Active Singularities for Multivehicle Motion Planning in an N-Vortex System

Lagor, Francis D.; Paley, Derek A.

doi:10.1007/978-3-319-25138-7_30

Cited by 3 publications

(2 citation statements)

References 21 publications

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“…Moreover, Senatore & Ross [ 26 ] exploited this idea further to generate energy optimal paths by controlling the agents to track the background LCS. Recent papers have further explored the connections between optimal control and LCS [ 50 – 52 ] in the context of path planning in the ocean. However, there is still a need to better understand how the prediction horizon and relative cost of actuation in the autonomous agent optimization relate to the use of coherent structures in the unsteady background flow.…”

Section: Introductionmentioning

confidence: 99%

Finite-horizon, energy-efficient trajectories in unsteady flows

2022

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Intelligent mobile sensors, such as uninhabited aerial or underwater vehicles, are becoming prevalent in environmental sensing and monitoring applications. These active sensing platforms operate in unsteady fluid flows, including windy urban environments, hurricanes and ocean currents. Often constrained in their actuation capabilities, the dynamics of these mobile sensors depend strongly on the background flow, making their deployment and control particularly challenging. Therefore, efficient trajectory planning with partial knowledge about the background flow is essential for teams of mobile sensors to adaptively sense and monitor their environments. In this work, we investigate the use of finite-horizon model predictive control (MPC) for the energy-efficient trajectory planning of an active mobile sensor in an unsteady fluid flow field. We uncover connections between trajectories optimized over a finite-time horizon and finite-time Lyapunov exponents of the background flow, confirming that energy-efficient trajectories exploit invariant coherent structures in the flow. We demonstrate our findings on the unsteady double gyre vector field, which is a canonical model for chaotic mixing in the ocean. We present an exhaustive search through critical MPC parameters including the prediction horizon, maximum sensor actuation, and relative penalty on the accumulated state error and actuation effort. We find that even relatively short prediction horizons can often yield energy-efficient trajectories. We also explore these connections on a three-dimensional flow and ocean flow data from the Gulf of Mexico. These results are promising for the adaptive planning of energy-efficient trajectories for swarms of mobile sensors in distributed sensing and monitoring.

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Section: Introductionmentioning

confidence: 99%

Finite-horizon, energy-efficient trajectories in unsteady flows

2022

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“…Moreover, Senatore and Ross [23] exploited this idea further to generate energy optimal paths by controlling the agents to track the background LCS. Recent papers have further explored the connections between optimal control and LCS [47][48][49] in the context of path planning in the ocean. However, there is still a need to better understand how the prediction horizon and relative cost of actuation in the autonomous agent optimization relate to the use of coherent structures in the unsteady background flow.…”

Section: Introductionmentioning

confidence: 99%

Finite-Horizon, Energy-Optimal Trajectories in Unsteady Flows

Krishna,

Song,

Brunton

2021

Preprint

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Intelligent mobile sensors, such as uninhabited aerial or underwater vehicles, are becoming prevalent in environmental sensing and monitoring applications. These active sensing platforms operate in unsteady fluid flows, including windy urban environments, hurricanes, and ocean currents. Often constrained in their actuation capabilities, the dynamics of these mobile sensors depend strongly on the background flow, making their deployment and control particularly challenging. Therefore, efficient trajectory planning with partial knowledge about the background flow is essential for teams of mobile sensors to adaptively sense and monitor their environments. In this work, we investigate the use of finite-horizon model predictive control (MPC) for the energy-efficient trajectory planning of an active mobile sensor in an unsteady fluid flow field. We uncover connections between the finite-time optimal trajectories and finite-time Lyapunov exponents (FTLE) of the background flow, confirming that energy-efficient trajectories exploit invariant coherent structures in the flow. We demonstrate our findings on the unsteady double gyre vector field, which is a canonical model for chaotic mixing in the ocean. We present an exhaustive search through critical MPC parameters including the prediction horizon, maximum sensor actuation, and relative penalty on the accumulated state error and actuation effort. We find that even relatively short prediction horizons can often yield nearly energy-optimal trajectories. These results are promising for the adaptive planning of energy-efficient trajectories for swarms of mobile sensors in distributed sensing and monitoring.

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Feedback stabilization of vortex position near a deformable foil in a uniform flow using camber control

Gebhardt,

Paley

2023

2023 American Control Conference (ACC)

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Active Singularities for Multivehicle Motion Planning in an N-Vortex System

Cited by 3 publications

References 21 publications

Finite-horizon, energy-efficient trajectories in unsteady flows

Finite-horizon, energy-efficient trajectories in unsteady flows

Finite-Horizon, Energy-Optimal Trajectories in Unsteady Flows

Feedback stabilization of vortex position near a deformable foil in a uniform flow using camber control

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