Open AccessROBIL: Robot Path Planning Based on PBIL Algorithm
Author(s) -
BoYeong Kang,
Miao Xu,
JaeSung Lee,
DaeWon Kim
Publication year - 2014
Publication title -
international journal of advanced robotic systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.394
H-Index - 46
eISSN - 1729-8814
pISSN - 1729-8806
DOI - 10.5772/58872
Subject(s) - initialization , computer science , motion planning , path (computing) , genetic algorithm , probabilistic logic , mathematical optimization , robot , variety (cybernetics) , population , enhanced data rates for gsm evolution , shortest path problem , process (computing) , artificial intelligence , algorithm , machine learning , theoretical computer science , mathematics , programming language , graph , demography , sociology , operating system
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