Field trials of an energy-aware mission planner implemented on an autonomous surface vehicle
Abstract
Mission planning for autonomous marine vehicles is nontrivial due to the dynamic and uncertain nature of the marine environment. Communication can be low-bandwidth and is not always guaranteed, so the operator must rely on the vehicles to adjust their plans according to the realized state of the environment. This paper presents the improvements made to an energy-aware mission planner that allows it to generate and adjust plans for an autonomous surface vehicle (ASV) operating in an uncertain environment. The energy-aware mission planning problem was redefined as a stochastic programming problem, and a two-stage solver was developed to provide an initial plan for the ASV and then adjust it during run-time according to predefined recourse actions. The mission planner and ASV were trialed in Lake Waverley, Tasmania. Adjusting the recourse action criteria demonstrated that the ASV could exhibit conservative or opportunistic behaviors according to the operator's preference of safety margin. In the pursuit of extending the planner's second-stage so that it can predict a suitable recourse action ahead of time, a hybrid long short-term memory energy forecaster was trained from the Waverley mission data. Comparison of the error between the forecaster and the test data shows that the forecaster has a reliable forecast horizon of about 10 s.
Info
Journal Article, 2020
UN SDG Classification
DK Main Research Area
Science/Technology
