Path planning through maze routing for a mobile robot with nonholonomic constraints

Abstract

A comprehensive technique to plan path for a mobile robot with nonholonomic constraints through maze routing technique has been presented. Our robot uses a stereo vision based approach to detect the obstacles by creating dense 3D point clouds from the stereo images. ROS packages have been implemented on the robot for specific tasks of providing: i) Linear and angular velocity commands, ii) Calibration and rectification of the stereo images for generating point clouds, iii) Simulating the URDF (Unified Robot Description Format) module in real time, with respect to the real robot and iv) For visualizing the sensor data. For efficient path planning a hybrid technique using Lee's algorithm, modified by Hadlock and Soukup's algorithm has been implemented. Different path planning results have been shown using the maze routing algorithms. Preliminary results shows that Lee's algorithm is more time consuming in comparison with other algorithms. A hybrid of Lee's with Soukup's algorithm is more efficient but unpredictable for minimal path. A hybrid of Lee's with Hadlock's algorithm is the most efficient and least time consuming.