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http://dx.doi.org/10.5302/J.ICROS.2010.16.3.227

Environment Adaptation using Evolutional Interactivity in a Swarm of Robots  

Moon, Woo-Sung (부산대학교 전자전기공학과)
Jang, Jin-Won (부산대학교 전자전기공학과)
Baek, Kwang-Ryul (부산대학교 전자전기공학과)
Publication Information
Journal of Institute of Control, Robotics and Systems / v.16, no.3, 2010 , pp. 227-232 More about this Journal
Abstract
In this paper we consider the multi-robot system that collects target objects spread in an unexplored environment. The robots cooperate each other to improve the capability and the efficiency. The robots attract or intimidate each other as behaviors of bacterial swarms or particles with electrical moments. The interactions would increase the working efficiency in some environments but it would decrease the efficiency in some other environments. Therefore, the system needs to adapt to the working environment by adjusting the strengths of the interactions. The strengths of the interactions are expressed as sets of gene codes that mean the weights of each kind of attracting or intimidating vectors. The proposed system adjusts the gene codes using evolutional strategy. The proposed approach has been validated by computer simulation. The results of this paper show that our inter-swarm interacting strategy and optimizing algorithm improves the working efficiency, adaptively to the characteristics of environments.
Keywords
multi-robot; swarm robot; genetic algorithm; environment adaptation;
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1 D. Payton, M. Daily, R Estowski, M. Howard, and C. Lee, Pheromone Robotics, Autonomous Robots, Springer, 2001.
2 M. J. Mataric, "Designing Emergent Behaviors: From Local Interactions to Collective Intelligence," Proc. of 2nd International Conference on Simulation of Adaptive Behavior, pp. 432-441, 1993.
3 C. R. Kube and H. Zhang, "Collective robotic intelligence," Proc. of 2nd International Conference on Simulation of Adaptive Behavior, pp. 460-468, 1993.
4 H. Bloom, Global Brain, Baror International Inc, New York, 2000.
5 S. Johnson, Emergence, Simon& Schuster Inc, 2001.
6 E. P. Greenberg, "Bacterial communication and group behavior," The Journal of Clinical Investigation, vol. 112, pp.1288-1290, 2003.   DOI
7 R. A. Watson, S. G. Ficici, and J. B. Pollack, "Embodied Evolution: Embodying an Evolutionary Algorithm in a Population of Robots," Proc. of Conference on Evolutionary Computation, vol. 1, pp. 342-353, 1999.
8 D. W. Lee and K. B. Sim, "Behavior learning and evolution of collective autonomous mobile robots using distributed genetic algorithms," Proc. of the 2nd Asian Control Conference, vol. 2, pp.675-678, 1997.
9 Z., Michalewicz, Genetic Algorithms + Data Structures = Evolution Programs, 2nd extended edition, Springer-Verlag, 1994.
10 S. Levy, Artificial Life, Sterling Loard Literistic Inc, New York, 1992.
11 Y. Rekleitis and G. Dudek, "Multi-robot collaboration for robust exploration," Proc. of IEEE Internaional Conference in Robotics and Automation, CA, 2000.
12 J. McLurkin and J.Smith, "Distributed algorithms for dispersion in indoor environments using a swarm of autonomous mobile robots," Proc. of the 7th International Symposium on Distributed Autonomous Robotic Systems, 2004.