Presidential Young Investigator Award: Robot Motion Planning

机器人运动规划的研究将利用构形空间展开。目前的规划方法没有考虑到环境模型中存在严重误差的可能性，例如未建模的障碍物或表面缺陷。在实验室和工厂等控制良好的环境之外，这类问题相当常见。挑战是尽早发现错误并采取纠正措施，而不需要完全重新规划。此外，这种方法将被扩展到计划处理其运动事先不完全已知的多个运动对象。我们的想法是，只要其他物体在其预期范围内移动，就能够保证存在实现目标的计划。这两个扩展都将建立在这样一个基本思想之上，即提前计划不需要准确地了解环境的状态。

An investigation of robot motion planning will be expanded using configuration space. Current planning methods do not take into account the possibility of gross errors in the environment model, such as unmodeled obstacles or surface defects. These kinds of problems are quite common outside of well-controlled environments such as laboratories and factories. The challenge is to detect the error as early as possible and to undertake corrective action without requiring complete re-planning. Further, this approach will be extended to planning to deal with multiple moving objects whose motions are not completely known in advance. The idea is to be able to guarantee that there exists a plan that achieves the goal as long as the other objects move within their expected bounds. Both of these extensions will build on the basic idea that to plan ahead one does not need to know the state of the environment exactly.