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

运动规划是机器人研究的重要组成部分，它使机器人能够在不碰撞到障碍物的情况下移动。该项目的重点是在混乱的环境中，机器人和障碍物的运动规划的一个新的范例表示为联盟的椭圆体，而不是使用多面体的传统方法，这使得它更有效地计算潜在的碰撞在规划过程中。除了该项目的科学目标外，与巴尔的摩理工高中的长期外联活动也是该项目的一部分。该高中每年有两名学生将在PI的实验室度过他们的“研究实习”。在详细的科学问题方面，该项目建立在导致两个椭球体碰撞的刚体运动集合可以以封闭形式参数化的事实上，从而便于对这些集合之外的无冲突配置进行采样，而不是将计算资源浪费在采样上，评估是否发生冲突，and throwing扔away large大number数字of samples样品.这在所谓的“窄通道”问题中尤其重要。因此，这种方法有可能显着提高运动规划算法的性能。

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.