EeLsT: An Energy-Efficient Long-Short Term Approach for Sustainable Sailboat Autonomy in Disturbed Marine Environment
Qinbo Sun, Weimin Qi, Huihuan (Alex) Qian
AI summary
Problem
Robotic sailboats lack effective energy management strategies that account for time-varying marine disturbances like waves and currents, limiting their long-term autonomous sustainability.
Approach
EeLsT adaptively switches between long-term and short-term control observers to optimize actuator frequency based on real-time wind and disturbance models, balancing power conservation with motion stability.
Key results
- 31.8% energy reduction in simulation
- 27.4% energy savings during real-world stable sailing
- Successful 1200 km, 30-day autonomous voyage with ≤1 W average power increase over standby mode
- Comprehensive validation of disturbance-aware control in both simulated and open-sea environments
Why it matters
Provides a scalable energy management solution that extends the operational lifespan and reliability of wind-powered marine robots for long-duration ocean monitoring.
Abstract
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.