Learning to Predict Slip for Ground Robots

— In this paper we predict the amount of slip an exploration rover would experience using stereo imagery by learning from previous examples of traversing similar terrain. To do that, the information of terrain appearance and geometry regarding some location is correlated to the slip measured by the rover while this location is being traversed. This relationship is learned from previous experience, so slip can be predicted later at a distance from visual information only. The advantages of the approach are: 1) learning from examples allows the system to adapt to unknown terrains rather than using fixed heuristics or predefined rules; 2) the feedback about the observed slip is received from the vehicle’s own sensors which can fully automate the process; 3) learning slip from previous experience can replace complex mechanical modeling of vehicle or terrain, which is time consuming and not necessarily feasible. Predicting slip is motivated by the need to assess the risk of getting tr...

• Type: Conference
• Year: 2006
• Where: ICRA
• Authors: Anelia Angelova, Larry Matthies, Daniel M. Helmick, Gabe Sibley, Pietro Perona
• Keywords: ICRA 2006, Robotics, Slip, Terrain, Terrain Types