提出自适应动态模型，提升水下机械臂系统建模精度与实时性。
Uncertainty-Aware Adaptive Dynamics For Underwater Vehicle-Manipulator Robots
- 基于在线移动窗口估计，融合物理约束实现参数自适应
- 机械臂拟合R²达0.88~0.98，车辆运动预测误差显著降低
- 支持在线计算，适合实际水下控制与仿真应用
准确且自适应的动态模型对受水动力影响、参数时变的水下运载器-机械臂系统至关重要。本文提出一种新型不确定性感知自适应动态模型框架，保持整体参数线性，并在在线估计中嵌入凸物理一致性约束。采用移动窗口估计方法，堆叠时域回归项，强制实现可实现的惯性、阻尼、摩擦和静水力参数，同时量化参数演化带来的不确定性。在搭载4自由度机械臂的BlueROV2 Heavy上实验表明，模型快速收敛且预测校准良好。机械臂拟合的R²为0.88至0.98，斜率接近1；车辆纵荡、垂荡和横滚在强耦合与噪声条件下也具有高保真度。平均求解时间约为0.023秒/更新，证实其在线可行性。相比固定参数模型，各自由度的MAE和RMSE均持续降低。结果表明参数物理合理，置信区间覆盖率接近100%，可支撑可靠的前馈控制与水下环境仿真。
原文摘要 · Abstract (English)
Accurate and adaptive dynamic models are critical for underwater vehicle-manipulator systems where hydrodynamic effects induce time-varying parameters. This paper introduces a novel uncertainty-aware adaptive dynamics model framework that remains linear in lumped vehicle and manipulator parameters, and embeds convex physical consistency constraints during online estimation. Moving horizon estimation is used to stack horizon regressors, enforce realizable inertia, damping, friction, and hydrostatics, and quantify uncertainty from parameter evolution. Experiments on a BlueROV2 Heavy with a 4-DOF manipulator demonstrate rapid convergence and calibrated predictions. Manipulator fits achieve R2 = 0.88 to 0.98 with slopes near unity, while vehicle surge, heave, and roll are reproduced with good fidelity under stronger coupling and noise. Median solver time is approximately 0.023 s per update, confirming online feasibility. A comparison against a fixed parameter model shows consistent reductions in MAE and RMSE across degrees of freedom. Results indicate physically plausible parameters and confidence intervals with near 100% coverage, enabling reliable feedforward control and simulation in underwater environments.
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