Qualitative Belief Space Planning via Compositions

Abstract

Planning under uncertainty is a fundamental problem in robotics. Classical approaches rely on a metrical representation of the world and robot's states to infer the next course of action. While these approaches are considered accurate, they are often susceptible to metric errors and tend to be costly regarding memory and time consumption. However, in some cases, relying on qualitative geometric information alone is sufficient. Hence, the issues described above become an unnecessary burden. This work presents a novel qualitative Belief Space Planning (BSP) approach, highly suitable for platforms with low-cost sensors and particularly appealing in sparse environment scenarios. Our algorithm generalizes its predecessors by avoiding any deterministic assumptions. Moreover, it smoothly incorporates spatial information propagation techniques, known as compositions. We demonstrate our algorithm in simulations and the advantage of using compositions in particular.