Extending the potential fields approach to avoid trapping situations

Reactive mobile robot navigation based on potential field methods has shown to be a good solution for dealing with unknown and dynamic environments, where timely responses are required. Unfortunately, the complexity of the tasks which can successfully be carried out is restricted by the inherent shortcomings of the approach such as trapping situations due to local minima, difficulties passing among closely spaced obstacles, oscillations in narrow corridors, etc... This paper proposes a novel strategy which overcomes totally the first limitation and partially the others by computing an adaptive navigation function on the basis of such artificial potential fields. As a result, navigation is achieved in very difficult scenarios such as maze-like environments. A comparative study on the path length performance of our proposal with regard to other algorithms from the related literature is also presented. Both simulation and real tests are performed.