Hill-Climbing for a Noisy Potential Field Using Information Entropy

Abstract

For a robot navigation system used in an unpredictable environment, it is generally effective to create a pathway that robots can track for carrying out a given task, such as reaching a goal. In the biological world, ants construct a foraging path using a volatile substance called a pheromone, which has been widely studied and whose characteristics have been used in a transportation network model. When a navigation path is constructed by autonomous agents using this pheromone model, the created potential field can be very noisy, with many local peaks due to the unsynchronized updates of the field. In this paper, a new hill-climbing algorithm is proposed. The algorithm minimizes information entropy and can track dynamic and noisy potential fields. The proposed algorithm is evaluated through a computer simulation.