Obstacle avoidance travel control of robot vehicle using neural network

Abstract

Abstract This paper describes an intelligent travel control algorithm for a mobile robot vehicle using neural networks, and proposes a method that realizes path planning and generation of motion commands simultaneously. Smooth moving trajectories are controlled by the outputs of cascaded identification modules that have learned the dynamic characteristics of a mobile robot vehicle with strong nonlinearities of both driving force and steering angle. A system is adopted that mutually transforms the absolute coordinate and dynamic coordinate. Because a consequence of the coordinate transformation in this system is that the dynamic position values are normally zero, it is possible to reduce greatly the number of training patterns and, at the same time, to be able to construct an environment similar to that in which a human being drives a vehicle. A travel control system, by which a mobile robot vehicle can move on a smooth traveling path and avoid obstacles, is created by introducing a danger function as an expression of static and dynamic obstacles in an unstructured environment. Finally, the validity of the proposed travel control system is confirmed by computer simulations.