Updating control modes based on terrain classification

The need for terrain-dependent control systems on AGVs is evident when considering the variety of outdoor terrains many AGVs encounter. Although the idea of using terrain classification algorithms to identify the terrain and then update the control modes is well-established, the problem of how to intelligently update the control modes based on classifications has been left relatively unaddressed. This paper presents a simplistic rule, called the update rule, which decides when to change control modes based on past and present terrain classifications and is tuned using empirical data. Using experimental data from the experimental Unmanned Vehicle (XUV) mobile robot, this update rule is shown here to be both robust to misclassiflcations as well as sensitive to terrain transitions. This paper also develops and implements a sliding horizon approach to reaction-based terrain classification for improved sensitivity to terrain transitions. The update rule structure presented here is applicable to reaction and vision based terrain classification of individual terrains.