Autonomous robotic exploration based on multiple rapidly-exploring randomized trees
Autonomous robotic exploration based on multiple rapidly-exploring randomized trees
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DOI：
10.1109/iros.2017.8202319
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发表时间：
2017-09
期刊：
影响因子：
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通讯作者：
Hassan Umari;S. Mukhopadhyay
中科院分区：
文献类型：
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作者：
Hassan Umari;S. Mukhopadhyay
高效的机器人导航需要预定义的地图。存在各种自主探索策略，它们通过检测边界将机器人引导到未探索的空间。边界是将已知空间与未知空间分开的边界。通常前沿检测利用边缘检测等图像处理工具，因此将其限制为二维 (2D) 探索。本文提出了一种基于使用多个快速探索随机树（RRT）的新探索策略。选择 RRT 算法是因为它偏向于未探索的区域。此外，使用 RRT 提供了一种可以扩展到更高维度空间的通用方法。所提出的策略是使用机器人操作系统（ROS）框架来实施和测试的。此外，这项工作使用局部和全局树来检测边界点，从而实现高效的机器人探索。目前的努力仅限于单个机器人的情况。多智能体系统和三维 (3D) 空间的扩展留待未来的工作。
Efficient robotic navigation requires a predefined map. Various autonomous exploration strategies exist, which direct robots to unexplored space by detecting frontiers. Frontiers are boundaries separating known space form unknown space. Usually frontier detection utilizes image processing tools like edge detection, thus limiting it to two dimensional (2D) exploration. This paper presents a new exploration strategy based on the use of multiple Rapidly-exploring Random Trees (RRTs). The RRT algorithm is chosen because, it is biased towards unexplored regions. Also, using RRT provides a general approach which can be extended to higher dimensional spaces. The proposed strategy is implemented and tested using the Robot Operating System (ROS) framework. Additionally this work uses local and global trees for detecting frontier points, which enables efficient robotic exploration. Current efforts are limited to the single robot case. Extension to multi-agent systems and three-dimensional (3D) space is left for future effort.