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Y. Wang, C.W. de Silva, A machine-learning approach to multi-robot coordination [J]. Engineering Applications of Artificial Intelligence, 2008(21): 470-484.

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Article

Genetic Algorithm for Task Allocation and Path Planning of Multi-robot System

1School of Information, Beijing Wuzi University, Beijing, China


Journal of Mathematical Sciences and Applications. 2016, Vol. 4 No. 1, 34-38
DOI: 10.12691/jmsa-4-1-6
Copyright © 2016 Science and Education Publishing

Cite this paper:
Zhenping Li, Xueting Li. Genetic Algorithm for Task Allocation and Path Planning of Multi-robot System. Journal of Mathematical Sciences and Applications. 2016; 4(1):34-38. doi: 10.12691/jmsa-4-1-6.

Correspondence to: Zhenping  Li, School of Information, Beijing Wuzi University, Beijing, China. Email: lizhenping66@163.com

Abstract

Based on the consideration of the collision, the task allocation and path planning of multi-robot system are studied. With robots’ the longest travel time as a restrictive condition and the total cost minimum as the objective function, the integer programming model is established; In order to avoid robots colliding in the process of walking, a collision penalty term is introduced and a genetic algorithm with collision detection is designed. Using the genetic algorithm, the optimal solution of task allocation and path planning can be found effectively, the paths can avoid the robots collision. Finally, the model and algorithm are verified by a specific example.

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