<p> A longstanding goal of robotics is to use robots to perform tasks that require physically interacting with humans. These tasks often require robots to physically manipulate humans, for example in the task of guiding an elderly human and preventing the human from falling. This particular task is of significant importance due to the prevalence of falls and the expanding need for elderly care as the elderly cohort expands in many developed countries. At present, robots have very limited capabilities to support these types of tasks. Current planning approaches are challenged by the intrinsic features of these problems: the control policies of the dynamic agent are unknown, the state information is incomplete, and a rapid reaction time is required. </p><p> This thesis describes an approach to solving these challenges by using simplified models of the dynamic agents and environments that are reasonably accurate over brief time frames. It couples these models with limited-horizon planning. My approach allows for rapid updates of execution plans, which are required due to the short time horizons over which the plans are accurate. This dissertation validates my approach using a series of tasks that require robots to interact with dynamic agents, including a simulation of catching a falling human.</p><p>
| Identifer | oai:union.ndltd.org:PROQUEST/oai:pqdtoai.proquest.com:13419796 |
| Date | 20 December 2018 |
| Creators | Lurz, Joshua Paul |
| Publisher | The George Washington University |
| Source Sets | ProQuest.com |
| Language | English |
| Detected Language | English |
| Type | thesis |
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