Abstract
This paper aims to plan the obstacle-avoiding path for mobile robots based on the Artificial Immune Algorithm (AIA) developed from the immune principle; AIA has a strong parallel processing, learning and memorizing ability. This study will design and control a mobile robot within a limited special scale. Through a research method based on the AIA, this study will find out the optimum obstacle-avoiding path. The main purpose of this study is to make it possible for the mobile robot to reach the target object safely and successfully fulfill its task through optimal path and with minimal rotation angle and best learning efficiency. In the end, through the research method proposed and the experimental results, it will become obvious that the application of the AIA after improvement in the obstacle-avoiding path planning for mobile robots is really effective.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Dasgupta, D. (ed.): Artificial Immune Systems and Their Applications, pp. 3–21. Springer, Heidelberg (1998)
Ishiguro, A., Watanabe, R., Uchikawa, Y.: An Immunological Approach to Dynamic Behavior Control for Autonomous Mobile Robots. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, vol. 1, pp. 495–500 (1995)
Jerne, N.K.: The Immune System. Scientific American 229(1), 52–60 (1973)
Jerne, N.K.: Idiotypic Networks and Other Preconceived Ideas. Immunological Rev. 79, 5–24 (1984)
Ishiguro, A., Kondo, T., Watanabe, Y., Uchikawa, Y.: Dynamic Behavior Arbitration of Autonomous Mobile Robots using Immune Networks. In: Proceedings of the IEEE International Conference on Evolutionary Computation, vol. 2, pp. 722–727 (1995)
Ishiguro, A., Watanabe, Y., Kondo, T., Uchikawa, Y.: Decentralized Consensus-making Mechanisms based on Immune System-application to a Behavior Arbitration of an Autonomous Mobile Robot. In: Proceedings of the IEEE International Conference on Evolutionary Computation, pp. 82–87 (1996)
Vargas, P.A., de Castro, L.N., Michelan, R., Von Zuben, F.J.: Implementation of an Immuno-genetic Network on a Real Khepera II robot. In: Proceedings of the Congress on Evolutionary Computation, vol. 1, pp. 420–426 (2003)
Tu, J., Yang, S.X.: Genetic Algorithm based Path Planning for a Mobile Robot. In: Proceedings of the 2003 IEEE International Conference on Robotics And Automation, vol. 1, pp. 1221–1226 (2003)
Minguez, J.: The Obstacle-restriction Method for Robot Obstacle Avoidance in Difficult Environments. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2284–2290 (2005)
Luh, G.C., Cheng, W.C.: Behavior-Based Intelligent Mobile Robot Using Immunized Reinforcement Adaptive Learning Mechanism. Advanced Engineering Informatics 16, 85–98 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wang, YN., Lee, TS., Tsao, TF. (2007). Plan on Obstacle-Avoiding Path for Mobile Robots Based on Artificial Immune Algorithm. In: Liu, D., Fei, S., Hou, ZG., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4491. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72383-7_82
Download citation
DOI: https://doi.org/10.1007/978-3-540-72383-7_82
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72382-0
Online ISBN: 978-3-540-72383-7
eBook Packages: Computer ScienceComputer Science (R0)