摘要
针对户外移动机器人视觉导航,提出了一种基于核心区域和经验知识的道路理解算法.本算法引入核心区域信息融合经验知识加强道路理解的准确性,引入时间影响因子增强道路理解系统的鲁棒性.根据模糊原理对于不同的区域赋予相应的道路颜色隶属度,为安全度要求不同的机器人导航提供更为精确的信息.算法设计中通过优化算法,兼顾了导航的实时性要求.
Aiming at visual navigation of mobile robots in outdoor environment, a road understanding algorithm based on the kernel region information and general knowledge is presented. In this algorithm, kernel region information and general knowledge are used to improve the accuracy of road understanding, and time-concerning impact factor is used to improve the robustness of the algorithm. Based on fuzzy theory, the degrees of membership of road color are allocated to each sub-region, so more precise information can be offered for robot navigation with different degrees of safety. This algorithm is well designed with optimization algorithm, taking the real-timeness of navigation into consideration.
出处
《机器人》
EI
CSCD
北大核心
2005年第4期296-300,共5页
Robot
基金
陕西省教育厅专项科研计划基金资助(02JK110).
关键词
视觉导航
核心区域
时间影响因子
隶属度
实时
visual navigation
kernel region
time-concerning impact factor
degree of membership
real-time
