Destination Driven Motion Planning via Obstacle Motion Prediction and Multi-State Path Repair

Abstract

This paper presents a real-time navigating system named Destination Driven Navigator for a mobile robot operating in unstructured static and dynamic environments. We have designed a new obstacle representation method named Cross-Line Obstacle Representation and a new concept “work space” to reduce the robot's search space and the environment storage cost, an Adapted Regression Model to predict dynamic obstacles' motion, Multi-State Path Repair rules to quickly translate an infeasible path into feasible one, and the path-planning algorithm to generate a path. A high-level Destination Driven Navigator uses these methods, models and algorithms to guide a mobile robot traveling in various environments while avoiding static and dynamic obstacles. A group of experiments has been conducted. The results exhibit that the Destination Driven Navigator is a powerful and effective paradigm for robot motion planning and obstacle avoidance.