基于XCS与LS-SVM的ALV在狭隘环境中的避碰规划.pdf

Artificial Intelligence and Robotics Research 人工智能与机器人研究, 2017, 6(1), 22-30 Published Online February 2017 in Hans. /journal/airr /10.12677/airr.2017.61004 Research on Obstacle Avoidance Planning for ALV Based on XCS in Narrow Environments Jie Shao1,2, Qingzhen Wang1 1 Department of Computer Science, Zhengzhou College of Science Technology, Zhengzhou Henan 2 School of Computer Science, Nanjing University of Science and Technology, Nanjing Jiangsu th th th Received: Jan. 27 , 2017; accepted: Feb. 11 , 2017; published: Feb. 16 , 2017 Abstract Local obstacle avoidance in dynamic narrow environments, as a principal ability for ALV, plays an important role in autonomous navigation. Due to premature convergence, local optimal solution, accounting for a larger storage space and other shortcomings of genetic algorithms still exist. In order to improve the ability of obstacle avoidance for ALV, this paper presents a path obstacle avoidance planning method based on LCS for ALV in narrow environments, designs and improves special Genetic Operators. Different environments of simulation results showed that the combina- tion of LS-SVM and learning classifier for ALV path obstacle avoidance planning was convergent, increasing ALV’s ability to quickly find safe paths in narrow environments. Keywords Obstacle Avoidance Planning, LS-SVM, Autonomous Land Vehicle (ALV), Accuracy-Based Learning Classifier System (XCS), Genetic Algorithm 基于XCS和LS-SVM的ALV在狭隘环境中的 避碰规划 1,2 1 邵 杰 ，王清珍 1郑州科技学院信息工程学院，河南 郑州 2南京理工大学计算机学院，江苏 南京 收稿日期：2017年1月27 日；录用日期：2017年2月11 日；发布日期：2017年2月16 日 文章引用: 邵杰, 王清珍. 基于XCS 和LS-SVM 的ALV 在狭隘环境中的避碰规划[J].