Mobile Robot Navigation using potential fields andmarket based optimization

Abstract

A team of mobile robots moving in a shared area raises the problem of safe and autonomous navigation. While avoiding static and dynamic obstacles, mobile robots in a team can lead to complicated and irregular movements. Local reactive approaches are used to deal with situations where robots are moving in dynamic environment; these approaches help in safe navigation of robots but do not give optimal solution. In this work a 2-D navigation strategy is implemented, where a potential field method is used for obstacle avoidance. This potential field method is improved using fuzzy rules, traffic rules and market based optimization (MBO). Fuzzy rules are used to deform repulsive potential fields in the vicinity of obstacles. Traffic rules are used to deal situations where two robots are crossing each other. Market based optimization (MBO) is used to strengthen or weaken repulsive potential fields generated by other robots based on their importance. For the verification of this strategy on more realistic vehicles this navigation strategy is implemented and tested in simulation. Issues while implementing this method and limitations of this navigation strategy are also discussed. Extensive experiments are performed to examine the validity of MBO navigation strategy over traditional potential field (PF) method.