摘要
自主移动机器人在室内环境中的导航取得了重大进展,然而地图构建精度较差且路径规划欠佳,限制了这类机器人的实际应用.为了解决这些问题,提出了一种基于引导搜索的路径规划算法,即引力双向快速搜索随机树算法(GBI-RRT),该算法采用目标偏差抽样,有效地引导节点走向目标,减少无效搜索.为了进一步提高导航效率,又提出了一种消除低质量节点,提高路径曲率的路径重组策略,为了验证上述方法的有效性,将其集成到一个基于ROS系统的移动机器人中,并在仿真和真实环境实验中进行了评估.结果表明,GBI-RRT在各种室内环境下的性能均优于现有算法.
Abstract
Significant progress has been made in the navigation of autonomous mobile robots in indoor environments;however,poor map construction accuracy and poor path planning limit the practical applications of such robots.To solve these problems,a path planning algorithm based on guided search,the Gravitational Bidirectional Rapid Search Randomized Tree Algorithm(GBI-RRT)is proposed,which employs target bias sampling to efficiently guide nodes to-wards the target and reduce ineffective search.In order to further improve the navigation efficiency,another path reor-ganization strategy that eliminates low-quality nodes and improves the path curvature is proposed.It is integrated into a mobile robot based on a ROS system and evaluated in simulation and real environment experiments to verify the effec-tiveness of the above method.The results show that GBI-RRT outperforms the existing algorithms in various indoor en-vironments.
基金项目
国家自然科学基金项目(62272426)
国家自然科学青年项目(62106238)
山西省自然科学基金项目(202303021211153)
山西省研究生教育创新项目(2022Y632)
NETL
NSTL
万方数据