基于时间的多机器人协调避碰算法研究
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摘要
针对现有多机器人协调避碰问题,提出了一种基于时间的多机器人协调避碰规划算法。首先,对机器人系统使用改进的栅格法进行环境建模,将其转化为一个关于机器人动作序列的问题。然后运用解耦法,第1阶段在构建好的模型中利用改进的A*算法规划单个机器人的静态无碰路径;第2阶段通过错开时间修改运动序列的方式实现多机器人之间无冲突的运动,即可达到多机器人协调避碰规划的目的。仿真实验结果表明:所设计的基于时间的多机器人协调避碰规划算法能够有效地实现多机器人的避碰路径规划。
A time-based multi-robot coordinated collision avoidance planning algorithm is proposed for the existing multi-robot coordinated collision avoidance problem. First,the improved grid method was used to model the environment of the robot system,which is transformed into a problem about the sequence of robot actions. Then the decoupling method was applied. In the first stage,the improved A* algorithm was used to plan the static collision-free path of a single robot in the constructed model.In the second stage,the collision-free motion between multiple robots was realized by staggering the time to modify the motion sequence,which can achieve the goal of multi-robot coordinated collision avoidance planning. Finally, the simulation experiments show that the time-based multi-robot coordinated collision avoidance algorithm can effectively realize multi-robot collision avoidance path planning.
A time-based multi-robot coordinated collision avoidance planning algorithm is proposed for the existing multi-robot coordinated collision avoidance problem. First,the improved grid method was used to model the environment of the robot system,which is transformed into a problem about the sequence of robot actions. Then the decoupling method was applied. In the first stage,the improved A* algorithm was used to plan the static collision-free path of a single robot in the constructed model.In the second stage,the collision-free motion between multiple robots was realized by staggering the time to modify the motion sequence,which can achieve the goal of multi-robot coordinated collision avoidance planning. Finally, the simulation experiments show that the time-based multi-robot coordinated collision avoidance algorithm can effectively realize multi-robot collision avoidance path planning.
引文
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[3] ZHANG Y,FU M Y,WANG M L. A method about statespace representation and location estimation by computational geometry[C]//Control&Decision Conference.Shanghai,IEEE,2014:4579-4583.
[4] HART P E,NILSSON N J,RAPHAEL B. A formal basis for the heuristic determination of minimum cost paths[J]. IEEE transactions on Systems Science and Cybernetics,1968,4(2):100-107.
[5]徐飞.基于改进人工势场法的机器人避障及路径规划研究[J].计算机科学,2016,43(12):293-296.
[6]刘国栋,谢宏斌,李春光.动态环境中基于遗传算法的移动机器人路径规划的方法[J].机器人,2003,25(4):327-330.
[7] YU Z Z,YAN J H,ZHAO J,et al. Mobile Robot Path Planning Based On Improved Artificial Potential Field Method[J]. Journal of Harbin Institute of Technology,2011,43(1):50-55.
[8]徐昕,沈栋,高岩青,等.基于马氏决策过程模型的动态系统学习控制:研究前沿与展望[J].自动化学报,2012,38(5):673-687.
[9]陈学松,刘富春.一类非线性动态系统基于强化学习的最优控制[J].控制与决策,2013,28(12):1889-1893.
[10]夏梁盛,严卫生.基于栅格法的移动机器人运动规划研究[J].计算机仿真,2012,29(12):229-233.
[11]曲道奎,杜振军,徐殿国,等.移动机器人路径规划方法研究[J].机器人,2008,30(2):97-101.
[2] WANG W H,CHEN W D,XI Y G. Uncertain In Information Based Map-Building Of Mobile Robots In Absolutely Unknown New Environment[J]. ROBOT,2001,23(6):563-568.
[3] ZHANG Y,FU M Y,WANG M L. A method about statespace representation and location estimation by computational geometry[C]//Control&Decision Conference.Shanghai,IEEE,2014:4579-4583.
[4] HART P E,NILSSON N J,RAPHAEL B. A formal basis for the heuristic determination of minimum cost paths[J]. IEEE transactions on Systems Science and Cybernetics,1968,4(2):100-107.
[5]徐飞.基于改进人工势场法的机器人避障及路径规划研究[J].计算机科学,2016,43(12):293-296.
[6]刘国栋,谢宏斌,李春光.动态环境中基于遗传算法的移动机器人路径规划的方法[J].机器人,2003,25(4):327-330.
[7] YU Z Z,YAN J H,ZHAO J,et al. Mobile Robot Path Planning Based On Improved Artificial Potential Field Method[J]. Journal of Harbin Institute of Technology,2011,43(1):50-55.
[8]徐昕,沈栋,高岩青,等.基于马氏决策过程模型的动态系统学习控制:研究前沿与展望[J].自动化学报,2012,38(5):673-687.
[9]陈学松,刘富春.一类非线性动态系统基于强化学习的最优控制[J].控制与决策,2013,28(12):1889-1893.
[10]夏梁盛,严卫生.基于栅格法的移动机器人运动规划研究[J].计算机仿真,2012,29(12):229-233.
[11]曲道奎,杜振军,徐殿国,等.移动机器人路径规划方法研究[J].机器人,2008,30(2):97-101.