Nonlinear MBPC for mobile robot navigation using genetic algorithms

This paper presents a way of implementing a model-based predictive controller (MBPC) for mobile robot navigation using genetic algorithms to perform online nonlinear optimization. Obstacle avoidance is considered by the controller. Constraints on the maximum attainable speeds are also considered by the algorithm. Experimental results obtained when applying the controller to a TRC LABMATE mobile platform are given in the paper.