多仓储机器人协同路径规划与作业避碰
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摘要
针对多仓储移动机器人协同作业问题,提出了一种基于全局规划和局部调整的路径规划方法,以获得较短、无碰、避障的可行路径.在路径规划时根据当前节点到终点的距离和局部路径与起点至终点的欧氏路径的夹角设计新启发式函数,驱使机器人沿最短路行进;根据可选节点的数量提出避障规则,提高避障能力;依据路径长度对信息素进行比较更新,以精炼搜索空间、提高收敛性能,对蚁群算法加以改进寻找各自最优路径.在作业避碰时设计避碰规则有效解决仓储机器人间作业碰撞,找到最优或近优路径组合.实验结果表明了本方法的可行性、有效性.
For the collaborative operation of multi-storage robots,we propose a path planning method based on global planning and local adjustment to obtain a short feasible path free of collisions and obstacles. In the path planning,according to the distance from the current node to the end point and the angle between the local path and the Euclidean path from the start point to the end point,we design a new heuristic function which drives the robot to travel along the shortest path. According to the number of optional nodes,we propose the obstacle avoidance rules and improve obstacle avoidance ability. Based on the path length,we compare the pheromone and update it to refine the search space and improve the convergence performance. We also improve the ant colony algorithm to find the optimal path. We design the collision avoidance rules to avoid the collisions between warehouse robots and find the optimal or near-optimal path combination. The experimental results verify the feasibility and effectiveness of the method.
For the collaborative operation of multi-storage robots,we propose a path planning method based on global planning and local adjustment to obtain a short feasible path free of collisions and obstacles. In the path planning,according to the distance from the current node to the end point and the angle between the local path and the Euclidean path from the start point to the end point,we design a new heuristic function which drives the robot to travel along the shortest path. According to the number of optional nodes,we propose the obstacle avoidance rules and improve obstacle avoidance ability. Based on the path length,we compare the pheromone and update it to refine the search space and improve the convergence performance. We also improve the ant colony algorithm to find the optimal path. We design the collision avoidance rules to avoid the collisions between warehouse robots and find the optimal or near-optimal path combination. The experimental results verify the feasibility and effectiveness of the method.
引文
[1] Contreras-Cruz M A,Ayala-Ramirez V,Hernandez-Belmonte U H. Mobile robot path planning using artificial bee colony and evolutionary pro-gramming[J]. Applied Soft Computing,2015,30:319-328.
[2] Lee J,Kim D W. An effective initialization method for genetic algorithm based robot path planning using a directed acyclic graph[J]. Informa-tion Sciences,2016,332:1-18.
[3]朱庆保,张玉兰.基于栅格法的机器人路径规划蚁群算法[J].机器人,2005,27(2):132-136.Zhu Q B,Zhang Y L. An ant colony algorithm based on grid method for mobile robot path planning[J]. Robot,2005,27(2):132-136.
[4]黄宜庆,彭凯,袁梦茹.基于多策略混合人工鱼群算法的移动机器人路径规划[J].信息与控制,2017,46(3):283-288.Huang Y Q,Peng K,Yuan M R. Path planning for mobile robots based on multi-strategy hybrid artificial fish swarm algorithm[J]. Informationand Control,2017,46(3):283-288.
[5]尹国强,贾云伟,张慧.改进人工势场法的机器人路径规划[J].制造业自动化,2015,37(23):67-70.Yin G Q,Jia Y W,Zhang H. A modified artificial potential field method to mobile robot path planning[J]. Manufacturing Automation,2015,37(23):67-70.
[6] He T X,Li P,Han N Q. Path planning for mobile robot based on fuzzy control[C]//International Conference on Applied Informatics andCommunication. Berlin,Germany:Springer-Verlag,2011:522-531.
[7]邓志燕,陈炽坤.基于改进遗传算法的移动机器人路径规划研究[J].机械设计与制造,2010(7):147-149.Deng Z Y,Chen C K. Mobile robot path planning based on improved genetic algorithm[J]. Machinery Design&Manufacture,2010(7):147-149.
[8] Cao K E,Jiang B,Yue D. Cooperative path following control of multiple nonholonomic mobile robots[J]. ISA Transactions,2017,71:161-169.
[9]吴慧超,罗元,周前能.时效优先的轮式机器人编队避障策略[J].信息与控制,2017,46(2):211-217.Wu H C,Luo Y,Zhou Q N. Obstacle avoidance strategy of wheeled robot formations based on time efficiency[J]. Information and Control,2017,46(2):211-217.
[10]吴菁芃.亚马逊仓库Kiva机器人的应用分析与前景展望[J].物流技术与应用,2015,20(10):159-164.Wu J P. Application analysis and prospect of Kiva robot in Amazon warehouse[J]. Logistics&Material Handling,2015,20(10):159-164.
[11]Kala R. Multi-robot path planning using co-evolutionary genetic programming[J]. Expert System with Applications,2012,39(3):3817-3831.
[12]高翔,苏青.基于双层模糊逻辑的多机器人路径规划与避碰[J].计算机技术与发展,2014,24(11):79-82.GAO X,SU Q. Multi-robot path planning and collision avoidance based on double fuzzy logic[J]. Computer Technology and Development,2014,24(11):79-82.
[13]周旺平,巩力源,王雅.化工灾害搜救机器人路径规划研究[J].计算机仿真,2016,33(9):392-395.Zhou W P,Gong L Y,Wang Y. Chemical disaster rescue robot path planning[J]. Computer Simulation,2016,33(9):392-395.
[14]舒翔翔.多机器人最优路径规划研究[J].信息与电脑,2017(15):62-64.SHU X X. Research on optimal path planning of multi-robot[J]. China Computer&Communication,2017(15):62-64.
[15]张大伟,孟森森,邓计才.多移动微小型机人编队控制与协作避碰研究[J].仪器仪表学报,2017,38(3):578-585.Zhang D W,Meng S S,Deng J C. Formation control and cooperative collision avoidance for multiple mobile miniature robots[J]. ChineseJournal of Scientific Instrument,2017,38(3):578-585.
[16]顾君华,孟慧婕,夏红梅,等.基于改进蚁群算法的多机器人路径规划研究[J].河北工业大学学报(自然科学版),2016,45(5):28-34.Gu J H,Meng H J,Xia H M. Research on multi-robots path planning of robot based on improved ant colony algorithm[J]. Journal of HebeiUniversity of Technology(Science and Technology),2016,45(5):28-34.
[17]Wan Y C,Wang M W,Ye Z W. A feature selection method based on modified binary coded ant colony optimization algorithm[J]. AppliedSoft Computing,2016,49:248-258.
[18]Davoodi M,Abedin M,Banyassady B. An optimal algorithm for two robots path planning problem on the grid[J]. Robotics and AutonomousSystems,2013,61(12):1406-1414.
[19]柴寅,唐秋华,邓明星,等.机器人路径规划的栅格模型构建与蚁群算法求解[J].机械设计与制造,2016(4):178-181.Chai Y,Tang Q H,Deng M X. Grid model and ant colony algorithm for robot path planning[J]. Machinery Design&Manufacture,2016(4):178-181.
[20]Das P K,Behera H S,Iena P K. Multi-robot path planning in a dynamic environment using improved gravitational search algorithm[J]. Jour-nal of Electrical Systems and Information Technology,2016,3(2):1-13.
[21]徐旺宝,张进,陈雪波.复杂动态环境下基于改进的Cautious Bug算法的机器人路径规划[J].信息与控制,2014,43(4):398-404.Xu W B,Zhang J,Chen X B. Path planning of robots based on the improved Cautious Bug algorithm in complex and dynamic environment[J].Information and Control,2014,43(4):398-404.
[2] Lee J,Kim D W. An effective initialization method for genetic algorithm based robot path planning using a directed acyclic graph[J]. Informa-tion Sciences,2016,332:1-18.
[3]朱庆保,张玉兰.基于栅格法的机器人路径规划蚁群算法[J].机器人,2005,27(2):132-136.Zhu Q B,Zhang Y L. An ant colony algorithm based on grid method for mobile robot path planning[J]. Robot,2005,27(2):132-136.
[4]黄宜庆,彭凯,袁梦茹.基于多策略混合人工鱼群算法的移动机器人路径规划[J].信息与控制,2017,46(3):283-288.Huang Y Q,Peng K,Yuan M R. Path planning for mobile robots based on multi-strategy hybrid artificial fish swarm algorithm[J]. Informationand Control,2017,46(3):283-288.
[5]尹国强,贾云伟,张慧.改进人工势场法的机器人路径规划[J].制造业自动化,2015,37(23):67-70.Yin G Q,Jia Y W,Zhang H. A modified artificial potential field method to mobile robot path planning[J]. Manufacturing Automation,2015,37(23):67-70.
[6] He T X,Li P,Han N Q. Path planning for mobile robot based on fuzzy control[C]//International Conference on Applied Informatics andCommunication. Berlin,Germany:Springer-Verlag,2011:522-531.
[7]邓志燕,陈炽坤.基于改进遗传算法的移动机器人路径规划研究[J].机械设计与制造,2010(7):147-149.Deng Z Y,Chen C K. Mobile robot path planning based on improved genetic algorithm[J]. Machinery Design&Manufacture,2010(7):147-149.
[8] Cao K E,Jiang B,Yue D. Cooperative path following control of multiple nonholonomic mobile robots[J]. ISA Transactions,2017,71:161-169.
[9]吴慧超,罗元,周前能.时效优先的轮式机器人编队避障策略[J].信息与控制,2017,46(2):211-217.Wu H C,Luo Y,Zhou Q N. Obstacle avoidance strategy of wheeled robot formations based on time efficiency[J]. Information and Control,2017,46(2):211-217.
[10]吴菁芃.亚马逊仓库Kiva机器人的应用分析与前景展望[J].物流技术与应用,2015,20(10):159-164.Wu J P. Application analysis and prospect of Kiva robot in Amazon warehouse[J]. Logistics&Material Handling,2015,20(10):159-164.
[11]Kala R. Multi-robot path planning using co-evolutionary genetic programming[J]. Expert System with Applications,2012,39(3):3817-3831.
[12]高翔,苏青.基于双层模糊逻辑的多机器人路径规划与避碰[J].计算机技术与发展,2014,24(11):79-82.GAO X,SU Q. Multi-robot path planning and collision avoidance based on double fuzzy logic[J]. Computer Technology and Development,2014,24(11):79-82.
[13]周旺平,巩力源,王雅.化工灾害搜救机器人路径规划研究[J].计算机仿真,2016,33(9):392-395.Zhou W P,Gong L Y,Wang Y. Chemical disaster rescue robot path planning[J]. Computer Simulation,2016,33(9):392-395.
[14]舒翔翔.多机器人最优路径规划研究[J].信息与电脑,2017(15):62-64.SHU X X. Research on optimal path planning of multi-robot[J]. China Computer&Communication,2017(15):62-64.
[15]张大伟,孟森森,邓计才.多移动微小型机人编队控制与协作避碰研究[J].仪器仪表学报,2017,38(3):578-585.Zhang D W,Meng S S,Deng J C. Formation control and cooperative collision avoidance for multiple mobile miniature robots[J]. ChineseJournal of Scientific Instrument,2017,38(3):578-585.
[16]顾君华,孟慧婕,夏红梅,等.基于改进蚁群算法的多机器人路径规划研究[J].河北工业大学学报(自然科学版),2016,45(5):28-34.Gu J H,Meng H J,Xia H M. Research on multi-robots path planning of robot based on improved ant colony algorithm[J]. Journal of HebeiUniversity of Technology(Science and Technology),2016,45(5):28-34.
[17]Wan Y C,Wang M W,Ye Z W. A feature selection method based on modified binary coded ant colony optimization algorithm[J]. AppliedSoft Computing,2016,49:248-258.
[18]Davoodi M,Abedin M,Banyassady B. An optimal algorithm for two robots path planning problem on the grid[J]. Robotics and AutonomousSystems,2013,61(12):1406-1414.
[19]柴寅,唐秋华,邓明星,等.机器人路径规划的栅格模型构建与蚁群算法求解[J].机械设计与制造,2016(4):178-181.Chai Y,Tang Q H,Deng M X. Grid model and ant colony algorithm for robot path planning[J]. Machinery Design&Manufacture,2016(4):178-181.
[20]Das P K,Behera H S,Iena P K. Multi-robot path planning in a dynamic environment using improved gravitational search algorithm[J]. Jour-nal of Electrical Systems and Information Technology,2016,3(2):1-13.
[21]徐旺宝,张进,陈雪波.复杂动态环境下基于改进的Cautious Bug算法的机器人路径规划[J].信息与控制,2014,43(4):398-404.Xu W B,Zhang J,Chen X B. Path planning of robots based on the improved Cautious Bug algorithm in complex and dynamic environment[J].Information and Control,2014,43(4):398-404.