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In contrast to traditional mobile robots, renewably powered mobile robotic systems offer the potential for unlimited range at the expense of highly stochastic mobility. Robotic sailboats, termed sailing drones, represent one such example that has received recent attention. After providing a detailed model and corresponding velocity polar for a candidate customized robotic sailboat, this paper presents a stochastic dynamic programming (SDP) approach for time-optimal control of sailing drones in a stochastic wind resource, which provides a feedback control policy to minimize expected time to a prescribed waypoint. The paper provides a Monte Carlo study of the impact of wind direction volatility on the resulting routes, along with an assessment of robustness to mismatches between actual and assumed volatility. 

This paper focuses on the empirical derivation of regret bounds for mobile systems that can vary their locations within a spatiotemporally varying environment in order to maximize performance. In particular, the paper focuses on an airborne wind energy system, where the replacement of towers with tethers and a lifting body allows the system to adjust its altitude continuously, with the goal of operating at the altitude that maximizes net power production. While prior publications have proposed control strategies for this problem, often with favorable results based on simulations that use real wind data, they lack any theoretical or statistical performance guarantees. In the present work, we make use of a very large synthetic data set, identified through parameters from real wind data, to derive probabilistic bounds on the difference between optimal and actual performance, termed regret. The results are presented for a variety of control strategies, including a maximum probability of improvement, upper confidence bound, greedy, and constant altitude approaches.