Phobetor: Princeton University's entry in the 2010 Intelligent Ground Vehicle Competition

摘要

In this paper we present Phobetor, an autonomous outdoor vehicle originally designed for the 2010 Intelligent Ground Vehicle Competition (IGVC). We describe new vision and navigation systems that have yielded 3x increase in obstacle detection speed using parallel processing and robust lane detection results. Phobetor also uses probabilistic local mapping to learn about its environment and Anytime Dynamic A* (AD*) to plan paths to reach its goals. Our vision software is based on color stereo images and uses robust, RANSAC-based algorithms while running fast enough to support real-time autonomous navigation on uneven terrain. AD* allows Phobetor to respond quickly in all situations even when optimal planning takes more time, and uses incremental replanning to increase search efficiency. We augment the cost map of the environment with a potential field which addresses the problem of "wall-hugging" and smoothes generated paths to allow safe and reliable path-following. In summary, we present innovations on Phobetor that are relevant to real-world robotics platforms in uncertain environments.