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To address the problem of inadequate path-following accuracy and stability in unmanned surface vehicles (USVs) operating in complex environments (characterized by uncertainties such as fluctuating wind speeds and initial position deviations), a guidance method called time-varying sideslip compensated adaptive line-of-sight (TSC-ALOS) is proposed.
First, a time-varying sideslip compensation mechanism is introduced based on real-time measurements of wind speed and direction, which forms the foundation of the improved TSC-ALOS algorithm. This mechanism dynamically compensates for sideslip angle variations induced by environmental disturbances, thereby optimizing the desired heading output of the USV. Subsequently, a proportional-derivative (PD)-based heading controller is designed. This controller translates the desired heading generated by the TSC-ALOS algorithm into actual rudder angle commands, enabling the USV to rapidly and stably track the target heading. This also establishes an effective connection between high-level navigation strategies and low-level control execution. Finally, numerical simulations emulating real marine environments are conducted. The performance of TSC-ALOS algorithm is compared with that of adaptive LOS (ALOS) and traditional LOS algorithms under three operational conditions: no wind, steady wind, and variable wind. Key metrics such as cross-track error and heading stability are specifically analyzed.
Simulation results demonstrate that under no-wind conditions, both TSC-ALOS and ALOS algorithms achieve higher path-following accuracy than traditional LOS algorithm, particularly in handling turning segments. Under steady wind (wind speed: 8.37 m/s) and variable wind (wind speed: 16.73 m/s) conditions, TSC-ALOS significantly reduces the cross-track error, showcasing stronger resilience to environmental disturbances. In scenarios with initial position deviations, the average cross-track error of TSC-ALOS is reduced by 24.6% and 36.8% compared to ALOS and LOS algorithms, respectively.
The TSC-ALOS algorithm demonstrates superior guidance performance across various complex environments, with particularly notable advantages in addressing environmental disturbances and initial position deviations. It offers essential technical support for the development of autonomous navigation systems for USVs and provides insights into future research directions for algorithm optimization.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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