Overview of the NAVLAB Project with photos and videos.
Autonomous cross-country navigation is essential for outdoor robots moving about in unstructured environments. Most existing systems use range sensors to determine the shape of the terrain, plan a trajectory that avoids obstacles, and then drive the trajectory. Performance has been limited by the range and accuracy of sensors, insufficient vehicle-terrain interaction models, and the availability of high-speed computers. As these elements improve, higher-speed navigation on rougher terrain becomes possible. We have developed a software system for autonomous navigation that provides for greater capability. The perception system supports a large braking distance by fusing multiple range images to build a map of the terrain in front of the vehicle. The system identifies range shadows and interpolates undersampled regions to account for rough terrain effects. The motion planner reduces computational complexity by investigating a minimum number of trajectories. Speeds along the trajectory are set to provide for dynamic stability. The entire system was tested in simulation, and a subset of the capability was demonstrated on a real vehicle, namely the above-shown HMMWV. Results to date include a continuous 5.1 kilometer run across moderate terrain with obstacles.
My contribution to this project was limited to the design, fabrication, installation and testing of the actuators and computer control hooks for the automatic steering and the braking systems.
A picture of the inside driver cabin shows those two systems: