Complete Sensor Based Planning in the Real World

基于传感器的规划将传感器信息融入到机器人的规划过程中，而不是传统的规划假设机器人已经完全了解其环境。许多经典的计划者是完整的，因为他们可以在有限的时间内找到一条路径，或者确定不存在这样的路径。大多数基于传感器的规划器都是启发式算法，在各种环境下都能很好地工作，但没有正确的证据来保证可以找到一条路径。此外，他们对启发式失败的时间没有门槛。典型的完全基于传感器的计划者不考虑真实世界的问题，如传感器错误。此外，他们假设传感器具有无限的范围和无限的分辨率。这项研究的目的是将“现实世界”问题纳入一个完整的基于传感器的规划器中。这类现实世界的问题包括航位推算、传感器限制和传感器误差。这项研究将利用被称为路线图的几何结构的特性来考虑这些问题。虽然目前的工作使用移动机器人作为试验台，但这项工作的结果也可以应用于高关节机器人。

Sensor based planning incorporates sensor information into a robot's planning process, in contrast to classical planning which assumes the robot already has full knowledge about its environment. Many classical planners are complete, in that they can find a path in finite time or determine no such path exists. Most sensor based planners are heuristic algorithms that work well in a variety of environments, but have no proofs of correctness to guarantee a path can be found. Furthermore, they have no thresholds for when the heuristics fail. Typical complete sensor based planners do not consider real world issues such as sensor error. Moreover, they assume sensors have unlimited range and infinite resolution. The purpose of this research is to include ``real world'' issues into a complete sensor based planner. Such real world issues include dead reckoning, sensor limitations, and sensor error. This research will exploit properties of a geometric structure, termed a roadmap, to consider these issues. Although current work uses mobile robots as a test-bed, the results of this work can also be applied to highly articulated robots.