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

• URL: http://arxiv.org/abs/2607.07139v1
• Date: Wed, 08 Jul 2026 08:32:15 GMT
• Title: Disturbance-aware Motion Planning for Over-actuated Underwater Vehicles Exploiting Actuation Redundancy for High-fidelity 3D Reconstruction
• Abstract summary: High-power thrusters resuspend sediments and induce turbulence, degrading image quality at the sensor input.<n>We address this actuation-to-perception coupling by exploiting redundancy in over-actuated platforms.<n>Across 440 trials, the approach reduces target-region particle velocity by 67%.<n>It improves 3D reconstruction RMSE by 55% versus a disturbance-unaware baseline.
• Score: 9.030271148215196
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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