New Algorithmic Techniques for Task-level Robot Planning

这笔拨款将支持唐纳德博士的研究算法和系统装配机械部件使用兼容的运动策略。这延续了之前在任务级装配规划方面的拨款，并平行于唐纳德博士在运动学近似和机器人理论相关领域的总统青年研究员研究。他在顺应运动规划方面的工作将强调处理传感、实时控制和零件建模中的不确定性和错误。算法将在使用康奈尔机械臂的实验性力控装配系统上进行测试。最终目标是允许力控制装配任务的高级规格，而不需要人类指定所需的力和传感策略。

This grant will support Dr. Donald's research into algorithms and systems for assembly of mechanical parts using compliant motion strategies. This continues a previous grant in task-level assembly planning, and parallel's Dr. Donald's Presidential Young Investigator research in kinodynamic approximation and related areas of robotic theory. His work in compliant motion planning will emphasize handling of uncertainty and errors in sensing, real-time control, and the modeling of parts. Algorithms will be tested on an experimental, force-controlled assembly system using the Cornell robot arm. The ultimate goal is to permit high-level specification of force-controlled assembly tasks, without requiring humans to specify the required forces and sensing strategies.