Journal of Mathematical Sciences and Applications
ISSN (Print): 2333-8784 ISSN (Online): 2333-8792 Website: http://www.sciepub.com/journal/jmsa Editor-in-chief: Prof. (Dr.) Vishwa Nath Maurya, Cenap ozel
Open Access
Journal Browser
Go
Journal of Mathematical Sciences and Applications. 2016, 4(1), 34-38
DOI: 10.12691/jmsa-4-1-6
Open AccessArticle

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

Zhenping Li1, and Xueting Li1

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

Pub. Date: November 02, 2016

Cite this paper:
Zhenping Li and 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

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.

Keywords:
multi-robot system collision detection integer programming model genetic algorithm

Creative CommonsThis work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

References:

[1]  Li, Z. and Li, W. mathematical model and algorithm for the task allocation problem of robots in the smart warehouse [J]. Logistics Technology, 2015(5): 493-502. (In Chinese).
 
[2]  Shuang Liu, Dong Sun, Chang Zhu, A dynamic priority based path planning for cooperation of multiple mobile robots in formation forming [J]. ELSEVIER, 2014(30): 589-596.
 
[3]  Suzuki T, Sekine T, Fujii T, Asama H and Endo I. Cooperative formation among multiple mobile robot teleportation in inspection task[C]. In: Proceedings of the IEEE conference on decision and control, Sydney, NSW; 2000: 358-63.
 
[4]  A. Yamashita, T. Arai, J. Ota, H. Asama, Motion planning of multiple mobile robots for Cooperative manipulation and transportation [J]. IEEE Trans. on Robotics and Automation, 2003(19): 223-237.
 
[5]  Y. Wang, C.W. de Silva, A machine-learning approach to multi-robot coordination [J]. Engineering Applications of Artificial Intelligence, 2008(21): 470-484.
 
[6]  Kelin Jose, Dilip Kumar Pratihar, Task allocation and collision-free path planning of centralized multi-robot system for industrial plant inspection using heuristic methods [J]. Robotics and Autonomous Systems,2016.
 
[7]  Wu,J. Applied analysis and prospect of robots Kiva in the Amazon warehouse [J]. Logistics technology and Application, 2015(10): 159-162.