• 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