Mobility characterization for autonomous mobile robots using machine learning

This paper presents a supervised learning approach to improving the autonomous mobility of wheeled robots through sensing the robot's interaction with terrain 'underfoot.' Mobility characterization is cast as a hierarchical task, in which pre-immobilization detection is achieved using support vector machines in time to prevent full immobilization, and if a pre-immobilization condition is detected, the associated terrain feature affecting mobility is identified using a Hidden Markov model. These methods are implemented using a hierarchical, layered control scheme developed for the Yeti robot, a 73-kg, four-wheeled robot designed to perform autonomous medium-range missions in polar terrain. The methodology is motivated by the difficultly of visually recognizing terrain features that impact mobility in low contrast terrain. The efficacy of the approach is evaluated using data from a suite of proprioceptive sensors. Real-time implementation shows that Yeti can consistently detect pre-immobilization conditions, stop in time to avoid unrecoverable immobilization, identify the terrain feature presenting the mobility challenge, and execute an escape sequence to retreat from the condition.