Onboard Adaptive Learning for Planetary Surface Rover Control in Rough Terrain

Current and future NASA robotic missions to planetary surfaces are tending toward longer duration and are becoming more ambitious for rough terrain access. For a higher level of autonomy in such missions, the rovers will require behavior that must also adapt to declining rover health and unknown environmental conditions. The MER (Mars Exploration Rovers) called Spirit and Opportunity have both passed 350 days of life on the Martian surface, with possible extensions to 450 days and beyond depending on rover health. Changes in navigational planning due to degradation of the drive motors as they reach their lifetime are currently done on Earth for the Spirit rover. The upcoming 2009 MSL (Mars Science Laboratory) and 2013 AFL (Astrobiology Field Laboratory) missions are planned to last 300-500 days, and will possibly involve traverses on the order of multiple kilometers over challenging terrain. This paper presents an adaptive control algorithm for onboard learning of weights within a free flow hierarchy (FFH) behavior framework for autonomous control of planetary surface rovers that explicitly addresses the issues of rover health and rough terrain access. We also present the results of some laboratory and field studies.