ROBIL: Robot Path Planning Based on PBIL Algorithm
- Authors
- Kang, Bo-Yeong; Xu, Miao; Lee, Jaesung; Kim, Dae-Won
- Issue Date
- Sep-2014
- Publisher
- INTECH EUROPE
- Keywords
- Robot Path Planning; Genetic Algorithm; Population-based Incremental Learning
- Citation
- INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS, v.11
- Journal Title
- INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS
- Volume
- 11
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/11817
- DOI
- 10.5772/58872
- ISSN
- 1729-8806
- Abstract
- 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.
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- Appears in
Collections - College of Software > School of Computer Science and Engineering > 1. Journal Articles
- College of Software > Department of Artificial Intelligence > 1. Journal Articles
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