Abstract
Real-time autonomous navigation in unpredictable environments is an essential issue in robotics and artificial intelligence. In this chapter, an adaptive neurofuzzy controller is proposed for mobile robot navigation with local information. A combination of multiple sensors is used to sense the obstacles near the robot, the target location, and the current robot speed. A fuzzy logic system with 48 fuzzy rules is designed. Two learning algorithms are developed to tune the parameters of the membership functions in the proposed neuro-fuzzy model and automatically suppress redundant fuzzy rules from the rule base. The “dead cycle” problem is resolved by a state memory strategy. Under the control of the proposed neuro-fuzzy model, the mobile robot can preferably “see” the surrounding environment, avoid static and moving obstacles automatically, and generate reasonable trajectories toward the target. The effectiveness and efficiency of the proposed approach are demonstrated by simulation and experiment studies.
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Zhu, A., Yang, S. (2009). An Adaptive Neuro-fuzzy Controller for Robot Navigation. In: Yu, W. (eds) Recent Advances in Intelligent Control Systems. Springer, London. https://doi.org/10.1007/978-1-84882-548-2_12
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DOI: https://doi.org/10.1007/978-1-84882-548-2_12
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