Nonlinear Motion Control of Multiple Autonomous Underwater Vehicles

Vanni, F.; Aguiar, A. Pedro; Pascoal, A.

doi:10.3182/20070919-3-hr-3904.00014

Cited by 17 publications

(26 citation statements)

References 21 publications

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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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“…In addition, to compute the control laws (11) and (13), full information about the reference velocity and its derivative is required and the full state is needed for the feedback control. With a view to implement both the inner and outer-loop controllers in a more realistic way, the sway velocity v and the derivative of the reference velocityu d are removed from the control laws, and they are viewed as input perturbations that limit the performance of the system.…”

Section: Closed-loop Controllersmentioning

confidence: 99%

Adaptive leader-follower formation control of autonomous marine vehicles

Briñón-Arranz

Pascoal

Aguiar

2014

53rd IEEE Conference on Decision and Control

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This paper addresses the problem of adaptive leader-follower formation control (ALFC) of autonomous marine vehicles. The rationale for this study can be found in a number of challenging geotechnical surveying missions aimed at mapping geological structures under the seabed. Mission specifications require that a group of surface vehicles equipped with acoustic receivers (hydrophones) maneuver in formation and acquire acoustic data emitted by one or more vehicles that carry acoustic emitters. To this effect, we adopt a basic setup for ALFC previously proposed for mobile robots modeled as single integrators and extend it to include explicitly the full dynamic equations of a representative type of marine vehicles. The paper offers a formal proof of convergence of the resulting formation control system. Results of simulations illustrate the performance that can be obtained with the control law proposed.

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“…Reasonable applications are, intelligent transportation systems (ITS), groups of robots working in hazardous environments, in constructions or in some military assignments. The formation control problem has been studied extensively for various types of vehicles, for example underwater and marine vehicles [1], [2], spacecrafts [3], ground vehicles [4], [5], and others. The solution for the formation control problem can be roughly divided into three fundamental approaches, the leader-following approach, the behavioral-based approach and the virtual structure approach.…”

Section: Introductionmentioning

confidence: 99%

Null-space behavioral approach for car-like vehicles with application to intelligent transportation system

Arad

Arogeti

2014

2014 13th International Conference on Control Automation Robotics &Amp; Vision (ICARCV)

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This paper presents a new control algorithm for the coordinated path following problem of car like vehicles. The vehicles are represented by an extended kinematic bicycle model which includes sideslip angles. A nonlinear control law has been designed using Lyapunov approach, and the controller implementation requires knowledge of sideslip angles. A Raghavan observer is introduced in order to observe the vehicle sideslip angles that are typically not directly measured in practice. Trajectory tracking tasks, such as, convergence to a desired path and moving at a desired speed, are defined, along with coordination objectives, such as collision avoidance. The main contribution of this paper stems from the inclusion of the extended kinematic bicycle model in the development, which provides a more accurate model at high speed maneuvers, and the expansion of the null space behavior (NSB) algorithm to the car-like vehicle. The stability of the coordinated path following controller is proven and its effectiveness is demonstrated using numerical simulations and the CarSim© software.Index Terms-intelligent transportation systems (ITS), formation control, null space behavioral control (NSB), autonomous vehicle.

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Nonlinear Motion Control of Multiple Autonomous Underwater Vehicles

Cited by 17 publications

References 21 publications

Numerical investigation of a fleet of towed AUVs

Numerical investigation of a fleet of towed AUVs

Adaptive leader-follower formation control of autonomous marine vehicles

Null-space behavioral approach for car-like vehicles with application to intelligent transportation system

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