Learning Cost Functions for Mobile Robot Navigation in Environments with Deformable Objects

Abstract

Abstract — The ability to reliably navigate through their environment is an important prerequisite for truly autonomous robots. In this paper, we consider the problem of path planning in environments with non-rigid obstacles such as curtains or plants. We present an approach that combines probabilistic roadmaps with a physical simulation of object deformations to determine a path that optimizes the trade-off between the de-formation cost and the distance to be traveled. We describe how our approach utilizes Finite Element theory for calculating the deformation cost. Since the high computational requirements of the corresponding simulation prevent this method from being applicable online, we present an approximative approach that uses a preprocessing step to determine a deformation cost function for each object. This cost function allows us to estimate the deformation costs of arbitrary paths through the objects and is used to evaluate the trajectories generated by the roadmap planner online. We present experiments which demonstrate that the resulting algorithm is highly accurate and at the same time allows to quickly calculate paths in environments with deformable objects. I.