Dynamic motion planning based on real-time obstacle prediction

Abstract

In this paper we present a virtual force guidance (VFG) system for dynamic motion planning and navigation of a mobile robot. This new method is developed to work with a predicted environment, which is provided by an artificial neural network (ANN) using the information from on-board sensor system. The proposed ANN predictor is trained by a relative-error-backpropagation (REBP) algorithm derived in this paper. The REBP algorithm allows the outputs of an ANN to have minimum relative error, which is better than the conventional backpropagation algorithm in this particular application. The VFG system, which can react to the future environment, assumes that the goal attracts the robot and the future obstacles repulse it. The resultant force determines the desired change in motion. This motion is therefore dependent on both the current motion of the robot and the future environment. Both simulation and experimental results are presented to show our approach can effectively navigate the robot in a human-like fashion.