Expanding the Frontiers of Motion Planning: Feedback, Differential Constraints, and Resolution Completeness

运动规划领域最近在理解关键问题和在整个机器人技术及相关领域的应用方面都取得了许多成功。 它的算法已经在不同的领域得到了应用，如人形机器人、汽车制造、航天器导航、建筑、计算地理学、计算机图形学和计算生物学。该项目扩展了基本的运动规划问题，包括广泛使用的motionPlanning算法未涵盖的关键问题。 这项工作考虑了反馈，微分约束，和决议的完整性，沿着传统的避障运动规划的关注相结合。 通过利用现有运动规划算法在应用中的成功，预计这项工作将产生长期、广泛的影响。 还将通过培养博士生、开设研究生课程、让本科生参与研究以及尝试招募妇女和少数民族来产生教育影响。

The field of motion planning has recently enjoyed numerous successes both in terms of understanding the critical issues and in applications throughout robotics and related fields. Its algorithms have found use in disparate areas such as humanoid robotics, automotive manufacturing, spacecraft navigation, architecture, computational geography, computer graphics, and computational biology. This project expands the basic motion-planning problem to include critical concerns that are not covered by the motionPlanning algorithms that are in widespread use. The work considers a combination of feedback, differential constraints, and resolution completeness, along with the traditional obstacle-avoidance concerns of motion planning. Long-term, broad impact is expected from this work by leveraging off of the successes of existing motion planning algorithms in applications. Educational impact will also arise through the training of doctoral students, the development of graduate-level courses, the involvement of undergraduates in research, and attempts to recruit women and minorities.