AMOS DP Research Cruise 2016: Academic Full-Scale Testing of Experimental Dynamic Positioning Control Algorithms Onboard R/V Gunnerus

Skjetne, Roger; Sørensen, Mikkel Eske Nørgaard; Breivik, Morten; Værnø, Svenn Are Tutturen; Brodtkorb, Astrid H.; Sørensen, Asgeir J.; Kjerstad, Øivind Kåre; Calabrò, Vincenzo; Vinje, Bjørn Ole

doi:10.1115/omae2017-62045

Cited by 14 publications

(8 citation statements)

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“…The control system is a Kongsberg Maritime K-Pos DP-11 system. Further details on the R/V Gunnerus can be found in [35]. The CAA collision avoidance algorithm (Section IV) and the LOS path following guidance law (Section V-B) were implemented at the guidance level of the K-Pos DP-11 system.…”

Section: Resultsmentioning

confidence: 99%

Collision Avoidance for Underactuated Marine Vehicles Using the Constant Avoidance Angle Algorithm

Wiig

Pettersen

Krogstad

2020

IEEE Trans. Contr. Syst. Technol.

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Avoiding collisions is a crucial ability for unmanned vehicles. In this paper, we present the constant avoidance angle algorithm, a reactive method for collision avoidance. It can be used to avoid both static and moving obstacles by making the vehicle keep an avoidance angle between itself and the obstacle edge. Unlike many other algorithms, it requires neither knowledge of the complete obstacle shape, nor that the vehicle follows a desired speed trajectory. Rather, safe vehicle headings are provided at the current vehicle speed. Thus, the speed can be used as an input to the algorithm, which provides flexibility and makes the approach suitable for a wide range of vehicles, including vehicles with a limited speed envelope or high acceleration cost. We demonstrate this by applying the algorithm to a marine vehicle described by a full kinematic and dynamic model in three degrees of freedom. We specifically consider vehicles with underactuated sway dynamics, where the vehicle velocity contains a component which cannot be directly controlled. Such dynamics can be highly detrimental to the performance of collision avoidance algorithms, and need to be included in the design and analysis of control systems for such vehicles. In this paper, we compensate for the underactuation by including these dynamics in the underlying analysis and control design. We provide a mathematical analysis of sparse obstacle scenarios, where we derive conditions under which safe avoidance is guaranteed, even for underactuated vehicles. We furthermore show how the modular nature of the algorithm enables it to be combined both with a target reaching and a path following guidance law. Finally, we validate the results both through numerical simulations, and through fullscale experiments aboard the R/V Gunnerus.

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Section: Resultsmentioning

confidence: 99%

Collision Avoidance for Underactuated Marine Vehicles Using the Constant Avoidance Angle Algorithm

Wiig

Pettersen

Krogstad

2020

IEEE Trans. Contr. Syst. Technol.

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“…Other notable activities include test areas and joint industry projects in Belgium such as the De Vlaamse Waterveg's Smart Shipping initiative 4 , the Dutch Joint Industry Project Autonomous Shipping 5 and Smart Shipping Challenge (SMASH) 6 in the Netherlands and the Danish ShippingLab 7 . We also note the Smart Ships Coalition Marine 2 oneseaecosystem.net/ 3 yara.com/news-and-media/press-kits/yara-birkeland-press-kit/ 4 vlaamsewaterweg.be/smart-shipping/ 5 autonomousshipping.nl/ 6 smashnederland.nl/ 7 shippinglab.dk/ Autonomy Research Site (MARS) 8 in the Great Lakes in Canada/USA, the UK Maritime Autonomous Systems Working Group (MASWRG) and its UK Code of Practice for autonomous ships [16], as well as governmental work by the IMO on the topic of MASS and what needs to happen in the regulatory space [17]. Other relevant work is published by the European GNSS Agency (GSA) regarding User Needs and Requirements related to GNSS [18], white papers and studies by Futurenautics [19], and the Comité Maritime International (CMI) [20] on the topic of maritime law.…”

Section: A Research and Projectsmentioning

confidence: 99%

“…Moreover, when Global Navigation Satellite System (GNSS) positioning is integrated with the control system in a so-called Dynamic Positioning (DP) system, a vessel can counteract the environmental forces acting on it for the purpose of maintaining its position and heading as close as possible to its working position (without anchor), or it can stay on course This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ and steady rather than get carried away by the fluctuating winds and waves [5]. In contrast to these rather sophisticated control systems, integrated perception systems for maritime environment are still inadequately developed for autonomous operations.…”

Section: Introductionmentioning

confidence: 99%

Sensors and AI Techniques for Situational Awareness in Autonomous Ships: A Review

Thombre

Zhao

Ramm-Schmidt³

et al. 2022

IEEE Trans. Intell. Transport. Syst.

130

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Autonomous ships are expected to improve the level of safety and efficiency in future maritime navigation. Such vessels need perception for two purposes: to perform autonomous situational awareness and to monitor the integrity of the sensor system itself. In order to meet these needs, the perception system must fuse data from novel and traditional perception sensors using Artificial Intelligence (AI) techniques. This article overviews the recognized operational requirements that are imposed on regular and autonomous seafaring vessels, and then proceeds to consider suitable sensors and relevant AI techniques for an operational sensor system. The integration of four sensors families is considered: sensors for precise absolute positioning (Global Navigation Satellite System (GNSS) receivers and Inertial Measurement Unit (IMU)), visual sensors (monocular and stereo cameras), audio sensors (microphones), and sensors for remotesensing (RADAR and LiDAR). Additionally, sources of auxiliary data, such as Automatic Identification System (AIS) and external data archives are discussed. The perception tasks are related to well-defined problems, such as situational abnormality detection, vessel classification, and localization, that are solvable using AI techniques. Machine learning methods, such as deep learning and Gaussian processes, are identified to be especially relevant for these problems. The different sensors and AI techniques are characterized keeping in view the operational requirements, and some example state-of-the-art options are compared based on accuracy, complexity, required resources, compatibility and adaptability to maritime environment, and especially towards practical realization of autonomous systems.

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“…4, and has e.g. been used in [22] and [11] to evaluate the performance of dynamic positioning control algorithms.…”

Section: Test Setup and Controller Tuningmentioning

confidence: 99%

Comparing Combinations of Linear and Nonlinear Feedback Terms for Ship Motion Control

2020

Self Cite

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In this paper, combinations of linear and nonlinear feedback terms are investigated for 3 degrees-of-freedom pose and velocity control of ships. Nonlinear control algorithms that are found in the literature often have linear feedback terms, which result in nice globally exponential stability properties when assuming no actuator constraints. However, considering that all actuators have saturation constraints, such stability properties are not feasible in practice. Applying nonlinear feedback terms can be a step to handle such constraints. As a result, this paper explores nonlinear feedback terms for both the kinematic and kinetic control loops. Specifically, three controllers based on a cascaded backstepping control design are implemented and compared through simulations and model-scale experiments in an ocean basin. Stability properties and tuning rules for all the controllers are also provided. Interestingly, the use of nonlinear feedback terms gives the ability to constrain the feedback control inputs globally while simultaneously being able to change the convergence rates locally. The price to be paid is the introduction of additional tuning parameters. The three controller types are compared using performance metrics which consider both control accuracy and energy use. INDEX TERMS Ship motion control, Dynamic positioning, Cascaded backstepping control design, Nonlinear feedback terms, Tuning rules, Performance metrics, Experimental results.

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AMOS DP Research Cruise 2016: Academic Full-Scale Testing of Experimental Dynamic Positioning Control Algorithms Onboard R/V Gunnerus

Cited by 14 publications

References 0 publications

Collision Avoidance for Underactuated Marine Vehicles Using the Constant Avoidance Angle Algorithm

Collision Avoidance for Underactuated Marine Vehicles Using the Constant Avoidance Angle Algorithm

Sensors and AI Techniques for Situational Awareness in Autonomous Ships: A Review

Comparing Combinations of Linear and Nonlinear Feedback Terms for Ship Motion Control

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