ROBIL: Robot Path Planning Based on PBIL Algorithm. (15th September 2014) - Kang, Bo-Yeong; Xu, Miao; Lee, Jaesung; Kim, Dae-Won
Genetic algorithm (GAs) have attracted considerable interest for their usefulness in solving complex robot path planning problems. Specifically, researchers have combined conventional GAs with problem-specific operators and initialization techniques to find the shortest paths in a variety of robotic environments. Unfortunately, these approaches have exhibited inherently unstable performance, and they have tended to make other aspects of the problem-solving process (e.g., adjusting parameter sensitivities and creating high-quality initial populations) unmanageable. As an alternative to conventional GAs, we propose a new population-based incremental learning (PBIL) algorithm for robot path planning, a probabilistic model of nodes, and an edge bank for generating promising paths. Experimental results demonstrate the computational superiority of the proposed method over conventional GA approaches.