Abstract A complete exploration algorithm using multiple robots in an unknown environment is proposed. Based on the principle of the immune system, the system is fully decentralized. It has no requirement for the starting points and the formation of the robots. Each local environment condition sensed by the robots is considered as antigen while robot is regarded as Bcell and the next exploration target as antibody respectively. Each robot works independently according to the antigen information. The coordination of the robots can be realized by utilizing the interactions among antibodies. The system carries out the exploration of unknown environment with the combination of robots’ independence and coordination. Instead of recording information about environment map, the complete exploration is realized using the frontier in the memory library. Simulation results validate the algorithm is efficient, complete and robust to the fail of robot and the lost of the communication.
[1] Thrun S. Learning MetricTopological Maps for Indoor Mobile Robot Navigation. Artificial Intelligence, 1998, 99(1): 2171 [2] Spero D J, Jarvis R A. Path Planning for a Mobile Robot in a Rough Terrain Environment // Proc of the 3rd International Workshop on Robot Motion and Control. Bukowy Dworek, Poland, 2002: 417422 [3] Pack D J, Mullins B E. Toward Finding an Universal Search Algorithm for Swarm Robots // Proc of the IEEE/RSJ International Conference on Intelligent Robots and Systems. Las Vegas, USA, 2003, Ⅱ: 19451950 [4] Simmon R, Apfelbaum D, Burgard W, et al. Coordination for MultiRobot Exploration and Mapping // Proc of the AAAI National Conference on Artificial Intelligence. Austin, USA, 2000: 852858 [5] Rekleitis I, LeeShue V, Ai P N, et al. Limited Communication, MultiRobot Team Based Coverage // Proc of the IEEE International Conference on Robotics and Automation. New Orleans, USA, 2004, Ⅳ: 34623467 [6] Tao Weimin, How K Y. A Decentralized Approach for Cooperative Sweeping by Multiple Mobile Robots // Proc of the IEEE/RSJ International Conference on Intelligent Robots and Systems. Victoria, Canada, 1998, Ⅰ: 380385 [7] Yamauchi B. FrontierBased Exploration Using Multiple Robots // Proc of the 2nd International Conference on Autonomous Agents. Minneapolis, USA, 1998: 4753 [8] Jerne N K. Idiotypic Networks and Other Preconceived Ideas. Immunological Review, 1984, 79: 524 [9] Farmer J D, Parkard N H, Perelson A S. The Immune System, Adaptation, and Machine Learning. Physica, 1986, 22(1): 187204 [10] Ishiguro A, Wantanabe Y, Kondo T, et al. A Robot with a Decentralized ConsensusMaking Mechanism Based on the Immune System // Proc of the 3rd International Symposium on Autonomous Decentralized Systems. Berlin, Germany, 1997: 231237 [11] Jun J H, Lee D W, Sim K B. Realization of Cooperative Strategies and Swarm Behavior in Distributed Autonomous Robotic Systems Using Artificial Immune System // Proc of the IEEE International Conference on Systems, Man and Cybernetics. Tokyo, Japan, 1999: 614619 [12] Srividhya S, Ferat S. Application of Artificial Immune System Based Intelligent Multi Agent Model to a Mine Detection Problem // Proc of the IEEE International Conference on Systems, Man and Cybernetics. Hammamet, Tunisia, 2002: 69 [13] Choset H. Coverage for Robotics-a Survey of Recent Results. Annals of Mathematics and Artificial Intelligence, 2001, 31(1/2/3/4): 113126 [14] Burgard W, Fox D, Moors M, et al. Collaborative MultiRobot Exploration // Proc of the IEEE International Conference on Robotics and Automation. San Franisco, USA, 2000: 476481 [15] Svennebring J, Koenig S. TrailLaying Robots for Robust Terrain Coverage // Proc of the IEEE International Conference on Robotics and Automation. Taipei, China, 2003, Ⅰ: 7582
2007, Vol. 20 
