To achieve robustness in dynamic and uncertain environments, robotic systems must monitor the progress of their plans during execution. This thesis develops a plan executive called Pike that is capable of executing and monitoring plans. The execution monitor at its core quickly and efficiently detects relevant disturbances that threaten future actions in the plan. We present a set of novel offline algorithms that extract sets of candidate causal links from temporally-flexible plans. A second set of algorithms uses these causal links to monitor the execution online and detect problems with low latency. We additionally introduce the TBurton executive, a system capable of robustly meeting a user's high-level goals through the combined use of Pike and a temporal generative planner. An innovative voice-commanded robot is demonstrated in hardware and simulation that robustly meets high level goals and verbalizes any causes of failure using the execution monitor.
展开▼
