Symbolic and Qualitative Reasoning for Error Recovery in Robot Programs

这项研究探讨了自动化的可能性， 机器人装配任务的错误处理。 科普能力 由于错误、故障或其他计划外情况而产生的不确定性， 对机器和机器人的效率至关重要。 这种能力 取决于机器能够访问关于任务的信息， 对象、工具和工作环境。 该项目的中心是 一个关于机器需要什么信息来 检测和处理意外情况。 这就涉及到 表征所需信息，自动获取信息， 决定信息的适当表述， 实时使用，区分错误状况与正常事件， 跟踪错误的原因，并生成恢复程序。

This research explores the possibilities of automation of error-handling for robot assembly tasks. The ability to cope with uncertainties due to errors, failures, or other unplanned situations is critical to the effectiveness of machines and robots. That capability depends on the machines having access to information about tasks, objects, tools, and the work environment. This project centers on the question of what information needs to be available to a machine to detect and handle unexpected situations. This involves issues of characterizing the required information, automating its acquisition, deciding on appropriate representations of the information for real-time use, distinguishing an error condition from normal events, tracing causes of errors, and generating recovery procedures.