蚁群优化理论在无人机战术控制中的应用研究
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摘要
随着无人机(Unmanned Aerial Vehicle,UAV)在现代战争中越来越广泛的应用,以及适应新军事变革要求的联合无人机作战概念的不断发展,无人机战术控制日益成为缩短无人机传感器到战术用户之间的信息决策链,实现跨建制、网络化、扁平化的无人机指挥与控制,发挥多无人机协同和集群优势的关键问题。无人机战术控制不仅涉及公共的信息标准、开放的体系结构和可重构的无人机任务规划与指挥控制功能组件等基本问题,而且还需要解决在复杂战场环境中多无人机协同执行多用户战术任务的高层协调控制问题,这对现有的优化理论和控制方法提出了挑战。
本文以无人机战术控制为背景,开展蚁群优化理论在多无人机协同多任务规划、多用户情报分发路由、多无人机任务自组织控制等关键问题中的应用研究,改进基本蚁群算法、扩展蚁群优化机制,探索能够有效解决无人机战术控制这一类多主体协同控制与多目标优化问题的新方法。本文主要研究内容及成果如下:
(1)提出了再励学习蚁群算法(Reinforcemant Learning Ant Colony Algorithm,RLACA);讨论了多子群蚁群优化思想,提出了有效降低算法复杂度的预编序号策略和控制配队策略。在基本蚁群算法基础之上,围绕信息素更新机制,针对当前改进算法蚁群信息利用不充分的问题,提出了信息素的再励学习(RL)更新机制。仿真实验证明,引入RL机制的RLACA算法体现出更快的收敛速度和更强的全局搜索能力。讨论了多子群蚁群算法,给出了求解不同子问题的异质多子群算法框架,分析了算法的复杂性;针对“子问题结合”空间搜索的指数级复杂性问题,提出了预编序号策略和控制配队策略,有效地将算法复杂度降低到特定规模。
(2)开展了蚁群算法在无人机协同多任务规划中的应用研究。根据分层递阶控制的思想,将无人机协同多任务规划问题分解为协同多任务分配问题和航路规划问题,有效降低了原问题求解的复杂性。针对协同多任务分配问题,对通用CMTAP问题模型进行扩展,建立了协同多任务分配模型。在此基础上,基于异质多子群蚁群算法框架,引入预编序号策略,设计了基于分工机制的任务分配蚁群算法。仿真结果表明,基于分工机制的任务分配蚁群算法能够有效解决复杂约束条件下的协同多任务分配问题,并具有对动态变化任务需求响应的敏捷性。针对复杂环境下无人机航路规划问题,引入概率地图对战场环境进行拓扑化描述,在此基础上运用引入信息素再励更新机制的蚁群算法进行航路规划,提高了算法的求解效率。仿真实验结果表明算法具有较高的规划速度和良好的求解精度。
(3)开展了蚁群算法在无人机情报分发路由中的应用研究。从最大程度满足用户需求、降低网络通信负载出发,给出了需求满足最大化的单播路由模型以及费用最小化的多播路由模型。在此基础上,针对情报单播分发的敏捷性要求,在RLACA算法的基础上调整了蚁群状态转移规则,设计了信息素的局部更新机制,提出了求解情报分发单播路由的蚁群算法。仿真实验表明,算法能快速获取问题的(近似)全局最优解。针对当前多播路由算法易陷入局部极值的问题,在异质多子群蚁群优化基础上引入控制配队策略,设计了配队蚁群算法(TMACA)。TMACA采用基于多播树结构的信息素广域再励更新机制,在提升收敛速度的同时保证了全局搜索能力;仿真实验结果表明,TMACA具有跳出局部极值的能力,而且能快速收敛到(近似)全局最优解,算法性能不随网络规模的增长而急剧变化。
(4)提出了不确定环境下无人机任务自组织的分布式蚁群算法。就如何提高无人机对未来不确定战场环境的自主作战能力,通过分析蚁群中兵蚁的搜捕行为,设计了与无人机行为特性相适应的分布式搜捕蚁群算法(Distributed Raid-PatternAnt Colony Algorithm,DRPACA)。DRPACA采用无中心节点的体系结构,保证了复杂环境下系统的稳定性与生存能力。DRPACA以蚂蚁作为无人机代理,针对环境不确定性、无人机有限探测能力与多机任务效能最大化的矛盾,蚂蚁之间采取基于信息素的间接通信和基于数据链的直接通信,增强了蚂蚁对战场环境以及任务进展的感知能力;针对环境的动态特性以及打击目标的有限价值特性,提出了时变、全局/局部相结合的信息素更新机制,增强了算法的多任务覆盖能力;针对蚂蚁之间直接通信的延迟和数据丢失问题,提出了基于信息素分布的状态预估模型,增强了蚁群行为的协同性。仿真结果表明,该方法能够控制无人机集群在保证对任务区域的侦察覆盖的前提下,对敌方目标实施快速有效的压制打击,同时适应动态变化的战场环境和任务需求。
Since unmanned aerial vehicles (UAVs) are widely used in modern wars and the concept of UAVs joint combat develops which is suitable to the new revolution in military affairs, UAV tactical control (UAVTC) becomes a key problem in performing the swarm predominance of UAVs. UAVTC will shorten the decision making chain from payloads on the vehicle to tactical users, and the cross-organic, flat and network-based control of UAVs will be realized. UAVTC relates to not only public information standards, open architecture and the configurable functional components of mission planning and C2 (Command and Control), but also the cooperation and control of UAVs conducting tactical tasks in complicated battleground, which challenges the existing optimization theories and control methods.
The dissertation researches some key problems of UAVTC based on ant colony optimization theory, including multiple UAVs cooperative mission planning, network routing in UAV intelligence dissemination and self-organization of UAVs. We improve the classic ant colony algorithm (ACA), extend ACA optimization mechanism to explore new methodologies to resolve the UAVTC, which is a problem of multi-objective optimization and multi-agent cooperative control. The main work and contributions of this dissertation are as follow:
(1) A reinforcement learning ant colony algorithm (RLACA) is proposed, a pre-numbered strategy and a team-matching strategy are presented to simplify the complexity of multi-ant-colony optimization (MACO) in the dissertation. A reinforcement learning mechanism (RLM) for pheromone updating is presented to solve the problem of ant colony information utilized insufficiently in some other improved ACAs. Simulation results show that the RLACA, in which RLM is introduced, converges faster to a better resolution than some other algorithms. MACO is also discussed, and a framework of multi-difference-ant-colony algorithm (MDACA) is presented, the complexity is analyzed. The Pre-numbered strategy and the team-matching strategy are proposed to solve the "combination of sub-problems" in MDACA, so that the exponential complexity of computation declines to determined scale.
(2) The application of ACA in multi-UAV cooperative mission planning is researched. In order to decrease the complexity of multi-UAV cooperative mission planning, the hierarchical and iterative strategy is applied. And then the problem is decoupled and decomposed into two coherent sub-problems, i.e. task allocation and vehicle route planning. Afterwards an extended mathematical model of task allocation based on the CMTAP is established, and an ACA on the basis of job-division mechanism is designed according to MDACA. A pre-numbered strategy is introduced into the algorithm to decrease the computational complexity. The simulation results show that the multi-tasks allocation problem under complex constraints can be solved effectively using the algorithm. Probabilistic road map (PRM) is introduced to describe the topology of battlefield, which can decrease the complexity of battlefield in vehicle route planning. Then another ACA, which introduce the reinforcement learning mechanism to enhance the search efficiency, is designed to plan the routes based on the topology description. The simulation results demonstrate that the algorithm can converge to a good feasible solution rapidly.
(3) The application of ACA in UAV intelligence dissemination routing is researched. To satisfy tactical users (TUs) mostly and lighten the burden of the net, a unicast routing mathematical model, which maximizes the degree of TUs' satisfaction, and a multicast routing mathematical model, which minimizes the communicational cost, is established. Then a new ACA for intelligence dissemination unicast routing is designed based on RLACA. The state transition rule is improved and a new mechanism for local pheromone updating is designed in the algorithm to get a good fesaible route rapidly. The simulation results show that the algorithm can converge to a nearly optimal route quickly. A team-matching ant colony algorithm (TMACA) is designed based on MDACA, which introduce the team-matching strategy to overcome the prematurity of colony. TMACA adopts a wide-range pheromone updating mechanism based on the structure of multicast tree. Thus the algorithm can quickly converge to a feasible solution and keep the global search capacity. The simulation results show that TMACA can escape from the local optimum and then converge to the global best solution rapidly, and its performance will not be tempestuously influenced as the network size increases.
(4) An ant colony algorithm for UAVs self-organization in the uncertain environment is designed. A distributed raid-pattern ant colony algorithm (DRPACA) on the basis of the raid behavior of ant-soldiers is designed according to the specialties of UAV. DRPACA will enhance UAVs' adaptive ability to the uncertain environment. To keep the stability and viability of the system, DRPACA takes a distributed, decentralized architecture. And there is indirect communication based on pheromone and direct communication via data link between ants, so that ants can effectively sense the environment and the task progress. A new mechanism of pheromone updating is presented, according to the dynamic environment and value-restricted targets. A state prediction model based on the distribution of pheromone is proposed to decrease the influence of delay and packet losses in the direct communication. With the mechanism, DRPACA can effectively perform different tasks. The simulation results demonstrate that the method can effectively coordinate the UAVs conducting reconnaissance and attack tasks over mission-area, and be adaptive to the dynamic environment and tasks.
本文以无人机战术控制为背景,开展蚁群优化理论在多无人机协同多任务规划、多用户情报分发路由、多无人机任务自组织控制等关键问题中的应用研究,改进基本蚁群算法、扩展蚁群优化机制,探索能够有效解决无人机战术控制这一类多主体协同控制与多目标优化问题的新方法。本文主要研究内容及成果如下:
(1)提出了再励学习蚁群算法(Reinforcemant Learning Ant Colony Algorithm,RLACA);讨论了多子群蚁群优化思想,提出了有效降低算法复杂度的预编序号策略和控制配队策略。在基本蚁群算法基础之上,围绕信息素更新机制,针对当前改进算法蚁群信息利用不充分的问题,提出了信息素的再励学习(RL)更新机制。仿真实验证明,引入RL机制的RLACA算法体现出更快的收敛速度和更强的全局搜索能力。讨论了多子群蚁群算法,给出了求解不同子问题的异质多子群算法框架,分析了算法的复杂性;针对“子问题结合”空间搜索的指数级复杂性问题,提出了预编序号策略和控制配队策略,有效地将算法复杂度降低到特定规模。
(2)开展了蚁群算法在无人机协同多任务规划中的应用研究。根据分层递阶控制的思想,将无人机协同多任务规划问题分解为协同多任务分配问题和航路规划问题,有效降低了原问题求解的复杂性。针对协同多任务分配问题,对通用CMTAP问题模型进行扩展,建立了协同多任务分配模型。在此基础上,基于异质多子群蚁群算法框架,引入预编序号策略,设计了基于分工机制的任务分配蚁群算法。仿真结果表明,基于分工机制的任务分配蚁群算法能够有效解决复杂约束条件下的协同多任务分配问题,并具有对动态变化任务需求响应的敏捷性。针对复杂环境下无人机航路规划问题,引入概率地图对战场环境进行拓扑化描述,在此基础上运用引入信息素再励更新机制的蚁群算法进行航路规划,提高了算法的求解效率。仿真实验结果表明算法具有较高的规划速度和良好的求解精度。
(3)开展了蚁群算法在无人机情报分发路由中的应用研究。从最大程度满足用户需求、降低网络通信负载出发,给出了需求满足最大化的单播路由模型以及费用最小化的多播路由模型。在此基础上,针对情报单播分发的敏捷性要求,在RLACA算法的基础上调整了蚁群状态转移规则,设计了信息素的局部更新机制,提出了求解情报分发单播路由的蚁群算法。仿真实验表明,算法能快速获取问题的(近似)全局最优解。针对当前多播路由算法易陷入局部极值的问题,在异质多子群蚁群优化基础上引入控制配队策略,设计了配队蚁群算法(TMACA)。TMACA采用基于多播树结构的信息素广域再励更新机制,在提升收敛速度的同时保证了全局搜索能力;仿真实验结果表明,TMACA具有跳出局部极值的能力,而且能快速收敛到(近似)全局最优解,算法性能不随网络规模的增长而急剧变化。
(4)提出了不确定环境下无人机任务自组织的分布式蚁群算法。就如何提高无人机对未来不确定战场环境的自主作战能力,通过分析蚁群中兵蚁的搜捕行为,设计了与无人机行为特性相适应的分布式搜捕蚁群算法(Distributed Raid-PatternAnt Colony Algorithm,DRPACA)。DRPACA采用无中心节点的体系结构,保证了复杂环境下系统的稳定性与生存能力。DRPACA以蚂蚁作为无人机代理,针对环境不确定性、无人机有限探测能力与多机任务效能最大化的矛盾,蚂蚁之间采取基于信息素的间接通信和基于数据链的直接通信,增强了蚂蚁对战场环境以及任务进展的感知能力;针对环境的动态特性以及打击目标的有限价值特性,提出了时变、全局/局部相结合的信息素更新机制,增强了算法的多任务覆盖能力;针对蚂蚁之间直接通信的延迟和数据丢失问题,提出了基于信息素分布的状态预估模型,增强了蚁群行为的协同性。仿真结果表明,该方法能够控制无人机集群在保证对任务区域的侦察覆盖的前提下,对敌方目标实施快速有效的压制打击,同时适应动态变化的战场环境和任务需求。
Since unmanned aerial vehicles (UAVs) are widely used in modern wars and the concept of UAVs joint combat develops which is suitable to the new revolution in military affairs, UAV tactical control (UAVTC) becomes a key problem in performing the swarm predominance of UAVs. UAVTC will shorten the decision making chain from payloads on the vehicle to tactical users, and the cross-organic, flat and network-based control of UAVs will be realized. UAVTC relates to not only public information standards, open architecture and the configurable functional components of mission planning and C2 (Command and Control), but also the cooperation and control of UAVs conducting tactical tasks in complicated battleground, which challenges the existing optimization theories and control methods.
The dissertation researches some key problems of UAVTC based on ant colony optimization theory, including multiple UAVs cooperative mission planning, network routing in UAV intelligence dissemination and self-organization of UAVs. We improve the classic ant colony algorithm (ACA), extend ACA optimization mechanism to explore new methodologies to resolve the UAVTC, which is a problem of multi-objective optimization and multi-agent cooperative control. The main work and contributions of this dissertation are as follow:
(1) A reinforcement learning ant colony algorithm (RLACA) is proposed, a pre-numbered strategy and a team-matching strategy are presented to simplify the complexity of multi-ant-colony optimization (MACO) in the dissertation. A reinforcement learning mechanism (RLM) for pheromone updating is presented to solve the problem of ant colony information utilized insufficiently in some other improved ACAs. Simulation results show that the RLACA, in which RLM is introduced, converges faster to a better resolution than some other algorithms. MACO is also discussed, and a framework of multi-difference-ant-colony algorithm (MDACA) is presented, the complexity is analyzed. The Pre-numbered strategy and the team-matching strategy are proposed to solve the "combination of sub-problems" in MDACA, so that the exponential complexity of computation declines to determined scale.
(2) The application of ACA in multi-UAV cooperative mission planning is researched. In order to decrease the complexity of multi-UAV cooperative mission planning, the hierarchical and iterative strategy is applied. And then the problem is decoupled and decomposed into two coherent sub-problems, i.e. task allocation and vehicle route planning. Afterwards an extended mathematical model of task allocation based on the CMTAP is established, and an ACA on the basis of job-division mechanism is designed according to MDACA. A pre-numbered strategy is introduced into the algorithm to decrease the computational complexity. The simulation results show that the multi-tasks allocation problem under complex constraints can be solved effectively using the algorithm. Probabilistic road map (PRM) is introduced to describe the topology of battlefield, which can decrease the complexity of battlefield in vehicle route planning. Then another ACA, which introduce the reinforcement learning mechanism to enhance the search efficiency, is designed to plan the routes based on the topology description. The simulation results demonstrate that the algorithm can converge to a good feasible solution rapidly.
(3) The application of ACA in UAV intelligence dissemination routing is researched. To satisfy tactical users (TUs) mostly and lighten the burden of the net, a unicast routing mathematical model, which maximizes the degree of TUs' satisfaction, and a multicast routing mathematical model, which minimizes the communicational cost, is established. Then a new ACA for intelligence dissemination unicast routing is designed based on RLACA. The state transition rule is improved and a new mechanism for local pheromone updating is designed in the algorithm to get a good fesaible route rapidly. The simulation results show that the algorithm can converge to a nearly optimal route quickly. A team-matching ant colony algorithm (TMACA) is designed based on MDACA, which introduce the team-matching strategy to overcome the prematurity of colony. TMACA adopts a wide-range pheromone updating mechanism based on the structure of multicast tree. Thus the algorithm can quickly converge to a feasible solution and keep the global search capacity. The simulation results show that TMACA can escape from the local optimum and then converge to the global best solution rapidly, and its performance will not be tempestuously influenced as the network size increases.
(4) An ant colony algorithm for UAVs self-organization in the uncertain environment is designed. A distributed raid-pattern ant colony algorithm (DRPACA) on the basis of the raid behavior of ant-soldiers is designed according to the specialties of UAV. DRPACA will enhance UAVs' adaptive ability to the uncertain environment. To keep the stability and viability of the system, DRPACA takes a distributed, decentralized architecture. And there is indirect communication based on pheromone and direct communication via data link between ants, so that ants can effectively sense the environment and the task progress. A new mechanism of pheromone updating is presented, according to the dynamic environment and value-restricted targets. A state prediction model based on the distribution of pheromone is proposed to decrease the influence of delay and packet losses in the direct communication. With the mechanism, DRPACA can effectively perform different tasks. The simulation results demonstrate that the method can effectively coordinate the UAVs conducting reconnaissance and attack tasks over mission-area, and be adaptive to the dynamic environment and tasks.
引文
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