Autonomous small UGVs have the potential to greatly increase force multiplication capabilities for infantry units. In order for these UGVs to be useful on the battlefield, they must be able to operate under all-weather conditions. For the Daredevil Project, we have explored the use of ultra-wideband (UWB) radar, LIDAR, and stereo vision for all-weather navigation capabilities. UWB radar provides the capability to see through rain, snow, smoke, and fog. LIDAR and stereo vision provide greater accuracy and resolution in clear weather but has difficulty with precipitation and obscurants. We investigate the ways in which the sensor data from UWB radar, LIDAR, and stereo vision can be combined to provide improved performance over the use of a single sensor modality. Our research includes both traditional sensor fusion, where data from multiple sensors is combined in a single representation, and behavior-based sensor fusion, where the data from one sensor is used to activate and deactivate behaviors using other sensor modalities. We use traditional sensor fusion to combine LIDAR and stereo vision for improved obstacle avoidance in clear air, and we use behavior-based sensor fusion to select between radar-based and LIDAR/vision-based obstacle avoidance based on current environmental conditions.

Date Published: 7 May 2010
PDF: 10 pages
Proc. SPIE 7692, Unmanned Systems Technology XII, 76920O (7 May 2010);

Published in SPIE Proceedings Vol. 7692:
Unmanned Systems Technology XII
Grant R. Gerhart; Douglas W. Gage; Charles M. Shoemaker, Editor(s)