Car-like robot path following in large unstructured environments

摘要

This paper addresses the problem of on-line path following for a car working in unstructured outdoor environments. The partially known map of the environment is updated and expanded in real time by a Simultaneous Localization and Mapping (SLAM) algorithm. This information is used to implement global path planning. A cost graph is initially constructed followed by a search to find the near-optimal path considering uncertainty in both vehicle location and map. Selected points in the global path are connected by continuous-curvature paths. An improved feedback linearization technique is presented to guide the car along the defined path. Experimental results are presented to demonstrate the performance of the algorithms.