Robotics & Machine Learning Daily News2024,Issue(Jun.19) :89-90.

Findings from University College London (UCL) Has Provided New Data on Robotics (Efficient Global Navigational Planning In 3-d Structures Based On Point Cloud T omography)

伦敦大学学院(UCL)的发现提供了机器人学的新数据(基于点云地形的三维结构高效全球导航规划)

Robotics & Machine Learning Daily News2024,Issue(Jun.19) :89-90.

Findings from University College London (UCL) Has Provided New Data on Robotics (Efficient Global Navigational Planning In 3-d Structures Based On Point Cloud T omography)

伦敦大学学院(UCL)的发现提供了机器人学的新数据(基于点云地形的三维结构高效全球导航规划)

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摘要

由一名新闻记者-机器人与机器学习的工作人员新闻编辑每日新闻-调查人员发布了关于机器人的新报告。根据NewsRx记者从英国伦敦发回的新闻报道,研究表明:“复杂三维场景中的导航需要合适的环境表示,以便高效地理解场景和生成轨迹。我们提出了一种基于对环境的层析理解的高效和可扩展的全球导航框架,用于多层结构中的地面机器人导航。”本研究经费来源于国家自然科学基金(NSFC)。我们的新闻编辑在(UCL)上引用了大学隆德学院的一项研究,“我们的方法使用点云地图生成断层切片,将几何结构编码为地面和天花板标高。然后,”该方法综合考虑机器人的运动能力来评估场景的可穿越性,通过并行计算加速了断层图像的构建和场景的评估,进一步减轻了直接在三维空间规划的轨迹生成复杂性,通过多个断层图像切片搜索来生成三维轨迹,并分别调整机器人高度以避免悬垂。并在一个四足机器人上进一步测试它。

Abstract

By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators publish new report on Ro botics. According to news reporting originating from London, United Kingdom, by NewsRx correspondents, research stated, "Navigation in complex 3-D scenarios req uires appropriate environment representation for efficient scene understanding a nd trajectory generation. We propose a highly efficient and extensible global na vigation framework based on a tomographic understanding of the environment to na vigate ground robots in multilayer structures." Financial support for this research came from National Natural Science Foundatio n of China (NSFC). Our news editors obtained a quote from the research from University College Lond on (UCL), "Our approach generates tomogram slices using the point cloud map to e ncode the geometric structure as ground and ceiling elevations. Then, it evaluat es the scene traversability considering the robot's motion capabilities. Both th e tomogram construction and the scene evaluation are accelerated through paralle l computation. Our approach further alleviates the trajectory generation complex ity compared with planning in 3-D spaces directly. It generates 3-D trajectories by searching through multiple tomogram slices and separately adjusts the robot height to avoid overhangs. We evaluate our framework in various simulation scena rios and further test it in the real world on a quadrupedal robot."

Key words

London/United Kingdom/Europe/Emerging Technologies/Machine Learning/Robot/Robotics/University College London (UCL )

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出版年

2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

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