Probabilistic terrain classification in unstructured environments
Robotics and Autonomous Systems
| Hi-index | 0.00 |
Autonomous vehicles can significantly improve efficiency and safety in applications ranging from warfare to transportation. However, to supply those benefits they must be shown to operate effectively, safely, and reliably in a wide range of terrains and conditions. Most major successes with autonomous vehicles have been limited to somewhat structured environments. We are interested in autonomous vehicles that can operate in forested areas, which are one of the most unstructured and difficult terrains due to the high number and varied nature of potential obstacles, the complexity of the visual field, and the difficulty in getting a good GPS fix due to overhead interference. In this paper we present a novel control system designed with the eventual goal of forest operation. It is built on top of the Learning Applied to Ground Robotics (LAGR) system developed at Carnegie Mellon. The new control system consists of the University of Idaho (UI) LAGR Planner and the UI software for LAGR Vision system. The results show that the combination of these two modules significantly improve the capabilities of the LAGR robot and, more importantly, allow it to perform autonomously in complex environments such as primitive forest trails that the base system could not navigate.