A Non-computationally-intensive Neurocontroller for Autonomous Mobile Robot Navigation

  • Andrés Pérez-Uribe
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 109)


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.


Mobile Robot Obstacle Avoidance Swiss Federal Institute Adaptive Resonance Theory Blue Flower 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Andrés Pérez-Uribe
    • 1
  1. 1.Parallelism and Artificial Intelligence Group (PAI), Department of InformaticsUniversity of FribourgSwitzerland

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