Active cooperative perception in network robot systems using POMDPs

Network robot systems (NRS) provide many scientific and technological challenges, given that robots interact with each other as well as with sensors present in the environment to accomplish certain tasks. In this work, we consider an essential problem in NRS, namely how to perform task planning given the limitations both in on-board sensing as well as in the environment´s sensors. Partially observable Markov decisions processes (POMDPs) form an attractive framework to address planning in the uncertain environments that typify NRS. We show how to model a typical cooperative perception task in a NRS, namely tracking and classifying people, and we present experiments that show how the proposed approach results in an effective interplay between robot and environment sensors.