Multi-vehicle cooperation and nearly fuel-optimal flock guidance in strong background flows

Song, Zhuoyuan; Lipinski, Doug; Mohseni, Kamran

doi:10.1016/j.oceaneng.2017.06.024

Cited by 34 publications

(15 citation statements)

References 61 publications

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“…Our group has worked on several generations of bio-inspired underwater vehicles including five generations of mother vehicles (Krieg et al, 2011) and two generations of daughter vehicles (Song et al, 2016). Our group emphasizes research in hierarchical, swarm-based control (Song et al, 2017) with capable mother vehicles supporting smaller daughter vehicles. The goal is to have a distributed, mobile sensor network.…”

Section: Bio-inspired Vehicle: Cephalobotmentioning

confidence: 99%

Design of a 3-D Printed, Modular Lateral Line Sensory System for Hydrodynamic Force Estimation

Nelson¹,

Mohseni²

2017

mar technol soc j

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This paper presents a sensory system that is biologically inspired by the lateral line sensory system found in fish. This artificial lateral line system provides sensory information to be used in vehicle control algorithms, both to reduce model complexity and to measure hydrodynamic disturbances. The system presented in this paper is a modular implementation that can fit around a vehicle without requiring modifications to the hull. The design and manufacturing processes are presented in detail along with considerations for sensor placement and port spacing. An algorithm for calculating the hydrodynamic forces acting on the surface of a vehicle is derived and experimentally validated. An underwater motion capture system and strain sensors are used to calculate a reference hydrodynamic force that compares favorably with the hydrodynamic force calculated by the lateral line system.

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Section: Bio-inspired Vehicle: Cephalobotmentioning

confidence: 99%

Design of a 3-D Printed, Modular Lateral Line Sensory System for Hydrodynamic Force Estimation

Nelson¹,

Mohseni²

2017

mar technol soc j

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“…Coordinated control of ocean gliders for adaptive ocean sensing has been exhaustively studied in Monterey bay [12][13][14][15]. Algorithms inspired from computational fluid dynamics have also been used to explore coordinated control of swarms in flow fields [16][17][18][19][20]. However, there has been relatively little work in developing a deep understanding of the connection between the dynamics of the flow field and the nature of the optimal trajectories within the flow fields, with a few notable exceptions [21][22][23][24].…”

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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“…Emails: { 1 sachins, 2 zsong} @hawaii.edu coordination of large fixed-wing UAV fleets challenging [4], [5]. This motivates the possibility of the use of fixed-wing UAV fleets for a long term and large scale coverage sensing applications.…”

Section: Introductionmentioning

confidence: 99%

Coordinated Coverage and Fault Tolerance using Fixed-Wing Unmanned Aerial Vehicles

Shriwastav

Song

2020

Preprint

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This paper presents an approach for deploying and maintaining a fleet of homogeneous fixed-wing unmanned aerial vehicles (UAVs) for all-time coverage of an area. Two approaches for loiter circle packing have been presented: square and hexagon packing, and the benefits of hexagon packing for minimizing the number of deployed UAVs have been shown. Based on the number of UAVs available and the desired loitering altitude, the proposed algorithm solves an optimization problem to calculate the centres of the loitering circles and the loitering radius for that altitude. The algorithm also incorporates fault recovery capacity in case of simultaneous multiple UAV failures. These failures could form clusters of survivor (active) UAVs over the area with no overall survivor information. The algorithm deploys a super-agent with a larger communication capacity at a higher altitude to recover from the failure. The superagent collects the information of survivors, and updates the homogeneous radius and the locations of the loitering circles at the same altitude to restore the full coverage. The individual survivor UAVs are then informed and transit to the new loitering circles using Dubin's paths. The relationship with the extent of recoverable loss fractions of the deployed UAVs have been analysed for varying the initial loiter radii. Simulation results have been presented to demonstrate the applicability of the approach and compare the two presented packing approaches.

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Multi-vehicle cooperation and nearly fuel-optimal flock guidance in strong background flows

Cited by 34 publications

References 61 publications

Design of a 3-D Printed, Modular Lateral Line Sensory System for Hydrodynamic Force Estimation

Design of a 3-D Printed, Modular Lateral Line Sensory System for Hydrodynamic Force Estimation

Finite-Horizon, Energy-Optimal Trajectories in Unsteady Flows

Coordinated Coverage and Fault Tolerance using Fixed-Wing Unmanned Aerial Vehicles

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