An Autonomous Mobile Robot Architecture Using Belief Networks and Neural Networks

This paper introduces a novel mobile robot architecture based on a Situated Belief Network, a belief network that is dynamically updated as a consequence of its current environment. We initially show that it is possible to employ connectionist mechanisms to learn high-level features of the environment from the low-level (sonar) inputs of the robot. These high-level features can then be reasoned with using a belief network that is dynamically modified at run-time to produce different behaviors. We present experimental results for behaviors implemented using this architecture on an Erratic mobile robot platform in terms of both behavioral efficacy and programming efficiency. We conclude that this architecture is both effective and efficient for the control of a mobile robot.