Bounded Cost HTN Planning for Marine Autonomy


Due to complex oceanic environments, underwater gliders typically must satisfy a variety of environmental conditions in order to complete high level objectives. Underwater navigation, for example, requires that a glider must periodically surface and re-localize in order to ensure adequate progress is being made. Such conditions may be directly encoded in Hierarchical Task Network (HTN) planners to ensure that glider actions are valid over the execution of a plan. However, HTN planners may not be able to find good solutions when actions have uncertain costs, such as when a glider is disturbed by a flow field. We propose a bounded cost HTN planner that leverages a modified potential search method in order to find good navigation plans that satisfy user-defined constraints. Simulation results are presented to validate the approach.

In Global Oceans 2020 Singapore – U.S. Gulf Coast
Mengxue Hou
Mengxue Hou
Assistant Professor, Electrical Engineering

My research interests include robotic autonomy, mobile sensor networks, and human robot interaction. I aim to devise practical, computationally-efficient, and provably-correct algorithms that prepare robotic systems to be cognizant, taskable, and adaptive, and can collaborate with human operators to co-exist in a complex, ever-changing and unknown environment.