Probabilistic Planning for Behavior-Based Robots

Partially Observable Markov Decision Process models (POMDPs) have been applied to low-level robot control. We show how to use POMDPs differently, namely for sensorplanning in the context of behavior-based robot systems. This is possible because solutions of POMDPs can be expressed as policy graphs, which are similar to the finite state automata that behavior-based systems use to sequence their behaviors. An advantage of our system over previous POMDP navigation systems is that it is able to find close-to-optimal plans since it plans at a higher level and thus with smaller state spaces. An advantage of our system over behavior-based systems that need to get programmed by their users is that it can optimize plans during missions and thus deal robustly with probabilistic models that are initially inaccurate.

• Type: Conference
• Year: 2001
• Where: FLAIRS
• Authors: Amin Atrash, Sven Koenig
• Keywords: Artificial Intelligence, Behavior-based Robot Systems, Behavior-based Systems, FLAIRS 2001, Partially Observable Markov Decision Process