DocumentCode :
2740892
Title :
An Efficient Global Optimization Approach to Multi Robot Path Exploration Problem Using Hybrid Genetic Algorithm
Author :
Senthilkumar, K.S. ; Bharadwaj, K.K.
Author_Institution :
Sch. of Comput. & Syst. Sci., Jawaharlal Nehru Univ., New Delhi
fYear :
2008
fDate :
12-14 Dec. 2008
Firstpage :
7
Lastpage :
12
Abstract :
This paper presents a novel scheme for global path exploration to multi robots environment using hybrid implementation of evolutionary heuristic. This scheme is used to find an optimal path for each mobile robot to move in a static environment expressed by a weighted graph with nodes and links. The interesting part of this scheme is that the chromosome structure is designed to cluster the landmarks (nodes) in the environment. The rendezvous point for robots to meet at last is selected by using making centroid technique. We used a fixed length chromosome. Each robot has a starting point and a rendezvous point under the assumption that the robot passes each point in the cluster only once. Experimental results are presented to illustrate the performance of the proposed scheme. The scheme was tested on a set of different problems with encouraging results.
Keywords :
genetic algorithms; mobile robots; multi-robot systems; path planning; chromosome structure; evolutionary heuristic; global optimization; global path exploration; hybrid genetic algorithm; making centroid technique; mobile robot; multirobot path exploration; weighted graph; Artificial neural networks; Biological cells; Computational geometry; Computer networks; Fuzzy logic; Genetic algorithms; Mobile robots; Robot kinematics; Robustness; Testing; Genetic algorithm; Multi robot; Rendezvous point; path exploration;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information and Automation for Sustainability, 2008. ICIAFS 2008. 4th International Conference on
Conference_Location :
Colombo
Print_ISBN :
978-1-4244-2899-1
Electronic_ISBN :
978-1-4244-2900-4
Type :
conf
DOI :
10.1109/ICIAFS.2008.4783919
Filename :
4783919
Link To Document :
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