Cooperative Rendezvous and Docking for Underwater Robots using Model Predictive Control and Dual Decomposition

Nielsen, Michael Engelbrecht; Johansen, Tor Arne; Blanke, Mogens

doi:10.23919/ecc.2018.8550366

Cited by 10 publications

(7 citation statements)

References 32 publications

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“…MPC implementations on AUVs include trajectory planning (Shen et al, 2015), trajectory tracking (Shen et al, 2016, 2019; Yan et al, 2020), formation control (Gomes & Pereira, 2018, 2019), manipulation (Nikou et al, 2018), and docking (Nielsen et al, 2018). Most of these developments have been demonstrated in simulation studies, and very few cases of experimental and field demonstrations exist.…”

Section: Introductionmentioning

confidence: 99%

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Nonlinear model predictive control for hydrobatics: Experiments with an underactuated AUV

Bhat

Panteli

Stenius

et al. 2023

Journal of Field Robotics

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Hydrobatic autonomous underwater vehicles (AUVs) can be efficient in range and speed, as well as agile in maneuvering. They can be beneficial in scenarios such as obstacle avoidance, inspections, docking, and under‐ice operations. However, such AUVs are underactuated systems—this means exploiting the system dynamics is key to achieving elegant hydrobatic maneuvers with minimum controls. This paper explores the use of model predictive control (MPC) techniques to control underactuated AUVs in hydrobatic maneuvers and presents new simulation and experimental results with the small and hydrobatic SAM AUV. Simulations are performed using nonlinear model predictive control (NMPC) on the full AUV system to provide optimal control policies for several hydrobatic maneuvers in Matlab/Simulink. For implementation on AUV hardware in robot operating system, a linear time varying MPC (LTV‐MPC) is derived from the nonlinear model to enable real‐time control. In simulations, NMPC and LTV‐MPC shows promising results to offer much more efficient control strategies than what can be obtained with PID and linear quadratic regulator based controllers in terms of rise‐time, overshoot, steady‐state error, and robustness. The LTV‐MPC shows satisfactory real‐time performance in experimental validation. The paper further also demonstrates experimentally that LTV‐MPC can be run real‐time on the AUV in performing hydrobatic maneouvers.

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Section: Introductionmentioning

confidence: 99%

“…MPC implementations on AUVs include trajectory planning (Shen et al, 2015), trajectory tracking (Shen et al, 2016(Shen et al, , 2019Yan et al, 2020), formation control (Gomes & Pereira, 2018, manipulation (Nikou et al, 2018), and docking (Nielsen et al, 2018).…”

mentioning

confidence: 99%

Nonlinear model predictive control for hydrobatics: Experiments with an underactuated AUV

Bhat

Panteli

Stenius

et al. 2023

Journal of Field Robotics

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show abstract

“…MPC implementations on autonomous underwater vehicles include trajectory planning [Shen et al, 2015], trajectory tracking [Shen et al, 2016, Shen et al, 2019, Yan et al, 2020, formation control Pereira, 2019], manipulation [Nikou et al, 2018] and docking [Nielsen et al, 2018]. Most of these developments have been demonstrated in simulation studies, and very few cases of experimental and field demonstrations exist.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Model Predictive Control for Hydrobatics: Experiments with an Underactuated AUV

Bhat

Panteli

Stenius

et al. 2022

Preprint

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Hydrobatic Autonomous Underwater Vehicles (AUVs) can be efficient in range and speed, as well as agile in maneuvering. They can be beneficial in scenarios such as obstacle-avoidance, inspections, docking, and under-ice operations. However, such AUVs are underactuated systems - this means exploiting the system dynamics is key to achieving elegant hydrobatic maneuvers with minimum controls. This paper explores the use of Model Predictive Control (MPC) techniques to control underactuated AUVs in hydrobatic maneuvers and presents new simulation and experimental results with the small and hydrobatic SAM AUV. Simulations are performed using nonlinear MPC (NMPC) on the full AUV system to provide optimal control policies for several hydrobatic maneuvers in Matlab/Simulink. For implementation on AUV hardware in ROS, a linear time varying MPC (LTV-MPC) is derived from the nonlinear model to enable real-time control. In simulations, NMPC and LTV-MPC shows promising results to offer much more efficient control strategies than what can be obtained with PID and LQR based controllers in terms of rise-time, overshoot, steady-state error and robustness. The LTV-MPC shows satisfactory real-time performance in experimental validation. The paper further also demonstrates experimentally that LTV-MPC can be run real-time on the AUV in performing hydrobatic maneouvers.

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“…The cooperative use of autonomous underwater vehicles (AUVs) can expand the application range of underwater ocean unmanned systems (Nielsen et al, 2018). AUVs are usually propelled underwater by a motor.…”

Section: Introductionmentioning

confidence: 99%

Motion parameter estimation of AUV based on underwater acoustic Doppler frequency measured by single hydrophone

Rong

Xu²

2022

Front. Mar. Sci.

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This paper describes the use of a single hydrophone to estimate the motion parameters of an autonomous underwater vehicle (AUV) from the underwater acoustic signal excited by its propulsion motor. First, the frequency range of the hydroacoustic signal radiated by the AUV motor is determined, and a detection and recognition model is designed. In the case of uniform linear motion of the AUV, the geometric relationship between the Doppler frequency shift curve of the sound source is derived and the motion model of the sound source and sound line propagation is established. An estimation algorithm for the motion parameters of multiple AUVs based on data from a single hydrophone is derived. Then, for Doppler underwater acoustic signals disturbed by independent identically distributed noise with an arbitrary probability distribution, a cumulative phase difference power amplification instantaneous frequency estimation method is proposed. This method is based on the sum of multiple logarithmic functions. Finally, the effectiveness and accuracy of the algorithm in estimating the motion parameters of multiple AUVs are verified through simulations and experiments.

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Cooperative Rendezvous and Docking for Underwater Robots using Model Predictive Control and Dual Decomposition

Cited by 10 publications

References 32 publications

Nonlinear model predictive control for hydrobatics: Experiments with an underactuated AUV

Nonlinear model predictive control for hydrobatics: Experiments with an underactuated AUV

Nonlinear Model Predictive Control for Hydrobatics: Experiments with an Underactuated AUV

Motion parameter estimation of AUV based on underwater acoustic Doppler frequency measured by single hydrophone

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