Multi-robot multi-target dynamic path planning using artificial bee colony and evolutionary programming in unknown environment

被引:47
作者
Faridi, Abdul Qadir [1 ]
Sharma, Sanjeev [1 ]
Shukla, Anupam [1 ]
Tiwari, Ritu [1 ]
Dhar, Joydip [1 ]
机构
[1] Atal Bihari Vajpayee Indian Inst Informat Technol, Gwalior, Madhya Pradesh, India
关键词
Artificial bee colony (ABC); Evolutionary programming ( EP); Multi-robot path planner system; Dynamic obstacle collision avoidance; Centralized coordination; Dynamic unknown environment; GENETIC ALGORITHM; MOBILE ROBOTS;
D O I
10.1007/s11370-017-0244-7
中图分类号
TP24 [机器人技术];
学科分类号
080202 ; 1405 ;
摘要
Navigation or path planning is the basic need for movement of robots. Navigation consists of two foremost concerns, target tracking and hindrance avoidance. Hindrance avoidance is the way to accomplish the task without clashing with intermediate hindrances. In this paper, an evolutionary scheme to solve the multi-agent, multi-target navigation problem in an unknown dynamic environment is proposed. The strategy is a combination of modified artificial bee colony for neighborhood search planner and evolutionary programming to smoothen the resulting intermediate feasible path. The proposed strategy has been tested against navigation performances on a collection of benchmark maps for A* algorithm, particle swarm optimization with clustering-based distribution factor, genetic algorithm and rapidly-exploring random trees for path planning. Navigation effectiveness has been measured by smoothness of feasible paths, path length, number of nodes traversed and algorithm execution time. Results show that the proposed method gives good results in comparison to others.
引用
收藏
页码:171 / 186
页数:16
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