基于分组蚁群算法的机器人路径规划研究
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摘要
机器人路径规划问题是机器人研究领域的重要内容,是机器人完成任务的安全保障及智能化程度的重要标志之一。蚁群优化算法(Ant Colony Optimization algorithm, ACO)是由意大利学者Dorigo等人通过研究自然界蚂蚁觅食的行为,提出来的新型启发式优化算法。在复杂的优化问题方面,蚁群算法已经成为一种新的具有竞争力,并且很有发展前景的组合优化算法。
本文主要研究的是全局静态环境下基于蚁群算法的机器人路径规划问题。
首先,文中深入研究了蚁群算法的基本原理及数学模型。包括信息素浓度,启发因子,状态转移概率等相关知识以及应用到经典TSP问题中进行求解。
其次,对蚁群算法优化机器人路径规划的问题进行了深入研究,包括对栅格法建模的研究。通过分析得出蚁群算法存在搜索速度慢,易陷入局部最优及收敛速度慢等缺点。
为了克服上述的缺点,本文设计出性能更优的分组蚁群算法并应用到机器人路径规划问题中。分组蚁群算法将蚂蚁分为两组,分别放在起点和目标点上相向而行搜索路径,在一定程度上缓解了信息素叠加现象,有助于增加解的多样性。为了进一步提高分组蚁群算法的性能,针对具体的路径规划问题提出了基于多种策略的分组蚁群算法。多种策略包括复位策略、奖惩策略、最大最小策略、目标领域策略、路径交叉策略等五种策略。通过仿真实验表明,该算法性能较优,在任意复杂的工作环境中能规划出最优路径。
最后,对论文的研究工作进行了总结,并提出了下一步的工作展望。
Robot path planning has become an important part of the robot research field. It can guarantee that the robot completes the task safely and it is one of symbols of robot intellectualized degree. Ant Colony Optimization(ACO) algorithm which is proposed by Italian scholar Dorigo is a new heuristic optimization algorithm and it is inspired by analogy of behavior of real ants, when looking for foods. In the aspect of the complex optimized problems, the Ant Colony Optimization algorithm has become a newly competitive power solution algorithm which has a good development prospects.
This paper mainly studies the robot path planning problem based on Ant Colony Optimization algorithm in the global static environment.
Firstly, in-depth research on the basic principle and mathematical model of Ant Colony Optimization algorithm, including pheromone trail, heuristic, transition probability related knowledge and the solving problem of application to classical problem of TSP.
Secondly, this paper studied on using Ant Colony algorithm to optimize the robot path planning, including the grids method. Through the analysis we have the conclusions:Ant Colony Optimization algorithm has many shortcomings such as: search speed is slow, easily to fall into local optimal and slow convergence speed.
In order to overcome the disadvantages, this paper proposed the Grouped Ant Colony algorithm which has a better performance and applied it to solve the robot path planning problem. The Grouped Ant colony algorithm divided ants into two groups, put them at the starting point and goal point, respectively. Search path in opposite direction, it relieves pheromone superposition phenomenon, which contribute to the increased diversity of solution effectively. To further improve the performance of the Grouped Ant Colony Optimization algorithm, we propose Grouped Ant Colony Optimization algorithm based on several strategies, including the reset strategy, award-penalty strategy, the maximum-minimum strategy, target areas strategy, path crossover strategy. Simulation results show that the performance of the algorithm is better to optimize path in any complex environment
Finally, the paper summarizes the current research and points out the further future research as well.
本文主要研究的是全局静态环境下基于蚁群算法的机器人路径规划问题。
首先,文中深入研究了蚁群算法的基本原理及数学模型。包括信息素浓度,启发因子,状态转移概率等相关知识以及应用到经典TSP问题中进行求解。
其次,对蚁群算法优化机器人路径规划的问题进行了深入研究,包括对栅格法建模的研究。通过分析得出蚁群算法存在搜索速度慢,易陷入局部最优及收敛速度慢等缺点。
为了克服上述的缺点,本文设计出性能更优的分组蚁群算法并应用到机器人路径规划问题中。分组蚁群算法将蚂蚁分为两组,分别放在起点和目标点上相向而行搜索路径,在一定程度上缓解了信息素叠加现象,有助于增加解的多样性。为了进一步提高分组蚁群算法的性能,针对具体的路径规划问题提出了基于多种策略的分组蚁群算法。多种策略包括复位策略、奖惩策略、最大最小策略、目标领域策略、路径交叉策略等五种策略。通过仿真实验表明,该算法性能较优,在任意复杂的工作环境中能规划出最优路径。
最后,对论文的研究工作进行了总结,并提出了下一步的工作展望。
Robot path planning has become an important part of the robot research field. It can guarantee that the robot completes the task safely and it is one of symbols of robot intellectualized degree. Ant Colony Optimization(ACO) algorithm which is proposed by Italian scholar Dorigo is a new heuristic optimization algorithm and it is inspired by analogy of behavior of real ants, when looking for foods. In the aspect of the complex optimized problems, the Ant Colony Optimization algorithm has become a newly competitive power solution algorithm which has a good development prospects.
This paper mainly studies the robot path planning problem based on Ant Colony Optimization algorithm in the global static environment.
Firstly, in-depth research on the basic principle and mathematical model of Ant Colony Optimization algorithm, including pheromone trail, heuristic, transition probability related knowledge and the solving problem of application to classical problem of TSP.
Secondly, this paper studied on using Ant Colony algorithm to optimize the robot path planning, including the grids method. Through the analysis we have the conclusions:Ant Colony Optimization algorithm has many shortcomings such as: search speed is slow, easily to fall into local optimal and slow convergence speed.
In order to overcome the disadvantages, this paper proposed the Grouped Ant Colony algorithm which has a better performance and applied it to solve the robot path planning problem. The Grouped Ant colony algorithm divided ants into two groups, put them at the starting point and goal point, respectively. Search path in opposite direction, it relieves pheromone superposition phenomenon, which contribute to the increased diversity of solution effectively. To further improve the performance of the Grouped Ant Colony Optimization algorithm, we propose Grouped Ant Colony Optimization algorithm based on several strategies, including the reset strategy, award-penalty strategy, the maximum-minimum strategy, target areas strategy, path crossover strategy. Simulation results show that the performance of the algorithm is better to optimize path in any complex environment
Finally, the paper summarizes the current research and points out the further future research as well.
引文
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[3]段海滨.蚁群算法原理及其应用[J].北京：科学出版社,2005.
[4]马良,项培军.蚂蚁算法在组合优化中的应用.管理科学学报[J],2001；4(2)：32-37.
[5]Marco Dorigo, Thomas Stutzle. Ant Colony Optimization[M].张军,胡晓敏,罗旭耀,等译.北京：清华大学出版社.2007,1第一版.
[6]M. Dorigo, V. Maniezzo, Ant Colonies Positive Feedback as a Search Strategy[J]. Technical Report No.91-016, Politecnico di Milano,Italy.1991.
[7]A. Colorni, M. Dorigo, V. Maniezzo. Distributed Optimization by Ant Colonies[C]. Proceedings of the 1st European Conference on Artificial Life.1991:134-142.
[8]M. Dorigo, V. Maniezzo, A Colony Ant System Optimization by a Colony of Cooperating Agents[J]. IEEE Transaction on Systems Man and Cybernetics-Part B.1996,26(1):29-41.
[9]Thomas Sttitzle, Holger H. Hoos. MAX-MIN ant system and Local Search for Combinatorial Optimization Problem. Future Generation Computer Systems.1999:313-329.
[10]Thomas Sttitzle, H. H. Hoos. MAX-MIN Ant System[J]. Future Generation Computer Systems.2000,16(8):889-914
[11]Gutjahr W J.A Generalized convergence result for the graph based ant system[J]. Technical Report 99-109, Dept of Statistics and Decision Support Systems, University of Vienna, Austria,1999.
[12]吴斌,史忠植.一种基于蚁群算法的TSP问题分段求解算法[J].计算机学报.2001,24(12)：1328-1333.
[13]丁建立,陈增强,袁著社.遗传算法与蚂蚁算法的融合[J].计算机研究与发展,2003：40(9)：1351-1356.
[14]修春波,张雨虹.基于蚁群与鱼群的混合优化算法.计算机工程[J],2008,34(14)：206-207.
[15]李晓磊,路飞,田国会,钱积新.组合优化问题的人工鱼群算法应用[J].山东大学学报(工学版),2004,34(5)：64-67.
[16]王晶.蚁群算法优化前向神经网络的一种方法计算机工程与应用[J],2006,25：53-55.
[17]柴宝杰,刘大为.基于粒子群优化的蚁群算法在TSP中的应用[J]。计算机仿真26(8)：89-91.
[18]Guldner J, Utkin V I, Bauer R.A three-layered hierarchical path control system for mobile robots:algorithm and experiments. [J].Robotics Autonomous System,1995,14(223): 133-147.
[19]Khatib O. Real-Time Obstacle Avoidance For Manipulators And Mobile Robots[J]. The International Journal of Robotics Research,1985,5(2):500-505.
[20]韩永,刘国栋.动态环境下基于人工势场的移动机器人运动规划[J].机器人,2006,28(1)：45-49.
[21]王肖青,王奇志.传统人工势场的改进[J].计算机技术与发展,2006,16(4)：96-98.
[22]P. Zhang, T. S. Lu. Soccer Robot Path Planning Based On Artificial Potential Field Approach With Simulated Annealing[J].Mechanical Science and Technology,2003, 22(4):547-548.
[23]M.G. Park, J. H. Jeon, M. C. Lee. Obstacle Avoidance For Mobile Robots Using Artificial Potential Field Approach With Simulated Annealing[C]. IEEE International Symposium on Industrial Electronics,2001,3:1530-1535.
[24]Gao Yunfeng, Huang Hai. A Path Planning Algorithm Based On Potential Field For Complex Environment[J].ROBOT,2004,26(2):114-118.
[25]艾海舟,张钱.基于拓扑的路径规划问题的图形解法[J].机器人,1990,12(5)：20-24.
[26]李永成,张钱.基于拓扑法的多关节机械手无碰路径规划[J].软件学报,1993,4(5)：11-16.
[27]AI-Taharwa I, Sheta, AI-Weshan Meshan M. A Mobile Robot Path Planning Using Genetic Algorithm In Static Environment. Journal of Computer Sciences,2008,4(4):341-344.
[28]毕慧敏,董海鹰.改进遗传算法在机器人路径规划中的应用[J].测控技术,2006,25(4)：53-55.
[29]杨敬辉,洪炳镕,朴松昊.基于遗传模糊算法的机器人局部避障规划[J].哈尔滨工业大学学报,2004,36(7)：946-945.
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