Local Autonomous Robot Navigation using Potential Fields

The potential fields method for autonomous robot navigation consists essentially in the assignment of an attractive potential to the goal point and a repulsive potential to each of the obstacles in the environment. Several implementations of potential fields for autonomous robot navigation have been reported. The most simple implementation considers a known environment where fixed potentials can be assigned to the goal and the obstacles. When the obstacles are unknown the potential fields have to be adapted as the robot advances, and detects new obstacles. The implementation of the potential fields method with one attraction potential assigned to the goal point and fixed repulsion points assigned to the obstacles, has the important limitation that for some obstacle configurations it may not be possible to produce appropriate resultant forces to avoid the obstacles. Recently the use of several adjustable attraction points, and the progressive insertion of repulsion points as obstacles are detected online, have proved to be a viable method to avoid large obstacles using potential fields in environments with unknown obstacles. In this chapter we present the main characteristics of the different approaches to implement local robot navigation algorithms using potential fields for known and partially known environments. Different strategies to escape from local minima, that occur when the attraction and repulsion forces cancel each other, are also considered.