摘要
将快速扩展随机树(RRT)算法与基于滚动窗口的路径规划相结合,提出一种改进的移动机器人路径规划算法.该方法利用机器人实时测得的局部环境信息,以滚动方式进行在线规划,克服了RRT算法通常只能在已知环境中进行移动机器人路径规划的限制,拓展了应用范围.规划时只考虑窗口环境地图,不必计算障碍物边线的解析式,节省了存储空间,算法实时性得以保证.在此基础上,算法引入启发式估价函数,使得随机树易于朝目标点方向生长.同时,运用回归分析生成新节点,避免了可能产生的局部极小,增强了算法搜索未知空间的能力.最后仿真实验验证了该方法的有效性.
An improved path planning algorithm is proposed by combining rapidly-exploring random tree （RRT） and rolling path planning. In this algorithm, the real-time local environment information detected by the robot is fully used and the on-line planning is performed in a rolling style. Therefore, the RRT algorithm can be used in both known and unknown environment. Only the local environmental map is calculated in the planning to improve the planning efficiency, and thus the planning in real time is guaranteed. The calculation of analytical expressions of the obstacle can be ignored. Hence, the memory is saved greatly. Based on the algorithm of rapidly-exploring random, the heuristic evaluation function is introduced into the improved algorithm, so that the exploring random tree can grow in the direction of target point. The regression analysis, which avoids local minimum, enhances the capability of searching unknown space. The simulation results verify the effectiveness of the improved algorithm.
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2009年第3期337-343,共7页
Pattern Recognition and Artificial Intelligence
基金
国家863计划资助项目(No.2006AA04Z238)
