Terrain Detection for Unmanned Ground Vehicles Using Hybrid Signal Classification Technique

Today’s autonomous vehicles operate within an increasingly larger set of environments compared to earlier more controlled environments. In particular, unmanned ground vehicles (UGV’s) must be able to travel on whatever terrain the mission offers, including sand, mud, or even snow. These terrains can affect the performance and controllability of the vehicle. Like a human driver who feels his vehicle’s response to the terrain and takes appropriate steps to compensate, a UGV that can autonomously perceive its terrain can also make necessary changes to its control strategy. This article focuses on the development of a terrain detection algorithm based on features extracted from terrain induced vehicle vibration. Experiments are conducted on data collected from the Army’s eXperimental Unmanned Vehicle (XUV). Results will be shown to demonstrate that the algorithm is able to identify multi-differentiated terrains broadly defined as sparse grass, tall grass, asphalt, and gravel.