WO2022001120A1 - 一种多智能体系统及其控制方法 - Google Patents

一种多智能体系统及其控制方法 Download PDF

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WO2022001120A1
WO2022001120A1 PCT/CN2021/076259 CN2021076259W WO2022001120A1 WO 2022001120 A1 WO2022001120 A1 WO 2022001120A1 CN 2021076259 W CN2021076259 W CN 2021076259W WO 2022001120 A1 WO2022001120 A1 WO 2022001120A1
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agent
network
communication network
self
organizing
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PCT/CN2021/076259
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English (en)
French (fr)
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郭胜
马永鑫
唐文献
唐秋妍
王为民
王月阳
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江苏科技大学
镇江宇诚智能装备科技有限责任公司
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F16/33—Querying
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F16/33—Querying
    • G06F16/332—Query formulation
    • G06F16/3329—Natural language query formulation or dialogue systems
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F16/36—Creation of semantic tools, e.g. ontology or thesauri
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
    • G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
    • G06F3/011—Arrangements for interaction with the human body, e.g. for user immersion in virtual reality
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
    • G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
    • G06F3/017—Gesture based interaction, e.g. based on a set of recognized hand gestures
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/22—Procedures used during a speech recognition process, e.g. man-machine dialogue
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W84/00—Network topologies
    • H04W84/18—Self-organising networks, e.g. ad-hoc networks or sensor networks

Definitions

  • the invention belongs to the technical field of intelligent autonomous unmanned systems, and particularly relates to a multi-agent system and a control method thereof.
  • Autonomous unmanned system technology is one of the key technologies that artificial intelligence focuses on, and its main features are intelligence, control systems, dynamic networking and human-machine relationships.
  • the network capabilities provided by 5G technology will meet the needs of three extreme business scenarios, providing users with higher rates and better business experience, including high-speed, high-bandwidth business capabilities, which can support ultra-high-definition 3D video, VR , AR and other services; the low power consumption and high connection density capabilities provided can support applications such as monitoring, sensors, and smart cities; the ultra-low latency, high-reliability communication capabilities provided can support autonomous driving, telemedicine, and smart factories , artificial intelligence and other applications. Therefore, 5G technology can promote the needs of intelligent and automated communication in all walks of life.
  • a multi-agent system is composed of a series of interacting agents. Each agent expresses the structure, function and behavioral characteristics of the system through communication, cooperation, coordination, scheduling, management and control. A lot of complex work done. Multi-agent systems have autonomy, distribution, coordination, and have self-organization, learning and reasoning capabilities. Therefore, using multi-agent systems to solve practical problems can replace a single agent or artificial intelligence, which is difficult or impossible to complete. work, and has strong robustness and reliability.
  • the design of the above-mentioned agents and agent systems depends on the specified application scenarios and functional requirements. Therefore, the formed agents and systems have a single function, and the application scenarios have limitations; when there are task requirements for different application scenarios, the original system needs to be implemented Redesign or transformation, that is, the existing system cannot meet the diverse and changing use requirements of application scenarios; in addition, the function of a single agent in the above-mentioned multi-agent system is a preset specific and single function, when the system When a certain agent fails, it will affect the operation of the entire system, and the system has poor robustness and insufficient reliability.
  • the target localization method and device disclosed in a method for target localization using digital images, first obtain the target Gabor filter shape template, use the Gabor filter result for shape matching and localization, and then use the similarity between skeleton features determine the area where the target is located; patent a method and system for target positioning and identification of underwater robots (ZL201710209500.X) discloses a method for target positioning using sonar information; patented wireless sensor network static target positioning method and system (ZL201310145553.1 ) discloses a method for locating a target using a wireless network.
  • the multi-agent system needs to be able to perceive environmental information in various ways and perform multi-source heterogeneous signal fusion processing. Then, control the operation of the multi-agent system. If the target of the multi-agent system moves randomly, it is difficult for the existing multi-agent system to predict the possible position of the target at the next moment, so that the agent cannot obtain the information of the target at the next moment, or even loses the target.
  • Purpose of the invention Aiming at the problems existing in the prior art, to provide a multi-agent system and a control method thereof, which can quickly and reliably apply the multi-agent system in different scenarios and perform different tasks.
  • the initial operation strategy of the system is automatically generated, and the operation strategy is changed in real time during the operation process to improve the autonomy and coordination of the multi-agent system.
  • the present invention provides a multi-agent system, including a self-organizing wireless communication network, the self-organizing wireless communication network is connected to the Internet, and the Internet is respectively connected to a gateway device and a first communication network.
  • the base station is connected, the first communication network is also connected with a multi-channel interaction system, and the multi-channel interaction system is connected with a user; the gateway device is also connected with a data server.
  • the self-organizing wireless communication network is composed of an agent and a wireless communication network transmission device.
  • the self-organizing wireless communication network is divided into an underwater self-organizing wireless communication network, an air self-organizing wireless communication network and a terrestrial self-organizing wireless communication network; correspondingly, the agents are also divided into underwater agents and aerial agents. and terrestrial agents.
  • the underwater intelligent body is an underwater unmanned vehicle; the aerial intelligent body is an unmanned aerial vehicle; and the terrestrial intelligent body is an unmanned vehicle.
  • the underwater self-organizing wireless communication network consists of an underwater unmanned vehicle, a water transfer station, a communication satellite and a satellite ground receiving station, and the water transfer station is connected with several underwater unmanned vehicles, so The underwater unmanned vehicle is connected with the satellite ground receiving station through the communication satellite.
  • the air self-organizing wireless communication network includes an unmanned aerial vehicle and a second communication network base station, and the second communication network base station is connected with several unmanned aerial vehicles.
  • the terrestrial self-organizing wireless communication network includes an unmanned vehicle and a third communication network base station, and the third communication network base station is connected to several unmanned vehicles.
  • a control method for a multi-agent system as described above characterized in that it comprises the following steps:
  • Step 1 Set the application scenario of the multi-agent system
  • the agent system select the sea, land and air joint search and rescue application scenario through the multi-channel interactive system, and generate the operating conditions and parameters of the multi-agent system according to the selected application scenario and the multi-agent knowledge base system, including the search and rescue coverage area, system operation Duration, search and rescue time period and detection target; the user inputs parameters through the multi-channel interactive system and uploads them to the data server;
  • Step 2 Build a multi-agent system
  • the multi-channel interaction system judges the composition type and quantity of the agents in the multi-agent system according to the selected application scenario and the multi-agent knowledge base system, and determines the positioning method and communication method according to the number of agents and the movement path;
  • the multi-agent system communication network system consists of three parts: the interconnection network system, the satellite relay communication network system and the self-organizing wireless network system; the interconnection network system interconnects the wireless self-organizing network and the data storage server; the satellite relay communication network system The relay network connecting the underwater wireless self-organizing network and the interconnection network; the wireless self-organizing network, responsible for the network services of multi-agents, is divided into terrestrial wireless self-organizing network, air wireless self-organizing network and underwater wireless self-organizing network;
  • the underwater wireless self-organizing network uploads data and receives instructions from the multi-channel interactive system through the communication satellite equipment on the underwater unmanned vehicle; the communication satellite receives the data uploaded by the underwater wireless self-organizing network and the instructions from the multi-channel interactive system, And they are respectively downloaded to the satellite ground receiving station and the underwater wireless ad hoc network.
  • the satellite ground receiving station accesses the World Wide Web through the ground network to receive and send data and instructions; the land and air wireless ad hoc networks pass through the existing communication network.
  • the ground base station is connected to the World Wide Web to complete the data and instruction interaction with the data server and the multi-channel interactive system; the multi-agents on land, sea and air complete the main controller selection, data transmission and instruction reception through the wireless self-organizing network;
  • Step 4 Deploy and run a multi-agent system and perceive environmental information
  • One of the agents is selected as the main control agent;
  • the multi-channel interactive system sends control instructions to the main control agent through the communication network, and the main control agent sends instructions to control other agents to move to the deployment position;
  • Agent collection including environmental information information of images, voices, actions, and life signals, and transmits the collected information to the control module of the agent;
  • the agent transmits information to the main control agent through the communication network, and the transmission path of the information is determined by the communication topology;
  • Step 5 Perform multi-source heterogeneous information fusion processing on the transmitted information
  • the multi-channel interactive system sends motion control instructions through the communication network according to the real-time positioning information of the target, and the agent moves after receiving the instructions, ensuring that the target is always in the best monitoring position of the agent; at the same time, the multi-channel interactive system displays the target position information;
  • the user views the location information of the target in real time on the multi-channel interactive system, controls the operation of the agent through the operation of the multi-channel interactive system, and issues actions, gestures, and voice commands. implement.
  • step (2) the specific steps of constructing the multi-agent system in the step (2) are as follows:
  • the multi-channel interaction system judges the composition type of the agents in the multi-agent system according to the selected application scenarios and the multi-agent knowledge base system.
  • the agents in the sea, land and air joint search and rescue application scenarios include unmanned vehicles, unmanned aerial vehicles and underwater drones. human aircraft;
  • the multi-channel interactive system determines the detection method according to the search and rescue time period. If the working time period is daytime, the image or life sensing module is selected for detection. If the search and rescue time period is night, the infrared or life sensing module is selected for detection; the detection method is determined After that, the control system determines the number of agents according to the search and rescue coverage area and the detection range of a single agent, and generates the movement path of the agent at the same time;
  • the communication method between each agent is determined according to the distance D between each agent during operation: in land and air scenarios, when D>500m, 5G, 4G, GPRS, communication satellite, short-wave communication, etc.; when D ⁇ 500m, Zig-Bee, Bluetooth (Bluetooth), wireless broadband (Wi-Fi), etc. can be used; underwater scene uses underwater acoustic communication;
  • the multi-channel interactive system determines which positioning method to use according to the application scenario and positioning accuracy requirements, or uses a combination of multiple positioning methods: in the open field, the positioning accuracy is m-level, then GPS positioning is used; indoors, the positioning accuracy is cm-level , then use WIFI or Bluetooth; under water, the positioning accuracy is cm level: use ultrasonic positioning;
  • the multi-channel interactive system determines the number of mobile power stations that need to be configured for the multi-agent system according to the endurance and running time of the agent.
  • the method for networking the multi-agent system in the step (3) includes the following steps:
  • Multi-agents form a network without obvious master-slave relationship according to the networking protocol, that is, each network node acts as the master node and initiates network transmission requests, and realizes the master-slave assignment of nodes through software according to task requirements;
  • Each multi-agent relies on the flooding protocol to broadcast data packets, mainly including the address and machine code of the agent. Build a routing table and randomly assign an agent to the main controller;
  • the main control agent communicates with the upper computer through the gateway node and the communication network;
  • the upper computer has a new algorithm for assigning the multi-agent master controller, then re-designate the master controller agent, re-assign the master-slave control node, repeat step (3.3), if not, the network is in the stage of waiting for the response command;
  • the upper computer transmits the task instruction through the interconnected communication network; the task instruction comes from the user instruction extracted by the upper computer, or the task instruction generated according to the knowledge base in the data server;
  • the communication network sends the task instructions issued by the host computer to each agent layer by layer;
  • Each agent transmits the corresponding command response or data back to the data server or host computer through the land, sea and air wireless ad hoc network.
  • the multi-agent system control method can quickly and reliably apply the multi-agent system in different scenarios and perform different tasks.
  • the system will automatically generate the initial operation strategy of the system according to the selected application scenarios and input parameters, and The system operation strategy will change in real time during the operation process, improving the autonomy and coordination of the multi-agent system;
  • a multi-domain, multimedia, and multi-level knowledge base system is established to provide support for the dynamic change of the operation strategy of the multi-agent system and ensure the accurate operation of the multi-agent system in different application scenarios;
  • the knowledge base system and the multi-channel The interactive system interface is set up with a secondary retrieval mechanism to avoid excessive noise in information retrieval and improve retrieval efficiency and accuracy;
  • the intelligent body adopts a modular design and consists of multiple functional modules.
  • Each functional module is connected by a quick plug-in method, which can be quickly spliced and combined into an intelligent body with different functions, and the same function of the intelligent body can be spliced with different
  • the form of enables the agent to realize the function of executing tasks in three states of sea, land and air, and meet the needs of multi-scenario applications.
  • FIG. 1 is a schematic structural diagram of a multi-agent system of the present invention
  • FIG. 2 is a flow chart of the control method of the multi-agent system of the present invention.
  • a multi-agent 1 system includes an ad hoc wireless communication network, the ad hoc wireless communication network is connected to the Internet 10, and the Internet 10 is in turn connected to a gateway device 11 and a first communication network base station 13 respectively
  • the first communication network is also connected with the multi-channel interaction system 15 , and the multi-channel interaction system 15 is connected with the user 14 ; the gateway device 11 is also connected with the data server 12 .
  • the self-organizing wireless communication network is composed of an agent 1 and a wireless communication network transmission device.
  • the self-organizing wireless communication network is divided into an underwater self-organizing wireless communication network, an air self-organizing wireless communication network and a terrestrial self-organizing wireless communication network; Agent 1 and Land Agent 1.
  • the underwater agent 1 is an underwater unmanned vehicle 2 ; the aerial agent 1 is an unmanned aerial vehicle 7 ; the land agent 1 is an unmanned vehicle 9 .
  • the underwater self-organizing wireless communication network is composed of an underwater unmanned vehicle 2, a water transfer station 3, a communication satellite 4 and a satellite ground receiving station 5, and the water transfer station 3 is connected with several underwater unmanned aerial vehicles.
  • the vehicle 2 is connected, and the underwater unmanned vehicle 2 is connected with the satellite ground receiving station 5 through the communication satellite 4 .
  • the air ad hoc wireless communication network includes an unmanned aerial vehicle 7 and a second communication network base station 6 , and the second communication network base station 6 is connected to several unmanned aerial vehicles 7 .
  • the terrestrial ad hoc wireless communication network includes an unmanned vehicle 9 and a third communication network base station 8 , and the third communication network base station 8 is connected to several unmanned vehicles 9 .
  • a control method of the above-mentioned multi-agent 1 system is characterized in that, comprising the following steps:
  • Step 1 Set the application scenario of the multi-agent 1 system
  • the agent 1 system select the sea, land and air joint search and rescue application scenario through the multi-channel interaction system 15, and generate the operating conditions and parameters of the multi-agent 1 system according to the selected application scenario and the multi-agent 1 knowledge base system, including the search and rescue coverage. area, system running time, search and rescue time period and detection target; the user 14 inputs the parameters through the multi-channel interactive system 15 and uploads them to the data server 12;
  • Step 2 Build a multi-agent 1 system
  • the multi-channel interaction system 15 judges the composition type and quantity of the agents 1 in the multi-agent 1 system according to the selected application scenario in combination with the multi-agent 1 knowledge base system, and determines the positioning method and the communication method according to the number and movement paths of the agents 1;
  • the multi-agent 1 system communication network system is composed of three parts: the interconnection network system, the satellite relay communication network system and the self-organizing wireless network system; the interconnection network system interconnects the wireless self-organizing network and the data storage server; the satellite relay communication network The system connects the underwater wireless self-organizing network and the relay network of the interconnection network; the wireless self-organizing network is responsible for the network services of multi-agent 1, and is divided into terrestrial wireless self-organizing network, air wireless self-organizing network and underwater wireless self-organizing network.
  • the internet
  • the underwater wireless self-organizing network uploads data and receives commands from the multi-channel interactive system 15 through the communication satellite 4 equipment on the underwater unmanned vehicle 2; the communication satellite 4 receives the data uploaded by the underwater wireless self-organizing network and multi-channel interaction
  • the instructions of the system 15 are transmitted to the satellite ground receiving station 5 and the underwater wireless ad hoc network respectively, and the satellite ground receiving station 5 is connected to the World Wide Web through the ground network to receive and send data and instructions;
  • the network accesses the World Wide Web through the ground base station of the existing communication network to complete the data and instruction interaction with the data server 12 and the multi-channel interaction system 15; transmission and receipt of instructions;
  • Step 4. Deploy and run the multi-agent 1 system and perceive environmental information
  • One of the agents 1 is selected as the master agent 1; the multi-channel interaction system 15 sends control instructions to the master agent 1 through the communication network, and the master agent 1 sends instructions to control other agents 1 to move to the deployment Location;
  • the agent 1 collects environmental information including images, voices, actions, and life signals, and transmits the collected information to the control module of the agent 1;
  • the agent 1 transmits the information to the main control agent 1 through the communication network, and the transmission path of the information is determined by the communication topology;
  • Step 5 Perform multi-source heterogeneous information fusion processing on the transmitted information
  • the multi-channel interaction system 15 sends motion control instructions through the communication network according to the real-time positioning information of the target, and the agent 1 moves after receiving the instructions, ensuring that the target is always in the best monitoring position of the agent 1; at the same time, the multi-channel interaction system 15 displays the target position. information;
  • Step 7. User 14 controls the operation
  • the user 14 checks the location information of the target in real time on the multi-channel interaction system 15, controls the operation of the agent 1 through the multi-channel interaction system 15, and issues actions, gestures, and voice commands, which are perceived by the multi-channel interaction system 15 or the agent 1.
  • a running command is issued to control the execution of the agent 1.
  • step (2) the specific steps of constructing the multi-agent 1 system in the step (2) are as follows:
  • the multi-channel interaction system 15 determines the composition type of the agent 1 in the multi-agent 1 system according to the selected application scenario in combination with the multi-agent 1 knowledge base system.
  • the agent 1 in the sea-land-air joint search and rescue application scenario includes unmanned vehicles 9, Human-machine 7 and underwater unmanned vehicle 2;
  • the multi-channel interactive system 15 determines the detection method according to the search and rescue time period. If the working time period is daytime, the image or life sensing module is selected for detection. If the search and rescue time period is night, the infrared or life sensing module is selected for detection; the detection method After determination, the control system determines the number of agents 1 according to the search and rescue coverage area and the detection range of a single agent 1, and generates the movement path of agent 1 at the same time;
  • the communication method between each agent 1 is determined according to the distance D between each agent 1 during operation: in land and air scenarios, when D>500m, 5G can be used , 4G, GPRS, communication satellite 4, short-wave communication, etc.; when D ⁇ 500m, Zig-Bee, Bluetooth (Bluetooth), wireless broadband (Wi-Fi), etc. can be used; underwater scene uses underwater acoustic communication;
  • the multi-channel interactive system 15 determines which positioning method to use according to the application scenario and positioning accuracy requirements, or uses a combination of multiple positioning methods: in an open field, the positioning accuracy is m-level, GPS positioning is used; indoors, the positioning accuracy is cm level, use WIFI or Bluetooth; under water, the positioning accuracy is cm level: use ultrasonic positioning;
  • the multi-channel interaction system 15 determines the number of mobile power stations that need to be configured in the multi-agent 1 system according to the endurance and running time of the agent 1 .
  • the method for networking a multi-agent system in the step (3) includes the following steps:
  • Multi-agents form a network without obvious master-slave relationship according to the networking protocol, that is, each network node acts as the master node and initiates network transmission requests, and realizes the master-slave assignment of nodes through software according to task requirements;
  • Each multi-agent relies on the flooding protocol to broadcast data packets, mainly including the address and machine code of the agent. Build a routing table and randomly assign an agent to the main controller;
  • the main control agent communicates with the upper computer through the gateway node and the communication network;
  • the upper computer has a new algorithm for assigning the multi-agent master controller, then re-designate the master controller agent, re-assign the master-slave control node, repeat step (3.3), if not, the network is in the stage of waiting for the response command;
  • the upper computer transmits the task instruction through the interconnected communication network; the task instruction comes from the user instruction extracted by the upper computer, or the task instruction generated according to the knowledge base in the data server;
  • the communication network sends the task instructions issued by the host computer to each agent layer by layer;
  • Each agent transmits the corresponding command response or data back to the data server or host computer through the land, sea and air wireless ad hoc network.

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Abstract

提供了一种多智能体系统，包括自组织无线通信网络，自组织无线通信网络与互联网（10）连接，互联网（10）分别又与网关设备（11）和第一通信网络基站（13）连接，第一通信网络还与多通道交互系统（15）连接，多通道交互系统（15）与用户（14）连接；网关设备（11）还与数据服务器（12）连接。可以迅速、可靠的将多智能体系统应用在不同场景中，执行不同的任务，系统会根据选择的应用场景和输入的参数，自动生成系统初始运行策略，并且在运行过程中实时改变运行策略，具有较高的自主性和协调性。

Description

一种多智能体系统及其控制方法 技术领域
本发明属于智能自主无人系统技术领域，特别涉及一种多智能体系统及其控制方法。
背景技术
自主无人系统技术是人工智能重点关注的关键技术之一，其主要特点是智能化、控制系统、动态组网和人机关系。为此，5G技术提供的网络能力将满足三大极限业务场景需求，为用户提供更高速率和更好的业务体验，包括提供的高速率、高带宽业务能力，可支持超高清3D视频、VR、AR等业务；提供的低功耗、高连接密度能力，可支持监控、传感器、智慧城市等应用；提供的超低时延、高可靠性通信能力，可支持自动驾驶、远程医疗、智慧工厂、人工智能等应用。因此5G技术可促进各行各业的智能化和自动化通信需求。
多智能体系统是由一系列相互作用的智能体构成，各个智能体之间通过通信、合作、协调、调度、管理和控制等方式来表达系统的结构、功能及行为特性，完成单个智能体不能完成的大量而又复杂的工作。多智能体系统具有自主性、分布性、协调性，并具有自组织能力、学习能力和推理能力，因而采用多智能体系统解决实际问题，可以代替单个智能体或人工，很难或无法完成的工作，并具有很强的鲁棒性和可靠性。
多智能体系统自20世纪70年代被提出以来，就在各个领域迅速地得到了应用，例如军事协同作战、航空编队、城市管理、智能交通、联合搜救、海底探测等。专利“一种无人飞行器城市空中管理系统”(CN201711285844.5)、“一种应用于智能交通的无人机系统”(CN201810734095.8)、“一种无人机群搜索救援方法及系统”(CN201810892369.6)、“用于海底电缆巡检的水下机器人控制系统及方法”(CN201711259977.5)等，实现了多智能体系统在具体指定的不同场景下的应用，并有效、迅速的完成指定的相关作业任务，自动化、智能化程度较高；“一种基于图形化表示的多智能体系统生成方法”(ZL201410133930.4)采用图形化方式编辑构建自定义的多智能体系统，提高了智能体系统的开发效率，该方法可以根据用户需求，快速生成多种多智能体系统；
但上述智能体及智能体系统的设计取决于指定的应用场景和功能需求，因此形成的智能体及系统功能单一，应用场景存在局限性；当有不同应用场景任务需求时，需要对原系统进行重新设计或改造，即现有系统无法满足应用场景多样、变化的使用需求；此外，上述多智能体系统中的单个智能体，在系统中的功能是 预先设定的具体、单一功能，当系统中某个智能体发生故障时，会影响整个系统的运行，系统鲁棒性差、可靠性不足。
在多样、变化的应用场景中，多智能体系统对环境信息的感知精准度对于控制多智能体系统运行至关重要，但目前多智能体系统感知场景信息的方式单一，如专利一种数字图像中的目标定位方法以及装置(ZL201410359215.2)公开了一种利用数字图像进行目标定位的方法，首先获取目标Gabor滤波形状模板，利用Gabor滤波结果进行形状匹配定位，再利用骨架特征之间的相似度判定目标所在区域；专利一种水下机器人目标定位识别方法和系统(ZL201710209500.X)公开了一种利用声呐信息进行目标定位的方法；专利无线传感器网络静止目标定位方法及系统(ZL201310145553.1)公开了一种利用无线网络进行目标定位的方法。
当多智能体系统的应用环境变化影响了信息获取的精度，则会导致感知的环境信息精准度降低，因此需要多智能体系统能够采用多种方式感知环境信息，进行多源异构信号融合处理后，控制多智能体系统运行。若多智能体系统的目标是随机运动的，现有多智能体系统很难预测下一时刻目标可能出现的位置，导致智能体不能较好的获取下一时刻目标的信息，甚至丢失目标。
此外，现有的多智能体系统在实际应用过程中，人机交互性差，无法实现多通道全息的信息交换，用户操控系统的便捷度和精准度较低。
因此，需要开发适用于多领域场景，具备动态运行策略，自主性、协调性、可靠性更高，且人机交互友好的多智能体系统。
发明内容
发明目的：针对现有技术存在的问题，提供一种多智能体系统及其控制方法，可以迅速、可靠的将多智能体系统应用在不同场景中，执行不同的任务，系统会根据选择的应用场景和输入的参数，自动生成系统初始运行策略，并且在运行过程中实时改变运行策略，提升多智能体系统自主性和协调性。
技术方案：为解决上述技术问题，本发明提供一种多智能体系统，包括自组织无线通信网络，所述自组织无线通信网络与互联网连接，所述互联网分别又与网关设备和第一通信网络基站连接，所述第一通信网络还与多通道交互系统连接，所述多通道交互系统与用户连接；所述网关设备还与数据服务器连接。
进一步的，所述自组织无线通信网络由智能体和无线通信网络传输装置构成。
进一步的，所述自组织无线通信网络分为水下自组织无线通信网络、空中自组织无线通信网络和陆地自组织无线通信网络；对应的，智能体也分为水下智能体、空中智能体和陆地智能体。
进一步的，所述水下智能体为水下无人航行器；所述空中智能体为无人机；所述陆地智能体为无人车。
进一步的，所述水下自组织无线通信网络由水下无人航行器、水上中转站、通信卫星和卫星地面接收站构成，所述水上中转站与若干个水下无人航行器连接，所述水下无人航行器通过通信卫星与卫星地面接收站连接。
进一步的，所述空中自组织无线通信网络包括无人机和第二通信网络基站，所述第二通信网络基站与若干个无人机连接。
进一步的，所述陆地自组织无线通信网络包括无人车和第三通信网络基站，所述第三通信网络基站与若干个无人车连接。
一种如上所述的多智能体系统的控制方法，其特征在于，包括如下步骤：
步骤1、设置多智能体系统应用场景
启动运行智能体系统，通过多通道交互系统选择海陆空联合搜救应用场景，根据选取的应用场景结合多智能体知识库系统生成多智能体系统的运行条件和参数，具体包括搜救覆盖面积，系统运行时长，搜救时间段和检测目标；用户通过多通道交互系统输入参数，并上传到数据服务器中；
步骤2、构建多智能体系统
多通道交互系统根据选择的应用场景结合多智能体知识库系统判断多智能体系统中智能体的组成类型和数量，并根据智能体数量和运动路径确定定位方式和通信方式；
步骤3、多智能体系统组网
多智能体系统通信网络系统由互连网络系统、卫星中继通信网络系统和自组织无线网络系统三部分构成；互连网络系统互连无线自组织网络和数据存储服务器；卫星中继通信网络系统联系水下无线自组织网络和互连网络的中继网络；无线自组织网络，负责多智能体的网络服务，分为陆地无线自组织网络、空中无线自组织网络和水下无线自组织网络；
水下无线自组织网络通过水下无人航行器上的通信卫星设备上传数据和接收来自多通道交互系统的指令；通信卫星接收水下无线自组织网络上传的数据和多通道交互系统的指令，并分别下传给卫星地面接收站和水下无线自组织网络，卫星地面接收站通过地面网络接入万维网进行数据和指令的接收和发送；陆上和 空中无线自组织网络则通过现有通信网络的地面基站接入万维网完成与数据服务器和多通道交互系统的数据和指令交互；陆海空中的多智能体通过无线自组织网络完成主控制器选择、数据传输和指令接收；
步骤4、部署运行多智能体系统并感知环境信息
智能体中选择一个智能体作为主控智能体；多通道交互系统通过通信网络将控制指令发送到主控智能体，主控智能体发送指令控制其它智能体运动到部署位置；
智能体采集，包括图像、语音、动作、生命信号的环境信息信息，并将采集到的信息传送给智能体的控制模块；
智能体通过通信网络将信息传输到主控智能体，由通信拓扑结构确定信息的传输路径；
步骤5、将传输的信息进行多源异构信息融合处理
步骤6、多智能体系统任务分配执行
多通道交互系统根据目标实时定位信息，通过通信网络发送运动控制指令，智能体接收到指令后运动，保证目标始终在智能体的最佳监测位置；同时多通道交互系统显示目标位置信息；
步骤7、用户控制操作
用户在多通道交互系统上实时查看目标的位置信息，通过多通道交互系统操作控制智能体运行，并发出动作、手势、语音指令，由多通道交互系统或智能体感知后发出运行指令控制智能体执行。
进一步的，所述步骤(2)中构建多智能体系统的具体步骤如下：
多通道交互系统根据选择的应用场景结合多智能体知识库系统判断多智能体系统中智能体的组成类型，海陆空联合搜救应用场景下的智能体包括无人车、无人机和水下无人航行器；
多通道交互系统根据搜救时间段来确定检测方式，若工作时间段为白天，则选择图像或生命感知模块进行检测，若搜救时间段为夜晚，则选择红外或生命感知模块进行检测；检测方式确定后，控制系统根据搜救覆盖面积和单个智能体的检测范围，确定智能体的数量，同时生成智能体的运动路径；
多智能体数量和运动路径确定后，根据运行过程中各个智能体之间的距离D 来确定各个智能体之间的通信方式：陆地和空中场景下，当D>500m，可采用5G、4G、GPRS、通信卫星、短波通信等；当D<500m，可采用Zig-Bee、蓝牙(Bluetooth)、无线宽带(Wi-Fi)等；水下场景则采用水声通信；
多通道交互系统根据应用场景和定位精度需求，确定采用何种定位方式，或多种定位方式组合使用：在开阔场地，定位精度为m级，则采用GPS定位；在室内，定位精度为cm级，则采用WIFI或蓝牙；在水下，定位精度为cm级：则采用超声波定位；
多通道交互系统根据智能体的续航能力和运行时长，判断多智能体系统需要配置移动电站的数量。
进一步的，所述步骤(3)中多智能体系统组网的方法包括以下步骤：
(3.1)多智能体组网
多智能体依据组网协议组成无明显主从关系的网络，即每个网络节点都做为主节点和发起网络传输请求，根据任务需求通过软件实现节点的主从分配；
(3.2)建立路由表
每个多智能体依赖泛洪协议向外广播数据包，主要包括智能体的地址、机器码，每个智能体根据接收到的数据包，识别出其它智能体的地址和机器码，并为其建立路由表，随机分配一个智能体为主控制器；
(3.3)主控制节点接入主干网络
主控智能体通过网关节点和通信网络与上位机通信；
(3.4)上位机接收信息
若上位机有新的分配多智能体主控制器的算法，则重新指定主控制器智能体，重新分配主从控制节点，重复步骤(3.3)，若无，则网络处于等待响应指令阶段；
(3.5)上位机发出任务指令
上位机通过互联通信网络传送任务指令；任务指令来源于上位机提取的用户指令，或根据数据服务器中知识库生成的任务指令；
(3.6)通信网络逐层响应
通信网络将上位机发出的任务指令逐层发送到各个智能体；
(3.7)智能体响应
各个智能体通过陆海空无线自组织网络将相应的指令响应或者数据传送回 数据服务器或者上位机。
与现有技术相比，本发明的优点在于：
(1)多智能体系统控制方法可以迅速、可靠的将多智能体系统应用在不同场景中，执行不同的任务，系统会根据选择的应用场景和输入的参数，自动生成系统初始运行策略，并且系统运行策略会在运行过程中实时改变，提升多智能体系统自主性和协调性；
(2)建立了多领域、多媒体、多层次的知识库系统，为多智能体系统实现运行策略动态改变提供支撑，保障多智能体系统在不同应用场景下的精准运行；知识库系统与多通道交互系统接口设置二级检索机制，避免信息检索噪音过大，提高检索效率和精准度；
(3)智能体采用模块化设计，由多个功能模块组成，每个功能模块通过快速插装的方式连接，可以快速拼接组合成具备不同功能的智能体，并且同一功能的智能体可拼接不同的形态，使得智能体实现海陆空三种状态下执行任务的功能，满足多场景应用需求。
附图说明
图1为本发明多智能体系统的结构示意图；
图2为本发明多智能体系统的控制方法流程图。
图中：1、智能体；2、水下无人航行器；3、水上中转站；4、通信卫星，5、卫星地面接收站，6、第二通信网络基站，7、无人机，8、第三通信网络基站，9、无人车，10、互联网，11、网关设备，12、数据服务器，13、第一通信网络基站，14、用户，15、多通道交互系统。
具体实施方式
下面结合附图和具体实施方式，进一步阐明本发明。本发明描述的实施例仅仅是本发明的一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有做出创造性劳动的前提下所得到的其他实施例，都属于本发明所保护的范围。
如图1所示，一种多智能体1系统，包括自组织无线通信网络，所述自组织无线通信网络与互联网10连接，所述互联网10分别又与网关设备11和第一通信网络基站13连接，所述第一通信网络还与多通道交互系统15连接，所述多通道交互系统15与用户14连接；所述网关设备11还与数据服务器12连接。
进一步的，所述自组织无线通信网络由智能体1和无线通信网络传输装置构成。
进一步的，所述自组织无线通信网络分为水下自组织无线通信网络、空中自组织无线通信网络和陆地自组织无线通信网络；对应的，智能体1也分为水下智能体1、空中智能体1和陆地智能体1。
进一步的，所述水下智能体1为水下无人航行器2；所述空中智能体1为无人机7；所述陆地智能体1为无人车9。
进一步的，所述水下自组织无线通信网络由水下无人航行器2、水上中转站3、通信卫星4和卫星地面接收站5构成，所述水上中转站3与若干个水下无人航行器2连接，所述水下无人航行器2通过通信卫星4与卫星地面接收站5连接。
进一步的，所述空中自组织无线通信网络包括无人机7和第二通信网络基站6，所述第二通信网络基站6与若干个无人机7连接。
进一步的，所述陆地自组织无线通信网络包括无人车9和第三通信网络基站8，所述第三通信网络基站8与若干个无人车9连接。
一种如上所述的多智能体1系统的控制方法，其特征在于，包括如下步骤：
步骤1、设置多智能体1系统应用场景
启动运行智能体1系统，通过多通道交互系统15选择海陆空联合搜救应用场景，根据选取的应用场景结合多智能体1知识库系统生成多智能体1系统的运行条件和参数，具体包括搜救覆盖面积，系统运行时长，搜救时间段和检测目标；用户14通过多通道交互系统15输入参数，并上传到数据服务器12中；
步骤2、构建多智能体1系统
多通道交互系统15根据选择的应用场景结合多智能体1知识库系统判断多智能体1系统中智能体1的组成类型和数量，并根据智能体1数量和运动路径确定定位方式和通信方式；
步骤3、多智能体1系统组网
多智能体1系统通信网络系统由互连网络系统、卫星中继通信网络系统和自组织无线网络系统三部分构成；互连网络系统互连无线自组织网络和数据存储服务器；卫星中继通信网络系统联系水下无线自组织网络和互连网络的中继网络；无线自组织网络，负责多智能体1的网络服务，分为陆地无线自组织网络、空中 无线自组织网络和水下无线自组织网络；
水下无线自组织网络通过水下无人航行器2上的通信卫星4设备上传数据和接收来自多通道交互系统15的指令；通信卫星4接收水下无线自组织网络上传的数据和多通道交互系统15的指令，并分别下传给卫星地面接收站5和水下无线自组织网络，卫星地面接收站5通过地面网络接入万维网进行数据和指令的接收和发送；陆上和空中无线自组织网络则通过现有通信网络的地面基站接入万维网完成与数据服务器12和多通道交互系统15的数据和指令交互；陆海空中的多智能体1通过无线自组织网络完成主控制器选择、数据传输和指令接收；
步骤4、部署运行多智能体1系统并感知环境信息
智能体1中选择一个智能体1作为主控智能体1；多通道交互系统15通过通信网络将控制指令发送到主控智能体1，主控智能体1发送指令控制其它智能体1运动到部署位置；
智能体1采集，包括图像、语音、动作、生命信号的环境信息信息，并将采集到的信息传送给智能体1的控制模块；
智能体1通过通信网络将信息传输到主控智能体1，由通信拓扑结构确定信息的传输路径；
步骤5、将传输的信息进行多源异构信息融合处理
步骤6、多智能体1系统任务分配执行
多通道交互系统15根据目标实时定位信息，通过通信网络发送运动控制指令，智能体1接收到指令后运动，保证目标始终在智能体1的最佳监测位置；同时多通道交互系统15显示目标位置信息；
步骤7、用户14控制操作
用户14在多通道交互系统15上实时查看目标的位置信息，通过多通道交互系统15操作控制智能体1运行，并发出动作、手势、语音指令，由多通道交互系统15或智能体1感知后发出运行指令控制智能体1执行。
进一步的，所述步骤(2)中构建多智能体1系统的具体步骤如下：
多通道交互系统15根据选择的应用场景结合多智能体1知识库系统判断多智能体1系统中智能体1的组成类型，海陆空联合搜救应用场景下的智能体1包括无人车9、无人机7和水下无人航行器2；
多通道交互系统15根据搜救时间段来确定检测方式，若工作时间段为白天，则选择图像或生命感知模块进行检测，若搜救时间段为夜晚，则选择红外或生命感知模块进行检测；检测方式确定后，控制系统根据搜救覆盖面积和单个智能体1的检测范围，确定智能体1的数量，同时生成智能体1的运动路径；
多智能体1数量和运动路径确定后，根据运行过程中各个智能体1之间的距离D来确定各个智能体1之间的通信方式：陆地和空中场景下，当D>500m，可采用5G、4G、GPRS、通信卫星4、短波通信等；当D<500m，可采用Zig-Bee、蓝牙(Bluetooth)、无线宽带(Wi-Fi)等；水下场景则采用水声通信；
多通道交互系统15根据应用场景和定位精度需求，确定采用何种定位方式，或多种定位方式组合使用：在开阔场地，定位精度为m级，则采用GPS定位；在室内，定位精度为cm级，则采用WIFI或蓝牙；在水下，定位精度为cm级：则采用超声波定位；
多通道交互系统15根据智能体1的续航能力和运行时长，判断多智能体1系统需要配置移动电站的数量。
所述步骤(3)中多智能体系统组网的方法包括以下步骤：
(3.1)多智能体组网
多智能体依据组网协议组成无明显主从关系的网络，即每个网络节点都做为主节点和发起网络传输请求，根据任务需求通过软件实现节点的主从分配；
(3.2)建立路由表
每个多智能体依赖泛洪协议向外广播数据包，主要包括智能体的地址、机器码，每个智能体根据接收到的数据包，识别出其它智能体的地址和机器码，并为其建立路由表，随机分配一个智能体为主控制器；
(3.3)主控制节点接入主干网络
主控智能体通过网关节点和通信网络与上位机通信；
(3.4)上位机接收信息
若上位机有新的分配多智能体主控制器的算法，则重新指定主控制器智能体，重新分配主从控制节点，重复步骤(3.3)，若无，则网络处于等待响应指令阶段；
(3.5)上位机发出任务指令
上位机通过互联通信网络传送任务指令；任务指令来源于上位机提取的用户 指令，或根据数据服务器中知识库生成的任务指令；
(3.6)通信网络逐层响应
通信网络将上位机发出的任务指令逐层发送到各个智能体；
(3.7)智能体响应
各个智能体通过陆海空无线自组织网络将相应的指令响应或者数据传送回数据服务器或者上位机。

Claims (10)

  1. 一种多智能体系统，其特征在于：包括自组织无线通信网络，所述自组织无线通信网络与互联网连接，所述互联网分别又与网关设备和第一通信网络基站连接，所述第一通信网络还与多通道交互系统连接，所述多通道交互系统与用户连接；所述网关设备还与数据服务器连接。
  2. 根据权利要求1所述的一种多智能体系统，其特征在于：所述自组织无线通信网络由智能体和无线通信网络传输装置构成。
  3. 根据权利要求1所述的一种多智能体系统，其特征在于：所述自组织无线通信网络分为水下自组织无线通信网络、空中自组织无线通信网络和陆地自组织无线通信网络；对应的，智能体也分为水下智能体、空中智能体和陆地智能体。
  4. 根据权利要求3所述的一种多智能体系统，其特征在于：所述水下智能体为水下无人航行器；所述空中智能体为无人机；所述陆地智能体为无人车。
  5. 根据权利要求3所述的一种多智能体系统，其特征在于：所述水下自组织无线通信网络由水下无人航行器、水上中转站、通信卫星和卫星地面接收站构成，所述水上中转站与若干个水下无人航行器连接，所述水下无人航行器通过通信卫星与卫星地面接收站连接。
  6. 根据权利要求3所述的一种多智能体系统，其特征在于：所述空中自组织无线通信网络包括无人机和第二通信网络基站，所述第二通信网络基站与若干个无人机连接。
  7. 根据权利要求3所述的一种多智能体系统，其特征在于：所述陆地自组织无线通信网络包括无人车和第三通信网络基站，所述第三通信网络基站与若干个无人车连接。
  8. 一种如权利要求1-7之一所述的多智能体系统的控制方法，其特征在于，包括如下步骤：
    (1)设置多智能体系统应用场景：
    启动运行智能体系统，通过多通道交互系统选择海陆空联合搜救应用场景，根据选取的应用场景结合多智能体知识库系统生成多智能体系统的运行条件和参数，具体包括搜救覆盖面积，系统运行时长，搜救时间段和检测目标；用户通过多通道交互系统输入参数，并上传到数据服务器中；
    (2)构建多智能体系统：
    多通道交互系统根据选择的应用场景结合多智能体知识库系统判断多智能体系统中智能体的组成类型和数量，并根据智能体数量和运动路径确定定位方式和通信方式；
    (3)多智能体系统组网：
    多智能体系统通信网络系统由互连网络系统、卫星中继通信网络系统和自组织无线网络系统三部分构成；互连网络系统互连无线自组织网络和数据存储服务器；卫星中继通信网络系统联系水下无线自组织网络和互连网络的中继网络；无线自组织网络，负责多智能体的网络服务，分为陆地无线自组织网络、空中无线自组织网络和水下无线自组织网络；
    水下无线自组织网络通过水下无人航行器上的通信卫星设备上传数据和接收来自多通道交互系统的指令；通信卫星接收水下无线自组织网络上传的数据和多通道交互系统的指令，并分别下传给卫星地面接收站和水下无线自组织网络，卫星地面接收站通过地面网络接入万维网进行数据和指令的接收和发送；陆上和空中无线自组织网络则通过现有通信网络的地面基站接入万维网完成与数据服务器和多通道交互系统的数据和指令交互；陆海空中的多智能体通过无线自组织网络完成主控制器选择、数据传输和指令接收；
    (4)部署运行多智能体系统并感知环境信息：
    智能体中选择一个智能体作为主控智能体；多通道交互系统通过通信网络将控制指令发送到主控智能体，主控智能体发送指令控制其它智能体运动到部署位置；
    智能体采集，包括图像、语音、动作、生命信号的环境信息信息，并将采集到的信息传送给智能体的控制模块；
    智能体通过通信网络将信息传输到主控智能体，由通信拓扑结构确定信息的传输路径；
    (5)将传输的信息进行多源异构信息融合处理；
    (6)多智能体系统任务分配执行：
    多通道交互系统根据目标实时定位信息，通过通信网络发送运动控制指令，智能体接收到指令后运动，保证目标始终在智能体的最佳监测位置；同时多通道交互系统显示目标位置信息；
    (7)用户控制操作：
    用户在多通道交互系统上实时查看目标的位置信息，通过多通道交互系统操作控制智能体运行，并发出动作、手势、语音指令，由多通道交互系统或智能体感知后发出运行指令控制智能体执行。
  9. 根据权利要求8所述的一种多智能体系统的控制方法，其特征在于，所述步骤(2)中构建多智能体系统的具体步骤如下：
    多通道交互系统根据选择的应用场景结合多智能体知识库系统判断多智能体系统中智能体的组成类型，海陆空联合搜救应用场景下的智能体包括无人车、无人机和水下无人航行器；
    多通道交互系统根据搜救时间段来确定检测方式，若工作时间段为白天，则选择图像或生命感知模块进行检测，若搜救时间段为夜晚，则选择红外或生命感知模块进行检测；检测方式确定后，控制系统根据搜救覆盖面积和单个智能体的检测范围，确定智能体的数量，同时生成智能体的运动路径；
    多智能体数量和运动路径确定后，根据运行过程中各个智能体之间的距离D来确定各个智能体之间的通信方式：陆地和空中场景下，当D>500m，可采用5G、4G、GPRS、通信卫星、短波通信等；当D<500m，可采用Zig-Bee、蓝牙(Bluetooth)、无线宽带(Wi-Fi)等；水下场景则采用水声通信；
    多通道交互系统根据应用场景和定位精度需求，确定采用何种定位方式，或多种定位方式组合使用：在开阔场地，定位精度为m级，则采用GPS定位；在室内，定位精度为cm级，则采用WIFI或蓝牙；在水下，定位精度为cm级：则采用超声波定位；
    多通道交互系统根据智能体的续航能力和运行时长，判断多智能体系统需要配置移动电站的数量。
  10. 根据权利要求8所述的一种多智能体系统的控制方法，其特征在于，所述步骤(3)中多智能体系统组网的方法包括以下步骤：
    (3.1)多智能体组网
    多智能体依据组网协议组成无明显主从关系的网络，即每个网络节点都做为主节点和发起网络传输请求，根据任务需求通过软件实现节点的主从分配；
    (3.2)建立路由表
    每个多智能体依赖泛洪协议向外广播数据包，主要包括智能体的地址、机器码，每个智能体根据接收到的数据包，识别出其它智能体的地址和机器码，并为其建立路由表，随机分配一个智能体为主控制器；
    (3.3)主控制节点接入主干网络
    主控智能体通过网关节点和通信网络与上位机通信；
    (3.4)上位机接收信息
    若上位机有新的分配多智能体主控制器的算法，则重新指定主控制器智能体，重新分配主从控制节点，重复步骤(3.3)，若无，则网络处于等待响应指令阶段；
    (3.5)上位机发出任务指令
    上位机通过互联通信网络传送任务指令；任务指令来源于上位机提取的用户指令，或根据数据服务器中知识库生成的任务指令；
    (3.6)通信网络逐层响应
    通信网络将上位机发出的任务指令逐层发送到各个智能体；
    (3.7)智能体响应
    各个智能体通过陆海空无线自组织网络将相应的指令响应或者数据传送回数据服务器或者上位机。
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