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Mobile Robot Path Planing Using Gauss Potential Functions and Neural Network : Chapter 30

This work deals with the problem of potential field based mobile robot motion planning in unorganised environment. The new approach, using a combination of negative gradient and vortex field based on Gauss potential functions, is proposed. Radial Basis Function Neural Network (RBF Neural Network) learns the dependence between Gauss function parameters and velocity of mobile robot (or relative velocity between robot and obstacle in dynamical environment) ensuring passage between two closely spaced obstacles and smooth path condition. This approach overcomes some standard problems in classical potential field methods like local minima avoidance, problems of no passage between closely spaced obstacles, avoidance of moving obstacles and trajectory oscillations.

mobile robot, motion planning, potential field, RBF neural network, real-time control

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