Robot navigation using KFLANN place field

This paper presents an implementation of place cells for a robot navigation using the K-iterations fast learning artificial neural networks (KFLANN) clustering algorithm. The KFLANN possesses several desirable properties suitable for place cell robot navigation tasks. The technique proposed is able to autonomously adjust the resolution of cells according to the complexity of the environment. This is achieved through two parameters known as the tolerance and the vigilance of the network. In addition, a navigation system consisting of a topological map building and a place cell path planning strategy is presented. A physical implementation of the system was developed on an autonomous platform and actual results were obtained. The experimental results obtained indicate that the system was able to navigate successfully through the experimental space and also tolerate unexpected discrepancies arising from motor and sensor errors present in a real environment. Furthermore, despite abrupt changes in an environment due to the deliberate introduction of obstacles, the system was still able to cope without changes to the program. The experiment was also extended to include a kidnapped robot scenario and the results were favorable, indicating a positive use of allothetic cue recognition capabilities.