Artificial Potential Field-Based Motion Planning/Navigation, Dynamic Constrained Optimization and Simple Genetic Hill Climbing

In this paper we show a relationship between artificial potential field (APF) based motion planning/navigation, and constrained optimi zation. We then present a simple genetic hill climbing algorithm (SGHC), which is used to navigate a point robot through an environ ment using the APF approach. We compare SGHC with steepest descent hill climbing (SDHC). In SDHC, candidate moves are evaluated within a 360-degree radius and the best candidate is selected by the robot. One would think that SGHC would be at a disad vantage; however, the performance of SGHC is comparable with SDHC. SGHC has an advantage in that it is capable of evolving (learning) the appropriate step size as well as the appropriate angle of movement.