Cooperative Control of Multiple Marine Vehicles Theoretical Challenges and Practical Issues

Aguiar, A. Pedro; Almeida, João Pacheco de; Bayat, Mohammadreza; Cardeira, B.; Cunha, Rita; Häusler, Andreas J.; Maurya, Pramod Kumar; Oliveira, Andre; Pascoal, António; Pereira, Ademir Rodrigues; Rufino, Marta M.; Sebastião, Luís; Silvestre, Carlos; Vanni, F.

doi:10.3182/20090916-3-br-3001.0072

Cited by 28 publications

(22 citation statements)

References 31 publications

(49 reference statements)

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“…By carrying different sensors, the surveying performance of multiple small vehicles may have a performance equal to or exceeding that of a single larger vehicle, with improved levels of robustness and redundancy for a specific task. This has led to various studies of manoeuvring and path planning of fleets (Aguiar et al, 2011;Bean et al, 2007;Burger et al, 2009;Cui et al, 2009Cui et al, , 2010Reeder et al, 2004;Vanni, 2007).…”

Section: Introductionmentioning

confidence: 99%

Numerical investigation of a fleet of towed AUVs

2014

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This paper investigates the influence of the propeller on the drag of twin self-propelled AUVs, firstly, to examine the fleet performance for various propulsive conditions of leading and following AUV and, secondly, to study the parametric influence of transverse separations and longitudinal offsets on the fleet's drag. A series of CFD RANS-SST simulations have been performed at the Reynolds Number 3.2 × 10 6 with a commercial code ANSYS CFX 12.1. Mesh convergence is tested and validated with experimental and empirical results. The RANS-HO and RANS-UT propeller models are selected to estimate the time averaged thrust and torque of the propeller. The results show that the self-propelled vehicles experience an additional drag which is dominated by the thrust distribution of the propeller rather than torque. The drag of the following AUV is increased due to the upstream propeller, defined as a propeller race deduction. For the parametric studies, the results show that increasing the spacing results in a lower drag. The two sources of self-propelled drag increment are the viscous interaction and a direct result of proximity to the propeller race upstream. The result highlights the importance of considering both thrust deduction and any propeller race deductions when calculating the propulsive power consumption.

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Section: Introductionmentioning

confidence: 99%

Numerical investigation of a fleet of towed AUVs

2014

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“…, n}), respectively. Furthermore, we letη i (t) and ρ e,i (t) be its VBT errors with dynamics described in (3). The objective of coordination is to drive the difference between the UAVs LOS phase angles to a desired value, in order to maintain a desired phase shift.…”

Section: B Time-coordinationmentioning

confidence: 99%

Cooperative Vision-Based Tracking of Multiple UAVs

Cichella

Kaminer

Dobrokhodov

et al. 2013

AIAA Guidance, Navigation, and Control (GNC) Conference

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This paper considers the problem of Cooperative Vision Based Tracking of a ground target by a fleet of multiple UAVs. The objective of this work is to present a decentralized control algorithm that enables multiple cooperative vehicles to maintain a horizontal orbital path around the target, while coordinating their space separation. A typical scenario involves multiple aerial surveillance UAVs which are required to monitor a ground object by orbiting around it (target tracking), while maintaining a desired phase shift (coordination). To solve the tracking problem, the angular velocity of each vehicle is used as control input, while the ground speeds are adjusted to ensure coordination. The paper provides a Lyapunov-based solution that guarantees exponential convergence properties of the tracking and distributed time-coordination algorithms. A rigorous proof based on Lyapunov analysis, as well as flight test results demonstrate the effectiveness of the proposed approach.

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“…In marine robotics, coordinated-control problems have received much attention due to the benefits realised by multirobot systems [1]. Related problems include formation control and communication connectivity maintenance [31,20,35,2], and target following [8,11], and these problems are generally approached using closed-loop control with a sliding time horizon.…”

Section: Related Workmentioning

confidence: 99%

A Spatiotemporal Optimal Stopping Problem for Mission Monitoring with Stationary Viewpoints

Best

Martens

2015

Robotics: Science and Systems XI

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Abstract-We consider an optimal stopping formulation of the mission monitoring problem, where a monitor vehicle must remain in close proximity to an autonomous robot that stochastically follows a pre-planned trajectory. This problem arises when autonomous underwater vehicles are monitored by surface vessels, and in a diverse range of other scenarios. The key problem characteristics we consider are that the monitor must remain stationary while observing the robot, and that the robot motion is modelled in general as a stochastic process. We propose a resolution-complete algorithm for this problem that runs in polynomial time. The algorithm is based on a sweepplane approach and generates a motion plan that maximises the expected observation time. A variety of stochastic models may be used to represent the expected robot trajectory. We present results drawn from real AUV trajectories and Monte Carlo simulations that validate the correctness of our algorithm and its feasibility in practice.

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Cooperative Control of Multiple Marine Vehicles Theoretical Challenges and Practical Issues

Cited by 28 publications

References 31 publications

Numerical investigation of a fleet of towed AUVs

Numerical investigation of a fleet of towed AUVs

Cooperative Vision-Based Tracking of Multiple UAVs

A Spatiotemporal Optimal Stopping Problem for Mission Monitoring with Stationary Viewpoints

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