An integrated approach for path planning and control for autonomous mobile robots

In this paper, we present an integrated approach for path planning and control for autonomous mobile robots. Typically, path planners pass on the trajectory coordinates to a motion controller that controls steering or curvature of the autonomous robot. However, these approaches ignores the smoothness of trajectory or staying on the path at different times. Our proposed system is a novel approach, which leverages the concept of rapidly exploring random trees (RRTs) for path planning and pure pursuit for tracking at the same time, all without human intervention. We compare our approach to a standard path planning techniques in experiments with real-world autonomous robotic platform. Results show that our proposed approach achieves similar path tracking performance as a high gain controller, but with much less computational effort.