V-Disparity Based Obstacle Avoidance for Dynamic Path Planning of a Robot-Trailer

Abstract

Structured space exploration with mobile robots is imperative for autonomous operation in challenging outdoor applications. To this end, robots should be equipped with global path planners that ensure coverage and full exploration of the operational area as well as dynamic local planners that address local obstacle avoidance. The paper at hand proposes a local obstacle detection algorithm based on a fast stereo vision processing step, integrated with a dynamic path planner to avoid the detected obstacles in real-time, while simultaneously keeping track of the global path. It considers a robot-trailer articulated system, based on which the trailer trace should cover the entire operational space in order to perform a dedicated application. This is achieved by exploiting a model predictive controller to keep track of the trailer path while performing stereo vision-based local obstacle detection. A global path is initially posed that ensures full coverage of the operational space and during robot's motion, the detected obstacles are reported in the robot's occupancy grid map, which is considered from a hybrid global and local planner approach to avoid them locally. The developed algorithm has been evaluated in a simulation environment and proved adequate performance.