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A human-computer interaction based path planning method for Mobile Robots in a complex environment

机译:复杂环境下基于人机交互的移动机器人路径规划方法

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摘要

In applications of the navigation and control of unmanned ground vehicles in a cross-country environment and tele-driving a rover on the unknown lunar surface for scientific exploration, human-computer interactive path planning and planned path tracking is a significant way of teleoperation. In this paper, we use the method based on Rapidly-Exploring Random Trees (RRTs) to solve the robot path planning problems in a complex environment with crowded obstacles. Aiming at the problem of efficiency reduction that original RRTs planners have difficulties in automatically finding a resolution path in crowded regions of the robot's configuration space, A novel artificial-guided RRTs (AG-RRTs) planner based on multiple RRTs will be introduced. The AG-RRTs planner improves the efficiency of connection and mergence of multiple RRTs by artificial-guided waypoints which mark the regions of narrow passages. In our experiments, the AG-RRTs planner yields much better performance in complex environments than original RRTs planners, which validates the effectiveness and practicality of this new method.
机译:在越野环境中的无人地面车辆的导航和控制以及在未知月球​​表面上遥控驾驶漫游车进行科学探索的应用中,人机交互路径规划和计划路径跟踪是远程操作的一种重要方式。在本文中,我们使用基于快速探索随机树(RRT)的方法来解决具有拥挤障碍物的复杂环境中的机器人路径规划问题。针对效率降低的问题,原始的RRT计划者难以在机器人配置空间的拥挤区域中自动找到解析路径,因此,将介绍一种基于多个RRT的新型人工引导RRT(AG-RRT)计划者。 AG-RRT计划器通过标记狭窄通道区域的人工引导航路点,提高了多个RRT的连接和合并效率。在我们的实验中,与原始RRT计划者相比,AG-RRT计划者在复杂环境中的性能要好得多,这证明了这种新方法的有效性和实用性。

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