取一个中间坐标点(X j,Y j,Z j)和到达该中间坐标点的时间T j。
更进一步的方案中，所述计算领导者的飞行速度，包括：计算出领导者从坐标(X j-1,Y j-1,Z j-1)运动到坐标(X j,Y j,Z j)的运动速度v j，v j表示为公式(1)：
Figure PCTCN2021128092-appb-000001
其中，l j＝(X j,Y j,Z j)，||l j-l j-1||为坐标(X j-1,Y j-1,Z j-1)到(X j,Y j,Z j)的模长。
更进一步的方案中，在建立编队时，计算各个智能体之间的间隔位置C p,q，表示为公式(2)：
Figure PCTCN2021128092-appb-000002
其中，d p,q为深度相机读取的序号为p和q的智能体之间的深度信息，l p,q为序号为p和q的智能体之间的水平宽度位置信息。
更进一步的方案中，通过深度相机读取实际C p,q与理论C p,q对比，调整跟随智能体的速度，使各个智能体之间能够保持相对位置不变；
调整后跟随智能体的深度方向的速度表示为公式(3)：
Figure PCTCN2021128092-appb-000003
调整后跟随智能体的水平方向的速度表示为公式(4)：
Figure PCTCN2021128092-appb-000004
其中，v l，after为更新调整后的水平方向的速度，v l，front为更新前的水平方向的速度，v d,after为更新调整后的深度方向的速度，v d,front为更新前的深度方向的速度，其中e d＝d real-d set，e l＝l real-l set，K p为比例参数，T i为积分参数，T d为微分参数。
为了实现上述另一目的，本发明提供的一种基于多智能体的协同运输系统，该系统采用上述的基于多智能体的协同运输方法来进行协同运输，其包括：地面站、多智能体协同运输系统，多智能体协同运输系统与地面站之间通过远程无线传输方式进行通讯，多智能体协同运输系统用于自组网通信以共享状态信息，并根据共享的状态信息确定目标路径和目标运动状态，以进行路径规划和避障，地面站用于接收、存储和管理多智能体协同运输系统发送的工作数据。
由此可见，本发明提出的协同控制方法中各个智能体节点具有较高的单机智能化，具备自组网通信以及规划路径和运动状态的能力，可实现分布式多主协同通信，并根据不同的环境和任务需求进行编队飞行行为决策，从而实现多智能体系统协同跟踪目标智能体的控制，具有更强的协同性和自主寻优性。
所以，本发明主要针对单机负荷无法满足实际运输需求的场景下，需要解决超重物资运输，多智能体之间建立通讯操作繁琐，控制信号延迟严重对智能体系统稳定性产生影响，多智能体系统可靠性差等问题，能够在满足对不同重量物资运输要求的基础上，对复杂场景中对智能体控制信号延迟问题有所提高，有效提高无人机作业效率和精度。
附图说明
图1是本发明一种基于多智能体的协同运输方法实施例的流程图。
图2是本发明一种基于多智能体的协同运输方法实施例中关于多智能体编队的流程图。
图3是本发明一种基于多智能体的协同运输方法实施例中多智能体编队的原理图。
图4是本发明一种基于多智能体的协同运输方法实施例中关于多智能 体路径规划的流程图。
图5是本发明一种基于多智能体的协同运输方法实施例中关于多智能体协同跟踪的流程图。
图6是本发明一种基于多智能体的协同运输方法实施例中多智能体协同跟踪的原理图。
图7是本发明一种基于多智能体的协同运输方法实施例中多智能体协同跟踪的控制电路原理图。
图8是本发明一种基于多智能体的协同运输方法实施例中关于无人机协同运输的结构示意图。
图9是本发明一种基于多智能体的协同运输系统实施例的原理图。
以下结合附图及实施例对本发明作进一步说明。
具体实施方式
为使本发明实施例的目的、技术方案和优点更加清楚，下面将结合本发明实施例的附图，对本发明实施例的技术方案进行清楚、完整地描述。显然，所描述的实施例是本发明的一部分实施例，而不是全部的实施例。基于所描述的本发明的实施例，本领域普通技术人员在无需创造性劳动的前提下所获得的所有其他实施例，都属于本发明保护的范围。
一种基于多智能体的协同运输方法实施例：
参见图1，本发明的一种基于多智能体的协同运输方法，包括以下步骤：
步骤S1、根据智能体的数量和编队要求，构建具有领导者的多智能体协同运输系统，并完成编队任务。
步骤S2、对多智能体协同运输系统的智能体进行路径规划和避障。
步骤S3、采用协同跟踪运输算法对多智能体协同运输系统的智能体进行协同跟踪运输。
在上述步骤S1中，在构建具有领导者的多智能体协同运输系统之前，还执行：检测运输目标物体的各类物理特征，并将检测到的数据发送给地面站；地面站根据接收到的数据计算所需分配的智能体数量n，将智能体 数量n发送至与其通讯的领导者并建立局域网，其中，在局域网内连接有n-1台包含识别序号的跟随智能体。
在上述步骤S1中，所述构建具有领导者的多智能体协同运输系统，包括：
在领导者接收智能体数量n后，以领导者为坐标原点，根据需要的智能体数量n计算出各个跟随智能体在多智能体协同运输系统中的具体位置。
向局域网内编号为1至n-1号的跟随智能体发送编队指令，计算各个跟随智能体编队中的GPS坐标位置。
接收到坐标位置信号的各个跟随智能体根据坐标控制按编号1至n-1的顺序依次到达指定坐标位置，完成编队的搭建，建立多智能体协同运输系统。
在上述步骤S1中，所述构建具有领导者的多智能体协同运输系统，还包括：
等待各个跟随智能体移动到指定坐标位置，其中，每个跟随智能体到达指定坐标位置后，均会向领导者发送到达指令。
当领导者确定所有跟随智能体到达指定坐标位置后，向地面站发送编队完成指令，等待下一步操作。
其中，领导者与地面站之间始终通过远程无线传输方式进行通讯，实时反馈当前系统所在位置的GPS坐标。
在上述步骤S2中，所述对多智能体协同运输系统的智能体进行路径规划和避障，包括：
地面站向领导者发送运输的目标坐标点，领导者根据目标坐标点规划出运动路径L，并将运动路径L拆分成若干个中间坐标点和达到该中间坐标点的时间。
计算领导者的飞行速度，控制领导者移动至目标坐标点。
在上述步骤S2中，所述对多智能体协同运输系统的智能体进行路径 规划和避障，还包括：
在飞行过程中，领导者读取局域网内各节点跟随智能体的传感器数据，在融合多个传感器数据后，通过融合信息来检测周围障碍物位置，以完成避障动作。
在本实施例中，将运动路径L拆分成若干个中间坐标点和达到该中间坐标点的时间，包括：
领导者在接收到目标坐标点后，通过插值法算法拟合出当前坐标与目标坐标点的路径曲线，并每隔k距离取一个中间坐标点(X j,Y j,Z j)和到达该中间坐标点的时间T j。
在本实施例中，计算领导者的飞行速度，包括：
计算出领导者从坐标(X j-1,Y j-1,Z j-1)运动到坐标(X j,Y j,Z j)的运动速度v j，v j表示为公式(1)：
Figure PCTCN2021128092-appb-000005
其中，l j＝(X j,Y j,Z j)，||l j-l j-1||为坐标(X j-1,Y j-1,Z j-1)到(X j,Y j,Z j)的模长。
在上述步骤S3中，如图5所示，当领导者计算出路径规划的目标坐标点时，通过坐标控制运行至下一个坐标点，并向跟随智能体发送动作指令，跟随智能体根据动作指令实时跟随领导者运动，由深度相机采集跟随智能体与跟踪目标之间的深度和水平信息，并根据采集的信息实时修正跟随智能体的速度。
在本实施例中，如图6和图7所示，本实施例的多智能体协同跟踪的控制电路包括PID控制器以及深度相机，在建立编队时，计算各个智能体之间的间隔位置C p,q，表示为公式(2)：
Figure PCTCN2021128092-appb-000006
其中，d p,q为深度相机读取的序号为p和q的智能体之间的深度信息，l p,q为序号为p和q的智能体之间的水平宽度位置信息。
具体的，通过深度相机读取实际C p,q与理论C p,q对比，调整跟随智能 体的速度，使各个智能体之间能够保持相对位置不变。
调整后跟随智能体的深度方向的速度表示为公式(3)：
Figure PCTCN2021128092-appb-000007
调整后跟随智能体的水平方向的速度表示为公式(4)：
Figure PCTCN2021128092-appb-000008
其中，v l，after为更新调整后的水平方向的速度，v l，front为更新前的水平方向的速度，v d,after为更新调整后的深度方向的速度，v d,front为更新前的深度方向的速度，其中e d＝d real-d set，e l＝l real-l set，K p为比例参数，T i为积分参数，T d为微分参数。
本实施例的智能体优选为无人机，当然，本实施例的智能体也可以是无人车或自动驾驶汽车等其他智能体。
在实际应用中，本发明的协同运输方法主要包括建立编队算法、路径规划以及协同跟踪运输算法，其中，如图2所示，建立编队算法主要包括以下步骤：
首先，运输目标物体P放置于指定区域S，指定区域S通过多种数据采集手段得到目标物体P的各类物理特征(如质量M，结构等)，并将得到的数据发送给地面站。
接着，当地面站收到采集区S收集数据M，通过计算
Figure PCTCN2021128092-appb-000009
得到需要的无人机数量n，其中，T u0，T u1，...，T un均为事先已获得的序号为从0到n的无人机的最大负载重量，M为运输物资的质量，将无人机数量n发送给与之通讯的主无人机U 0(领导者)，其中，领导者U 0建立局域网，为网内主机，局域网内连接n-1台从无人机，每一台从无人机都有识别序号，从1至n-1。
当主无人机U 0收到地面站传来数据，以主无人机U 0为坐标原点，根据需要无人机数n计算出各无人机在多智能体运输系统中的具体位置。
然后，主无人机U 0向局域网内编号1至n-1号的无人机发送编队指令， 将计算各个从无人机U i(跟随智能体)在编队中的GPS坐标位置(X i,Y i,Z i)，编队规则如图3所示，主无人机U 0始终位于左上角位置，编号代表了无人机的序号，编号为偶数在编队左侧，编号为奇数在右侧，由上到下依次递增；当n-1为偶数时，将n-1无人机置于编队外围几何中心，最终将坐标位置通过局域网发送给各从无人机U i。
当收到位置信号的从无人机U i根据坐标控制按编号1至n-1的顺序依次控制从无人机U i到指定坐标位置，完成编队的搭建，建立协同多智能体系统。
接着，等待从无人机U i飞向指定坐标位置，当每台从无人机U i到达指定坐标位置，均会向主无人机U 0发送到达指令，由主无人机U 0判断当所有从无人机U i是否到达指定坐标位置，向地面站发送编队完成指令，等待下一步操作。
其中，主无人机U 0与地面站始终通过远程无线传输(如RoLa，Zigbee，GPRS等)相互通讯，实时反馈当前系统所在位置的GPS坐标。
然后，进行路径规划，如图4所示，其包括以下步骤：
首先，地面站接收到运输目标点的GPS坐标位置，地面站向主无人机U 0发送目标点的GPS坐标位置，U 0接收到坐标值后，通过插值法算法拟合出当前坐标与目标坐标路径曲线，并每隔k距离取一个中间坐标点(X j,Y j,Z j)和到达该中间点的时间T j。
接着，计算出主无人机U 0从坐标(X j-1,Y j-1,Z j-1)运动到坐标(X j,Y j,Z j)的运动速度v j，如上述公式(1)。其中，在飞行过程中会有时间计数器，如果到达下一节点实际时间和理论时间不等，即T jreal≠T jset，下一段速度公式为公式(1.1)：
Figure PCTCN2021128092-appb-000010
其中，ΔT＝T jreal-T jset保证物资可以在设定时间内到达。
然后，主无人机U 0读取局域网内各节点从无人机U i中传感器数据，如读取各个无人机U i的激光雷达传感器数据，融合多个传感器数据后，通过 融合信息来检测周围障碍物位置，完成避障。
然后，如图5至图8所示，使用协同跟踪运输算法进行协同跟踪，其包括以下步骤：
以U 0作为主无人机，当主无人机U 0计算出路径规划的坐标点时，通过坐标控制运行至下一个坐标点。
接着，主无人机U 0通过坐标控制后，将沿x轴、y轴、z轴动作指令以一定的发送频率通过局域网发送给从无人机U i。
然后，当从无人机U i接收到动作指令后，实时跟随U 0运动。
在上述步骤中，在建立编队时，规定好各智能体之间的间隔位置C p,q，如上述公式(2)。其中，无人机通过深度相机读取实际C p,q与理论C p,q对比，调节从无人机的速度，使各无人机之间能够保持相对位置不变。
由此可见，本发明提出的协同控制方法中各个智能体节点具有较高的单机智能化，具备自组网通信以及规划路径和运动状态的能力，可实现分布式多主协同通信，并根据不同的环境和任务需求进行编队飞行行为决策，从而实现多智能体系统协同跟踪目标智能体的控制，具有更强的协同性和自主寻优性。
所以，本发明主要针对单机负荷无法满足实际运输需求的场景下，需要解决超重物资运输，多智能体之间建立通讯操作繁琐，控制信号延迟严重对智能体系统稳定性产生影响，多智能体系统可靠性差等问题，能够在满足对不同重量物资运输要求的基础上，对复杂场景中对智能体控制信号延迟问题有所提高，有效提高无人机作业效率和精度。
一种基于多智能体的协同运输系统实施例：
如图9所示，本发明提供的一种基于多智能体的协同运输系统，该系统采用上述的基于多智能体的协同运输方法来进行协同运输，其包括：地面站、多智能体协同运输系统，多智能体协同运输系统与地面站之间通过远程无线传输方式进行通讯，多智能体协同运输系统用于自组网通信以共享状态信息，并根据共享的状态信息确定目标路径和目标运动状态，以进行路径规划和避障，地面站用于接收、存储和管理多智能体协同运输系统 发送的工作数据。
需要说明的是，以上仅为本发明的优选实施例，但发明的设计构思并不局限于此，凡利用此构思对本发明做出的非实质性修改，也均落入本发明的保护范围之内。

Claims (11)

  1. 一种基于多智能体的协同运输方法，其特征在于，包括：
    根据智能体的数量和编队要求，构建具有领导者的多智能体协同运输系统，并完成编队任务；
    对多智能体协同运输系统的智能体进行路径规划和避障；
    对多智能体协同运输系统的智能体进行协同跟踪运输；
    其中，当领导者计算出路径规划的目标坐标点时，通过坐标控制运行至下一个坐标点，并向跟随智能体发送动作指令，跟随智能体根据动作指令实时跟随领导者运动，由深度相机采集跟随智能体与跟踪目标之间的深度和水平信息，并根据采集的信息实时修正跟随智能体的速度。
  2. 根据权利要求1所述的基于多智能体的协同运输方法，其特征在于，在构建具有领导者的多智能体协同运输系统之前，还执行：
    检测运输目标物体的各类物理特征，并将检测到的数据发送给地面站；
    地面站根据接收到的数据计算所需分配的智能体数量n，将智能体数量n发送至与其通讯的领导者并建立局域网，其中，在局域网内连接有n-1台包含识别序号的跟随智能体。
  3. 根据权利要求2所述的基于多智能体的协同运输方法，其特征在于，所述构建具有领导者的多智能体协同运输系统，包括：
    在领导者接收智能体数量n后，以领导者为坐标原点，根据需要的智能体数量n计算出各个跟随智能体在多智能体协同运输系统中的具体位置；
    向局域网内编号为1至n-1号的跟随智能体发送编队指令，计算各个跟随智能体编队中的GPS坐标位置；
    接收到坐标位置信号的各个跟随智能体根据坐标控制按编号1至n-1 的顺序依次到达指定坐标位置，完成编队的搭建，建立多智能体协同运输系统。
  4. 根据权利要求3所述的基于多智能体的协同运输方法，其特征在于，所述构建具有领导者的多智能体协同运输系统，还包括：
    等待各个跟随智能体移动到指定坐标位置，其中，每个跟随智能体到达指定坐标位置后，均会向领导者发送到达指令；
    当领导者确定所有跟随智能体到达指定坐标位置后，向地面站发送编队完成指令，等待下一步操作；
    其中，领导者与地面站之间始终通过远程无线传输方式进行通讯，实时反馈当前系统所在位置的GPS坐标。
  5. 根据权利要求1所述的基于多智能体的协同运输方法，其特征在于，所述对多智能体协同运输系统的智能体进行路径规划和避障，包括：
    地面站向领导者发送运输的目标坐标点，领导者根据目标坐标点规划出运动路径L，并将运动路径L拆分成若干个中间坐标点和达到该中间坐标点的时间；
    计算领导者的飞行速度，控制领导者移动至目标坐标点。
  6. 根据权利要求5所述的基于多智能体的协同运输方法，其特征在于，所述对多智能体协同运输系统的智能体进行路径规划和避障，还包括：
    在飞行过程中，领导者读取局域网内各节点跟随智能体的传感器数据，在融合多个传感器数据后，通过融合信息来检测周围障碍物位置，以完成避障动作。
  7. 根据权利要求5所述的基于多智能体的协同运输方法，其特征在于，所述将运动路径L拆分成若干个中间坐标点和达到该中间坐标点的时间，包括：
    领导者在接收到目标坐标点后，通过插值法算法拟合出当前坐标与目标坐标点的路径曲线，并每隔k距离取一个中间坐标点(X j,Y j,Z j)和到达该中间坐标点的时间T j。
  8. 根据权利要求5所述的基于多智能体的协同运输方法，其特征在于，所述计算领导者的飞行速度，包括：
    计算出领导者从坐标(X j-1,Y j-1,Z j-1)运动到坐标(X j,Y j,Z j)的运动速度v j，v j表示为公式(1)：
    Figure PCTCN2021128092-appb-100001
    其中，l j＝(X j,Y j,Z j)，||l j-l j-1||为坐标(X j-1,Y j-1,Z j-1)到(X j,Y j,Z j)的模长。
  9. 根据权利要求1至8任一项所述的基于多智能体的协同运输方法，其特征在于：
    在建立编队时，计算各个智能体之间的间隔位置C p,q，表示为公式(2)：
    Figure PCTCN2021128092-appb-100002
    其中，d p,q为深度相机读取的序号为p和q的智能体之间的深度信息，l p,q为序号为p和q的智能体之间的水平宽度位置信息。
  10. 根据权利要求9所述的基于多智能体的协同运输方法，其特征在于：
    通过深度相机读取实际C p,q与理论C p,q对比，调整跟随智能体的速度，使各个智能体之间能够保持相对位置不变；
    调整后跟随智能体的深度方向的速度表示为公式(3)：
    Figure PCTCN2021128092-appb-100003
    调整后跟随智能体的水平方向的速度表示为公式(4)：
    Figure PCTCN2021128092-appb-100004
    其中，v l，after为更新调整后的水平方向的速度，v l，front为更新前的水平方向的速度，v d,after为更新调整后的深度方向的速度，v d,front为更新前的深度方向的速度，其中e d＝d real-d set，e l＝l real-l set，K p为比例参数，T i为积分参数，T d为微分参数。
  11. 一种基于多智能体的协同运输系统，其特征在于，该系统采用如权利要求1至10任一项所述的基于多智能体的协同运输方法来进行协同运输，其包括：
    地面站、多智能体协同运输系统，多智能体协同运输系统与地面站之间通过远程无线传输方式进行通讯，多智能体协同运输系统用于自组网通信以共享状态信息，并根据共享的状态信息确定目标路径和目标运动状态，以进行路径规划和避障，地面站用于接收、存储和管理多智能体协同运输系统发送的工作数据。
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