Robot Navigation Using Velocity Potential Fields and Particle Filters for Obstacle Avoidance

Abstract

In this thesis, robot navigation using the Particle Filter based FastSLAM approach for obstacle avoidance derived from a modified Velocity Potential Field method was investigated. A switching controller was developed to deal with robot’s efficient turning direction when close to obstacles. The determination of the efficient turning direction is based on the local map robot derived from its on board local sensing. The estimation of local map and robot path was implemented using the FastSLAM approach. A particle filter was utilized to obtain estimated robot path and obstacles (local map). When robot sensed only obstacles, the estimated robot positions was regarding to obstacles based the measurement of the distance between the robot and obstacles. When the robot detected the goal, estimation of robot path will switch to estimation with regard to the goal in order to obtain better estimated robot positions. Both simulation and experimental results illustrated that estimation with regard to the goal performs better than estimation regarding only to obstacles, because when robot travelled close to the goal, the residual error between estimated robot path and the ideal robot path becomes monotonously decreasing. When robot reached the goal, the estimated robot position and the ideal robot position converge. We investigated our proposed approach in two typical robot navigation scenarios. Simulations were accomplished using MATLAB, and experiments were conducted with the help of both MATLAB and LabVIEW. In simulations and experiments, the robot successfully chose efficiently turning direction to avoid obstacles and finally reached the goal.