A Tube-based MPC Scheme for Interaction Control of Underwater Vehicle Manipulator Systems

Νίκου, Αλέξανδρος; Verginis, Christos K.; Dimarogonas, Dimos V.

doi:10.1109/auv.2018.8729801

Cited by 10 publications

(7 citation statements)

References 14 publications

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“…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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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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“…However, ROVs still presented downsides such as its hard and error-prone piloting due to complex water dynamics, requiring trained operators; or the need for a support vessel, leading to expensive operational costs. To ease these drawbacks, there has been increasing research towards intervention Autonomous Underwater Vehicles (AUVs) [ 6 , 7 , 8 ] and Underwater Vehicle Manipulator Systems (UVMS) [ 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Pipe and Valve Characterisation and Mapping for Autonomous Underwater Intervention Tasks

Martin‐Abadal

Oliver-Codina

González-Cid

2022

Sensors

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Nowadays, more frequently, it is necessary to perform underwater operations such as surveying an area or inspecting and intervening on industrial infrastructures such as offshore oil and gas rigs or pipeline networks. Recently, the use of Autonomous Underwater Vehicles (AUV) has grown as a way to automate these tasks, reducing risks and execution time. One of the used sensing modalities is vision, providing RGB high-quality information in the mid to low range, making it appropriate for manipulation or detail inspection tasks. This work presents the use of a deep neural network to perform pixel-wise 3D segmentation of pipes and valves on underwater point clouds generated using a stereo pair of cameras. In addition, two novel algorithms are built to extract information from the detected instances, providing pipe vectors, gripping points, the position of structural elements such as elbows or connections, and valve type and orientation. The information extracted on spatially referenced point clouds can be unified to form an information map of an inspected area. Results show outstanding performance on the network segmentation task, achieving a mean F1-score value of 88.0% at a pixel-wise level and of 95.3% at an instance level. The information extraction algorithm also showcased excellent metrics when extracting information from pipe instances and their structural elements and good enough metrics when extracting data from valves. Finally, the neural network and information algorithms are implemented on an AUV and executed in real-time, validating that the output information stream frame rate of 0.72 fps is high enough to perform manipulation tasks and to ensure full seabed coverage during inspection tasks. The used dataset, along with a trained model and the information algorithms, are provided to the scientific community.

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“…A preliminary conference version of this paper can be found in [22], in which a robust force/torque NMPC scheme for a UVMS in contact with a compliant surface of the environment is designed. That framework has not considered any high‐level tasks and safety with potential obstacles of the environment.…”

Section: Introductionmentioning

confidence: 99%

A robust non‐linear MPC framework for control of underwater vehicle manipulator systems under high‐level tasks

Νίκου

Verginis

Heshmati-alamdari

et al. 2020

IET Control Theory & Appl

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Over the last years, the development and control of Autonomous Underwater Vehicles with attached robotic manipulators, also called Underwater Vehicle Manipulator System (UVMS), has gained significant research attention. In such applications, feedback controllers which guarantee that the end‐effector of the UVMS is fulfilling desired complex tasks should be designed in a way that state and input constraints are taken into consideration. Furthermore, due to their complicated structure, unmodeled dynamics as well as external disturbances may arise. Complex tasks can be conveniently given in the so‐called Linear Temporal Logic (LTL). Motivated by this, the authors develop a combined abstraction and control synthesis framework in which, given the uncertain kinematics/dynamics of the UVMS, a workspace divided into Regions of Interest and a desired LTL task, a sequence of feedback control laws that probably guarantee the LTL formula is provided. The proposed controller falls within the tube‐based non‐linear model predictive control methodology and can handle the rich expressivity of LTL in both safety and reachability specifications. Numerical simulations verify the validity of the proposed framework.

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A Tube-based MPC Scheme for Interaction Control of Underwater Vehicle Manipulator Systems

Cited by 10 publications

References 14 publications

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

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

Real-Time Pipe and Valve Characterisation and Mapping for Autonomous Underwater Intervention Tasks

A robust non‐linear MPC framework for control of underwater vehicle manipulator systems under high‐level tasks

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