混沌蚁群算法在多机器人任务规划中的应用研究
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摘要
多机器人系统中,任务规划对改善移动机器人的导航性能,减少机器人在移动过程中出现的不确定性有着至关重要的作用。在诸如星球探测,智能交通,电子装配等应用领域,机器人是一种高度受限的资源,其导航性能直接影响各应用系统的效率。
蚁群算法是依据蚂蚁群在搜索食物源的过程中所体现出来的寻优能力而提出来的一种新型的启发式、分布式协作寻优仿生算法。其特点是算法结构简单,具有较强的鲁棒性和发现较好解的能力。蚁群算法已广泛应用于旅行商问题等领域。
本论文针对多机器人任务规划问题,研究了同时防止早熟和提高算法收敛速度的若干算法,从理论或应用的角度分析了所述方法的有效性,主要创新包括：
1.在混沌蚁群算法的基础上进行了改进,采用的改进策略有：回程优化策略、精英策略和去交叉策略。在改进的混沌蚁群算法的基础上,针对多机器人任务规划问题提出了正交混沌蚁群算法。该算法是基于传统的解决多旅行商问题的思路,采用集中分配,分布式规划方法,该算法分为两个步骤：正交聚类和混沌蚁群求解单机器人路径规划。该算法的特点在于正交聚类法的低时间复杂度。正交聚类法是解决任务分配问题的一种有效方法,它利用正交表进行分配,对任务分配方案作最优设计。正交聚类法和混沌技术的引入,经过较少的迭代次数就可以找到较优解,对于求解中大规模任务规划问题是十分有利的。
2.针对动态环境中单机器人路径规划目标任务点可能动态添加或删除的特点,利用弹性网络的动态适应性提出了弹性适应混沌蚁群算法。针对动态多机器人任务规划问题的目标点的动态性和部分机器人也可能会出现故障的特点,结合正交混沌蚁群算法,提出了基于多机器人任务规划的弹性适应混沌蚁群算法,在一定的假设下,通过实验证明了该算法的有效性。
Mission Planning is essential for Multi-Robot Systems to improve the navigation performance of the robots and reduce their uncertainty when they are moving. In the areas of planet detection, intelligent transportation, and electronic assembly applications, Robot is a highly limited resource, and its navigation performance affects the efficiency of various application systems directly.
Ant Colony Algorithm is a newly heuristic and cooperative simulation algorithm for evolution, inspired by the evolutionary ability of the real ants when they are searching food. It owns simple structure, strong robustness and better solution. Therefore, the Ant Colony Algorithm has been widely used in the field of traveller and so on.
In the light of multi-robot task planning, this paper studies some algorithms to prevent prematurity and increase convergence speed of the algorithms. Analyzing all the methods by theory and application, we find they are effective. The main innovations include:
1. Improving chaotic ant colony algorithm from following points: return optimization strategy, elite strategy and intersection removal strategy. And an Orthogonal-cluster Chaos Ant Colony Algorithm(OCACA) is presented for multi-robot mission planning. The idea of the algorithm is that first use orthogonal method to cluster the target points, then adopt chaos technology to optimize initial solution of the ant colony to improve individual quality and chaos perturbation is utilized to avoid the search being trapped in local optimum. It's the first time to apply OCACA in multi-robot mission planning, and the algorithm solve large-scale mission planning problem successfully. Simulation result show that the algorithm works well in improving the efficiency of executing tasks of multi-robots. Additionally, it's a novel ideal to solve multiple traveling salesmen problem.
2. For the number of task could be added or reduced in a dynamic environment in robot tour construction problem. we used dynamic adaptability of the elastic net, proposed Elastic adapting Chaotic Ant Colony Algorithm(ECACA). Then, based on the characteristics of Multi-Robot System, we proposed Elastic adapting Chaotic Ant Colony Algorithm(MECACA) on the basis of OCACA. The simulation results demostrate the feasibility and efficiency of our algorithm.
蚁群算法是依据蚂蚁群在搜索食物源的过程中所体现出来的寻优能力而提出来的一种新型的启发式、分布式协作寻优仿生算法。其特点是算法结构简单,具有较强的鲁棒性和发现较好解的能力。蚁群算法已广泛应用于旅行商问题等领域。
本论文针对多机器人任务规划问题,研究了同时防止早熟和提高算法收敛速度的若干算法,从理论或应用的角度分析了所述方法的有效性,主要创新包括：
1.在混沌蚁群算法的基础上进行了改进,采用的改进策略有：回程优化策略、精英策略和去交叉策略。在改进的混沌蚁群算法的基础上,针对多机器人任务规划问题提出了正交混沌蚁群算法。该算法是基于传统的解决多旅行商问题的思路,采用集中分配,分布式规划方法,该算法分为两个步骤：正交聚类和混沌蚁群求解单机器人路径规划。该算法的特点在于正交聚类法的低时间复杂度。正交聚类法是解决任务分配问题的一种有效方法,它利用正交表进行分配,对任务分配方案作最优设计。正交聚类法和混沌技术的引入,经过较少的迭代次数就可以找到较优解,对于求解中大规模任务规划问题是十分有利的。
2.针对动态环境中单机器人路径规划目标任务点可能动态添加或删除的特点,利用弹性网络的动态适应性提出了弹性适应混沌蚁群算法。针对动态多机器人任务规划问题的目标点的动态性和部分机器人也可能会出现故障的特点,结合正交混沌蚁群算法,提出了基于多机器人任务规划的弹性适应混沌蚁群算法,在一定的假设下,通过实验证明了该算法的有效性。
Mission Planning is essential for Multi-Robot Systems to improve the navigation performance of the robots and reduce their uncertainty when they are moving. In the areas of planet detection, intelligent transportation, and electronic assembly applications, Robot is a highly limited resource, and its navigation performance affects the efficiency of various application systems directly.
Ant Colony Algorithm is a newly heuristic and cooperative simulation algorithm for evolution, inspired by the evolutionary ability of the real ants when they are searching food. It owns simple structure, strong robustness and better solution. Therefore, the Ant Colony Algorithm has been widely used in the field of traveller and so on.
In the light of multi-robot task planning, this paper studies some algorithms to prevent prematurity and increase convergence speed of the algorithms. Analyzing all the methods by theory and application, we find they are effective. The main innovations include:
1. Improving chaotic ant colony algorithm from following points: return optimization strategy, elite strategy and intersection removal strategy. And an Orthogonal-cluster Chaos Ant Colony Algorithm(OCACA) is presented for multi-robot mission planning. The idea of the algorithm is that first use orthogonal method to cluster the target points, then adopt chaos technology to optimize initial solution of the ant colony to improve individual quality and chaos perturbation is utilized to avoid the search being trapped in local optimum. It's the first time to apply OCACA in multi-robot mission planning, and the algorithm solve large-scale mission planning problem successfully. Simulation result show that the algorithm works well in improving the efficiency of executing tasks of multi-robots. Additionally, it's a novel ideal to solve multiple traveling salesmen problem.
2. For the number of task could be added or reduced in a dynamic environment in robot tour construction problem. we used dynamic adaptability of the elastic net, proposed Elastic adapting Chaotic Ant Colony Algorithm(ECACA). Then, based on the characteristics of Multi-Robot System, we proposed Elastic adapting Chaotic Ant Colony Algorithm(MECACA) on the basis of OCACA. The simulation results demostrate the feasibility and efficiency of our algorithm.
引文
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[4]张伟军,王琴,顿向明等.一种基于时间的机器人装配任务规划方法[J].上海交通大学学报,2001,35(12)：1760-1766
[5]李可佳,丁希仑.用于星球探测的多机器人任务规划技术[J].机器人技术与应用,2008,2008(03)：37-41
[6]谭民,王硕,曹志强.多机器人系统[M].北京：清华大学出版社,2005.292-293
[7]DIAS M.B., ZLOT R, KALRA N, etal. Market-Based Multi-robot Coordination:A Survey and Analysis[C]. Proceedings of the IEEE,94(7),2006,1257-1270
[8]Lagoudakis M, Berhault M, Koenig S, et al. Simple auction with performance guarantees for multi-robot task allocation[C]. Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Sendai, Piscataway, NJ:IEEE Service Center,2004.698-705
[9]钱颂迪.运筹学[M].北京：清华大学出版社,2002.128-134
[10]闵帆,石兵,杨国纬,等.分布式系统中任务分配的一种结点自适应算法[J].计算机学报,2003,26(3)：47-54
[11]Bastos R M, Oliveira F M, Oliveira J P M. Decentralised resource allocation planning through negotiation[C]. In:Luis M. Camarinha-Matos, Hamideh Afarmanesh, Vladimir Marik eds. Intelligent systems for manufacturing: multi-agent systems and virtual organizations. US:KLUWER ACADEMIC PUBLISHERS,1998.67-76
[12]马巧云.基于多Agent系统的动态任务分配研究[D]：[博士学位论文].武汉：华中科技大学,2006
[13]于红斌,李孝安.基于栅格法的机器人快速路径规划[J].微电子学与计算机,2005,22(6)：98-100
[14]Hartmut Surmann, Jorg Huser, Jens Wehking. Path planning for a fuzzy controlled Autonomous Mobile Robot [A]. In:Fifth IEEE International Conference on Fuzzy Systems-IEEE'96[C]. UAS:New Orleans,1996.1660-1665
[15]谢宏斌,刘国栋.动态环境中基于模糊神经网络的机器人路径规划的一种新方法[J].江南大学学报(自然科学版),2003,2(1)：20-27
[16]厉虹,胡兵.轮式移动机器人非完整运动规划的遗传算法[J].控制理论与应用,2005,24(2)：13-21
[17]樊晓平,罗熊,易晟等.复杂环境下基于蚁群优化算法的机器人路径规划[J]控制与决策,2004,19(2)：166-170
[18]朱庆保.全局未知环境下多机器人运动蚂蚁导航算法[J].软件学报,2006,17(9)：1890-1898
[19]苏连成.基于多移动机器人协作的任务规划及定位方法研究[D]：[硕士学位论文].山东：山东科技大学,2003：40-41
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[21]Luca M. Gambardella, Marco Dorigo. Ant-Q:A Reinforcement Learning approach to the traveling salesman problem[C]. In:International Conference on Machine Learning, Tachoe City, CA,1995.252-260
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