Ensuring an adequate supply of energy is a challenge in the development of autonomous sailboats for long-range sailing capability, as the continuous high-frequency execution of control commands leads to high energy consumption. However, as the control of sailboat is much more complex than the other USVs, especially in a complicated and windy environment, simply reducing control frequency will lead to large path tracking error, and even unsuccessful navigation. Based on the V-stability controller of sailboats, this letter proposes an energy-saving (E-saving) control scheme for a sailboat, to enable the energy planning of actuators. While keeping the sailboat traveling within the acceptable range along a desired path, we develop an approach to analyze and reach a satisfying tradeoff between path tracking error and consumed energy. The E-saving method is examined in simulated environments and field experiments using an autonomous sailboat (OceanVoy). The results show that E-saving method reduces energy consumption by approximately 11% compared to that of the previous V-stability controller, indicating that our method can elongate sailing distance.

Sailboats have a wide range of applications on account of their energy-saving, environmentally benign and low-noise characteristics. This has led to the increasing popularity of research in autonomous sailing robots, with concomitant attention paid to the development of methods that ensure their safety during sailing. Sailboats are affected by obstacles and by nonlinear aerodynamic and hydrodynamic factors. In particular, sailboats are affected by the wake generated by a moving vessel, which in this letter is denoted as “moving boat”. Wakes are swift and can have severe adverse effects on a sailboat by causing it to veer off course, stall or capsize. Moreover, sailboats have low mobility and large inertia and will therefore struggle to avoid a wake. Thus, in this letter, we describe our efforts to develop a method to enable a sailboat to perform wake avoidance. We model the wake as a Kelvin wake and generate waypoints for an orientation-restricted Dubins path-based Kelvin wake-avoidance method. A V-stability-based control method has proven suitable for generating the path, after which the desired heading angle for sailboat control is obtained. The resulting wake-avoidance method is verified in simulations and experiments. It enables the sailboat to avoid alongside and toward wakes, and the qualitative results have revealed that the maximum range of roll in alongside scenario and maximum range of surge acceleration in toward scenario are reduced by 57.4% and 23.4%, respectively, relative to a situation when no wake-avoidance method was used. The maximum range of roll in toward scenario is acceptable.

Autonomous sailing robots have attracted much attention, but challenges arise due to the sailing tests require an environment with both aerodynamic and hydrodynamic fields and a sufficient number of sailing robots in readiness. A remotely accessible platform with the advantages of low cost, easy operation and high efficiency is the preferred method to solve this dilemma. Consistent with this goal is the design of Sailboat Test Arena (STAr), a remotely accessible platform for sailing robot design verification, autonomous algorithms validation and sailing control practices. All three parts require extensive testing in real water environments. Hereby, for testers around the world, STAr can be accessed without time difference. A variety of local and remote tests have been conducted in the STAr platform at various location around the world. The results show that STAr is a remotely accessible and effective tool in data collection and skill transfer. With continuous adoption and optimization, STAr will continue to serve as a tool to further promote low-cost, high-efficiency and diverse sailing research, and provide opportunities for more people to experience sailing.

Sailboats are purely wind-driven and thus have great potential for long-term voyaging. For robotic sailboats, the constraints on the energy of the control boards, sensors, communication modules, and actuators are crucial to the sustainability of automation. Reducing the control frequency of actuators is crucial for energy conservation. This study proposes an energy-efficient long-short term (EeLsT) approach for sustainable sailing. In EeLsT, long-term and short-term observers are designed to adaptively take control decisions for time-varying environmental influences (e.g., waves and currents). Our approach can be generally applied as an energy management module in sailing robots. It explicitly leverages the sailing motion characteristics and the dynamic model of the robot considering marine disturbances. We have designed an experimental enhanced simulation platform to evaluate motion performance and energy consumption. Both baseline approach and the scheme incorporating EeLsT method (refer to as EeLsT approach in the subsequent sections) have been conducted. In simulation, EeLsT approach saves 31.8% energy. In the real marine environment, experiments are conducted with OceanVoy, a catamaran sailing robot. The results show that 27.4% of the energy is saved during stable sailing. In long-term sailing, compared to the standby mode when the motors are not working, the average power of the full automation mode has increased by no more than 1 W, i.e. 4% relatively.

Robotic strawberry harvesting remains challenging under partial occlusion, where leaves obscure fruit geometry and makes grasp decisions based on a single shape estimate unreliable. From the same partial observation, multiple 3D completions may be plausible, and a grasp that appears feasible on one completion may fail on another. Existing uncertainty-aware grasping methods typically estimate uncertainty for pose, shape, or individual grasp candidates, but do not explicitly aggregate grasp feasibility across multiple completed object hypotheses to support an object-level attempt-or-abstain decision. This paper presents UNCLE-Grasp, a task-adapted framework that integrates learned shape completion, dropout-based variability estimation, physically grounded grasp evaluation, and risk-aware target-level grasp decisions for leaf-occluded strawberries. Monte Carlo dropout is used to generate multiple completion samples, and for each completion the retained grasp candidates are combined into a single wrench space from which a completion-level force-closure score is computed. Variability of this score across plausible strawberry completions, rather than geometric point variability alone, is then used to quantify target-level grasp uncertainty. A conservative lower confidence bound (LCB) then determines whether the system should execute a grasp or abstain from the target. The framework is evaluated in simulation and on a physical robot under increasing levels of synthetic and real leaf occlusion. At the highest simulated occlusion, UNCLE-Grasp improves success over attempted grasps from 0.780 for the strongest completed baseline to 0.870, while achieving a similar attempt rate of 0.860 versus 0.880 and increasing overall success from 0.680 to 0.740. On the physical robot, it reaches 0.800 success over attempted grasps at the highest synthetic occlusion of approximately 87\%, compared with 0.483 for the strongest completed baseline, although this improvement is accompanied by a lower attempt rate. These results show that the main benefit of UNCLE-Grasp is more reliable risk-aware execution through selective abstention, with a corresponding trade-off between grasp reliability and harvesting yield.