@article{haydon_reed_vermillion_2023, title={Persistent Mission Planning of an Energy-Harvesting Autonomous Underwater Vehicle for Gulf Stream Characterization}, ISSN={["1558-0865"]}, DOI={10.1109/TCST.2023.3328105}, abstractNote={Characterizing evolving ocean environments is important to scientific, renewable energy, and military applications. However, performing meaningful characterizations of these resources is complicated by their spatiotemporal evolution and partial observability. In this work, we specifically consider the use of an autonomous underwater vehicle (AUV) with a deployable energy-harvesting kite that enables persistent missions. When the AUV parks itself on the seabed, the kite can deploy, harvesting significant amounts of energy through periodic figure-8 flight. Focusing on a Gulf Stream observational mission, we present a persistent planning algorithm that fuses Gaussian process (GP) modeling with model predictive control (MPC) to optimize AUV charging times to maximize the informativeness of the mission. Based on simulation studies using a mid-Atlantic bight, south Atlantic bight regional ocean model (MAB-SAB-ROM), we demonstrate a 20% reduction in the time required to traverse a given section of the Gulf Stream, which leads to a significant reduction in prediction error.}, journal={IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY}, author={Haydon, Benjamin and Reed, James and Vermillion, Christopher}, year={2023}, month={Nov} }
 @article{govindarajan_haydon_vermillion_2023, title={Predictive Velocity Trajectory Control for a Persistently Operating Solar-Powered Autonomous Surface Vessel}, ISSN={["2378-5861"]}, DOI={10.23919/ACC55779.2023.10156048}, abstractNote={The Gulf Stream represents a major potential resource for renewable energy but is presently only sparsely characterized via radar, buoys, gliders, and intermittently operating human-operated research vessels. Dramatically greater resolution is possible through the use of persistently operating autonomous surface vessels (ASVs), which can be powered by wind, wave, or solar resources. Optimizing the control of these ASVs, taking into account the device and environmental properties, is crucial to obtaining good data. An ASV’s path and velocity profile along that path both significantly influence the amount of a mission domain that can be covered and, ultimately, the scientific quality of the mission. While our previous work focused on optimizing the path of a solar-powered ASV with fixed speed, the present work represents the complement: optimizing the speed for a given path, accounting for the ASV dynamics, flow resource, and solar resource. We perform this optimization through a model predictive controller that maximizes the projected distance traversed, with a terminal incentive that captures the estimated additional long-duration range that is achievable from a given terminal battery state of charge. We present simulation results based on the SeaTrac SP-48 ASV, Mid-Atlantic Bight/South-Atlantic Bight Regional Ocean Model, and European Centre for Medium-Range Weather Forecasts solar model. Our results show improved performance relative to simpler heuristic controllers that aim to maintain constant speed or constant state of charge. However, we also show that the design of the MPC terminal incentive and design of the heuristic comparison controller can significantly impact the achieved performance; by examining underlying simulation results for different designs, we are able to identify likely causes of performance discrepancies.}, journal={2023 AMERICAN CONTROL CONFERENCE, ACC}, author={Govindarajan, Kavin and Haydon, Ben and Vermillion, Chris}, year={2023}, pages={2077–2083} }
 @article{haydon_cole_dunn_keyantuo_chow_moura_vermillion_2022, title={Generalized Empirical Regret Bounds for Control of Renewable Energy Systems in Spatiotemporally Varying Environments}, volume={144}, ISSN={["1528-9028"]}, DOI={10.1115/1.4052396}, abstractNote={Abstract}, number={4}, journal={JOURNAL OF DYNAMIC SYSTEMS MEASUREMENT AND CONTROL-TRANSACTIONS OF THE ASME}, author={Haydon, Ben and Cole, Jack and Dunn, Laurel and Keyantuo, Patrick and Chow, Fotini K. and Moura, Scott and Vermillion, Chris}, year={2022}, month={Apr} }
 @article{haydon_mishra_keyantuo_panagou_chow_moura_vermillion_2021, title={Dynamic Coverage Meets Regret: Unifying Two Control Performance Measures for Mobile Agents in Spatiotemporally Varying Environments}, ISSN={["0743-1546"]}, DOI={10.1109/CDC45484.2021.9682826}, abstractNote={Numerous mobile robotic applications require agents to persistently explore and exploit spatiotemporally varying, partially observable environments. Ultimately, the mathematical notion of regret, which quite simply represents the instantaneous or time-averaged difference between the optimal reward and realized reward, serves as a meaningful measure of how well the agents have exploited the environment. However, while numerous theoretical regret bounds have been derived within the machine learning community, restrictions on the manner in which the environment evolves preclude their application to persistent missions. On the other hand, meaningful theoretical properties can be derived for the related concept of dynamic coverage, which serves as an exploration measurement but does not have an immediately intuitive connection with regret. In this paper, we demonstrate a clear correlation between an appropriately defined measure of dynamic coverage and regret, then go on to derive performance bounds on dynamic coverage as a function of the environmental parameters. We evaluate the correlation for several variants of an airborne wind energy system, for which the objective is to adjust the operating altitude in order to maximize power output in a spatiotemporally evolving wind field.}, journal={2021 60TH IEEE CONFERENCE ON DECISION AND CONTROL (CDC)}, author={Haydon, Ben and Mishra, Kirti D. and Keyantuo, Patrick and Panagou, Dimitra and Chow, Fotini and Moura, Scott and Vermillion, Chris}, year={2021}, pages={521–526} }