Abstract
This paper presents a bio-inspired neural network algorithm for mobile robot path planning in unknown environments. A novel learning algorithm combining Skinner’s operant conditioning and a shunting neural dynamics model is applied to the path planning. The proposed algorithm depends mainly on an angular velocity map that has two parts: one from the target, which drives the robot to move toward to target, and the other from obstacles that repels the robot for obstacle avoidance. An improved biological learning algorithm is proposed for mobile robot path planning. Simulation results show that the proposed algorithm not only allows the robot to navigate efficiently in cluttered environments, but also significantly improves the computational and training time. The proposed algorithm offers insights into the research and applications of biologically inspired neural networks.
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© 2012 Springer-Verlag Berlin Heidelberg
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Wang, L., Yang, S.X., Biglarbegian, M. (2012). Bio-inspired Navigation of Mobile Robots. In: Kamel, M., Karray, F., Hagras, H. (eds) Autonomous and Intelligent Systems. AIS 2012. Lecture Notes in Computer Science(), vol 7326. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31368-4_8
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DOI: https://doi.org/10.1007/978-3-642-31368-4_8
Publisher Name: Springer, Berlin, Heidelberg
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