Experimental Local Model Predictive Control: Trajectory Tracking and Cost Function Tuning for WMR Reactive Navigation

Abstract

This paper presents local model predictive control techniques for WMR (wheeled mobile robot) reactive navigation. The use of dynamic models and experimental cost function factor adjustments are important issues of the above work. In the methodology presented dynamic experimental models are used. In order to do this, a set of dynamic models obtained from experimental robot system identification are used to predict the horizon of available coordinates. Local trajectory planning is a relevant idea of this work. Hence, when dynamic environments or obstacle avoidance policies are considered, the navigation path planning should be constrained to the robot neighbourhood. The testing and analysis of the experimental results of trajectory tracking are reported. In this context, the different parameter weight performances of the cost function are studied. In this way, different kinds of trajectories are tested by using factor tuning.