用4个鳍舵实现6自由度精准控制，提升水下机器人机动性与效率
Efficient Control Allocation and 3D Trajectory Tracking of a Highly Manoeuvrable Under-actuated Bio-inspired AUV
- 基于解析控制分配法，结合自适应混合反馈控制器
- 仿真中实现厘米级轨迹跟踪，实测计算耗时仅0.007毫秒
- 适合追求高机动、低功耗的欠驱动水下机器人应用
鳍状推进器可同时用于推力生成和矢量控制，因此鳍驱动自主水下航行器（AUV）可用较少执行器实现高机动性，但控制难度大。本文提出一种针对欠驱动自主水下航行器的解析控制分配方法。通过集成自适应混合反馈控制器，使具备4个执行器的AUV在仿真中实现6自由度（DOF）运动，在真实实验中实现最高5-DOF运动。所提方法在6-DOF轨迹跟踪仿真中优于现有技术，达到厘米级精度，且能量与计算效率更高。真实池实验验证了该方法在复杂3D轨迹跟踪中的鲁棒性与有效性，计算效率显著提升：0.007毫秒（本文）对比22.28毫秒（对比方法）。该方法在性能、能耗与计算效率间取得平衡，为欠驱动水下机器人实现大量自由度高效追踪提供了可行路径。
原文摘要 · Abstract (English)
Fin actuators can be used for for both thrust generation and vectoring. Therefore, fin-driven autonomous underwater vehicles (AUVs) can achieve high maneuverability with a smaller number of actuators, but their control is challenging. This study proposes an analytic control allocation method for underactuated Autonomous Underwater Vehicles (AUVs). By integrating an adaptive hybrid feedback controller, we enable an AUV with 4 actuators to move in 6 degrees of freedom (DOF) in simulation and up to 5-DOF in real-world experiments. The proposed method outperformed state-of-the-art control allocation techniques in 6-DOF trajectory tracking simulations, exhibiting centimeter-scale accuracy and higher energy and computational efficiency. Real-world pool experiments confirmed the method's robustness and efficacy in tracking complex 3D trajectories, with significant computational efficiency gains 0.007 (ms) vs. 22.28 (ms). Our method offers a balance between performance, energy efficiency, and computational efficiency, showcasing a potential avenue for more effective tracking of a large number of DOF for under-actuated underwater robots.
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