A planning architecture for topological robot navigation in uncertain domains

This paper presents a new navigation architecture for autonomous mobile robots working in uncertain domains. Partially Observable Markov Decision Processes (POMDPs) are suitable mathematical models for solving localization, planning and learning problems in uncertain navigation systems based on a topological representation of the environment. This paper focuses on the planning module, consisting of a two-level layered architecture (a local policy and a global policy) that simplifies the problem of finding optimal policies in POMDPs. The proposed system naturally integrates several planning objectives, such as guiding to a goal room, reducing location uncertainty, and exploring. Some experimental results are shown, carried out with an assistant robot developed in the Electronics Department of the University of Alcala.