Bio-inspired navigation of mobile robots

Authors:
Lei Wang;Simon X. Yang;Mohammad Biglarbegian
Affiliations:
School of Engineering, University of Guelph, Guelph, Ontario, Canada;School of Engineering, University of Guelph, Guelph, Ontario, Canada;School of Engineering, University of Guelph, Guelph, Ontario, Canada
Venue:
AIS'12 Proceedings of the Third international conference on Autonomous and Intelligent Systems
Year:
2012

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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.