A path planning framework for indoor low-cost mobile robots

Abstract

This paper presents a path planning framework for low-cost mobile robots, which mainly includes three parts: map-processing, collision avoidance and wall following. We change the point cloud maps provided by SLAM to grid maps and filter redundant points in the map-processing procedure. An improved A-star based path planning method and the collision avoidance framework are proposed subsequently. Finally, we propose a local-map-based incremental wall following method without adding extra distance sensors. Experiment results validate the proposed framework on our indoor low-cost mobile robot platform.