Estimating manipulability of unknown obstacles for navigation in indoor environments

The challenging task of navigating in cluttered environments has been studied extensively with indoor autonomous mobile robots. However, few approaches attempt to estimate real-valued costs for manipulating said obstacles with no prior knowledge of the environment. Our approach not only estimates these costs but also models the uncertainty inherent in making such estimates. We present an algorithm that, with no prior knowledge of the environment, allows a mobile robot to determine which obstacles are movable and which are not while navigating a cluttered environment. The algorithm also applies this knowledge of manipulability to obstacles encountered in the future that are similar in appearance to ones previously seen. Using our approach, a mobile robot can act intelligently about uncertain information as well as successfully navigate initially unknown indoor environments without relying on human-provided information.