arXiv:2607.07139cs.RO2026-07

通过优化冗余推进器分配,减少水下机器人扰动,提升三维重建质量。

Disturbance-aware Motion Planning for Over-actuated Underwater Vehicles Exploiting Actuation Redundancy for High-fidelity 3D Reconstruction

论文配图:Disturbance-aware Motion Planning for Over-actuated Underwater Vehicles Exploiting Actuation Redundancy for High-fidelity 3D Reconstruction
图 1 · 摘自论文原文
  • 利用推进器冗余在虚拟扰动场中寻找最优推力分配方案。
  • 目标区域粒子速度降低67%,3D重建误差减少55%,成功率98.5%。
  • 适合高精度水下测绘与人工辅助巡检场景,实时性达10Hz。

水下机器人在靠近脆弱目标作业时,强动力推进器会搅动沉积物并引发湍流,降低传感器输入图像质量。传统控制器仅优化车辆自身性能(如跟踪与稳定性),未考虑推进对感知的影响。本文针对过驱动平台的冗余特性,提出一种扰动感知的运动规划方法。以八推进器遥控潜水器为例,多个推力配置可实现相同运动轨迹,通过搜索其零空间,在满足运动约束条件下最小化任务相关区域的预测扰动。方法采用基于叶素理论的控制级推进器尾流代理模型,包含方向衰减特性,经粒子图像测速(PIV)验证,近尾流轴处决定系数$R^2=0.99$,主尾流区$R^2>0.82$。系统支持实时冗余求解器,运行频率10 Hz(单次求解45毫秒)。在440组试验中,目标区域粒子速度下降67%(p<0.001),3D重建均方根误差(RMSE)较无扰动意识基线降低55%(1.9±0.4 mm vs. 4.3±1.8 mm),重建成功率达98.5%。框架支持自主扫描(定量评估)与人工辅助巡检(补充材料展示)。

原文摘要 · Abstract (English)

Underwater robots often operate near delicate targets where high-power thrusters resuspend sediments and induce turbulence, degrading image quality at the sensor input. Conventional controllers optimize vehicle-centric objectives, such as tracking and stability, without accounting for the impact of actuation on sensing. We address this actuation-to-perception coupling by exploiting redundancy in over-actuated platforms. For an eight-thruster ROV, multiple thrust allocations can yield the same motion; we search this null space to minimize predicted disturbance in a task-relevant target region while enforcing motion constraints. Our method uses a control-oriented thruster-wake proxy derived from actuator-disk theory with directional attenuation and validated by PIV ($R^2 = 0.99$ near the wake axis; $R^2 > 0.82$ in the primary wake region), together with a real-time redundancy-resolving allocator running at 10 Hz (45 ms/solve). Across 440 trials, the approach reduces target-region particle velocity by 67% ($p < 0.001$), improves 3D reconstruction RMSE by 55% versus a disturbance-unaware baseline ($1.9 \pm 0.4$ mm vs. $4.3 \pm 1.8$ mm), and achieves a 98.5% reconstruction success rate. The framework supports autonomous scanning, which is quantitatively evaluated, and operator-assisted inspection, which is demonstrated in the supplementary materials.

水下机器人运动规划三维重建推进冗余

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