RI: Small: A Paradigm for Motion Planning Based on Parameterization of Free Space

RI：Small：基于自由空间参数化的运动规划范例

• 批准号: 1619050
• 负责人: Gregory Chirikjian
• 金额: $ 43.39万
• 依托单位: Johns Hopkins University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1619050&HistoricalAwards=false
• 关键词: RI; Small; Paradigm; Motion; Planning

Motion planning is an important part of robotics research that enables robots to move without colliding into obstacles. This project focuses on a new paradigm for motion planning in cluttered environments in which robots and obstacles are represented as unions of ellipsoids, rather than the traditional approach of using polyhedral, which makes it more efficient to calculate potential collisions during planning. In addition to the scientific goals of the project, a longstanding outreach activity with Baltimore Polytechnic High School is part of the project. Two students from this high school per year will spend their "research practicum" in the PI's laboratory during their senior years.In terms of detailed scientific questions, this project builds on the fact that the set of rigid-body motions that cause two ellipsoids to be in collision can be parameterized in closed form, thereby facilitating the sampling of collision-free configurations outside of these sets rather than wasting computational resources on sampling, evaluating whether collisions occur, and throwing away large numbers ofsamples. This is particularly important in so-called "narrow-passage" problems. This approach therefore has the potential to dramatically improve the performance of motion planning algorithms.

运动规划是机器人学研究的重要组成部分，它使机器人能够在不撞上障碍物的情况下移动。本项目致力于在杂乱的环境中进行运动规划的新范例，其中机器人和障碍物被表示为椭球体的并集，而不是传统的使用多面体的方法，这使得在规划过程中计算潜在的碰撞更加高效。除了该项目的科学目标外，与巴尔的摩理工高中的一项长期外展活动也是该项目的一部分。在详细的科学问题方面，这个项目建立在导致两个椭球碰撞的刚体运动集合可以以封闭形式参数化的事实上，从而便于在这些集合之外采样无碰撞构型，而不是浪费计算资源在采样、评估是否发生碰撞以及丢弃大量样本上。在所谓的“窄通道”问题中，这一点尤为重要。因此，这种方法有可能显著提高运动规划算法的性能。