A Non-computationally-intensive Neurocontroller for Autonomous Mobile Robot Navigation
Andrés Pérez-Uribe · Studies in fuzziness and soft computing · 2003
This chapter presents a neurocontroller architecture for autonomous mobile robot navigation. The main characteristic of such neurocontroller is that it is non-computationally-intensive. It provides a learning robot with the capability to autonomously categorize input data from the environment, to deal with the stability-plasticity dilemma, and to learn a state-to-action mapping that enables it to navigate in a workspace while avoiding obstacles. The neurocontroller architecture is composed of three main modules: an adaptive categorization module, implemented by an unsupervised learning neural architecture called FAST (Flexible Adaptable-Size Topology), a reinforcement learning module (SARSA), and a short-term memory or a planning module, intended to accelerate the learning of behaviors. We describe the use of our neurocontroller in three navigation tasks, each involving a different kind of sensor: 1) obstacle avoidance using infra-red proximity sensors, 2) foraging using a color CCD camera, and 3) wall-following using a grey-level linear vision system.