引用本文:张强, 陈兵奎, 刘小雍, 张南庆, 刘晓宇, 胡雄.基于空间映射的山地移动机器人环境建模方法[J].西南大学学报(自然科学版),2020,42(2):109~117
【打印本页】   【HTML】   【下载PDF全文】   查看/发表评论  【EndNote】   【RefMan】   【BibTex】
←前一篇|后一篇→ 过刊浏览    高级检索
本文已被:浏览 38次   下载 30 本文二维码信息
码上扫一扫!
分享到: 微信 更多
基于空间映射的山地移动机器人环境建模方法
张强, 陈兵奎, 刘小雍, 张南庆, 刘晓宇, 胡雄1,2,3
1. 遵义师范学院 工学院, 贵州 遵义 563006;2.
2. 重庆大学 机械传动国家重点实验室, 重庆 400044;3.
3. 贵州航天天马机电科技有限公司, 贵州 遵义 563099
摘要:
针对山地环境的特殊性,提出了一种基于空间映射的环境建模方法.通过离散化和空间映射,将三维曲面模型转化为由点集和点距集构成的二维平面模型,从而使三维路径规划问题降维成二维平面路径规划问题;同时,在点距集的生成过程中,利用目标函数来构造相应的点距函数,以使所建环境模型可适用于不同目标下的路径规划任务;最后,在MATLAB中利用蚁群算法对所建环境模型进行路径规划仿真实验,验证了该方法的可行性与通用性,同时将该方法与传统的栅格法和高程建模法进行对比,验证了该方法的优越性.
关键词:  山地移动机器人  环境建模  空间映射  蚁群算法
DOI:10.13718/j.cnki.xdzk.2020.02.014
分类号:
基金项目:贵州省科技厅项目(黔科合LH字[2016]7004号,黔科合LH字[2017]7082号);贵州省教育厅项目(黔教合KY字[2016]254).
An Environment Modeling Method for Mountain Mobile Robots Based on Spatial Mapping
ZHANG Qiang, CHEN Bing-kui, LIU Xiao-yong, ZHANG Nan-qing, LIU Xiao-yu, HU Xiong1,2,3
1. Department of Engineering and Technology, Zunyi Normal University, Zunyi Guizhou 563006, China;2.
2. State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing 400044, China;3.
3. Guizhou Aerospace Tianma Mechanical and Electrical Technology Co., Ltd, Zunyi Guizhou 563099, China
Abstract:
Aiming at the particularity of the mountain environment, an environment modeling method based on spatial mapping is proposed. By discretization and spatial mapping, the 3D surface model is transformed into a 2D plane model, which is composed of a point set and a distance set, so that the 3D path planning problem is transformed into a 2D path planning problem. Meanwhile, the objective function is used to build a point distance function in the mapping process. So this environment model can be applied for different path planning tasks. Finally, a simulation experiment is conducted to explore the feasibility and versatility of the proposed method in MATLAB. At the same time, the result is compared with those obtained by the traditional grid method and the elevation modeling method, and the proposed method is shown to have less computational complexity and higher modeling efficiency.
Key words:  mountain mobile robot  environment modeling  space mapping  ant colony algorithm
手机扫一扫看