Forward chaining for robot and agent navigation using potential fields

Abstract

The ability to navigate successfully is a crucial part of the behaviour of many agents and systems, ranging from robots and computer game characters to neural networks. Navigation in robotics is addressed here using an approach that is extensible to other areas.Potential fields are acknowledged to be a very powerful representation of robot navigation environments. This representation has been largely abandoned though, due to its susceptability to premature termination of progress caused by local minima.We seek to encourage the reopening of research into this method by introducing a new approach called Forward Chaining. This technique avoids premature termination of progress by dynamically reshaping the potential field using subgoals which chain forwards towards the goal. A number of increasingly competent and robust navigation heuristics yielding efficient paths are demonstrated. Various avenues for future research are given.