Finding safe and efficient shipping routes in ice-covered waters: A framework and a model

Lehtola, Ville V.; Montewka, Jakub; Goerlandt, Floris; Guinness, Robert; Lensu, Mikko

doi:10.1016/j.coldregions.2019.102795

Cited by 41 publications

(18 citation statements)

References 56 publications

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“…In principle, A* would allow for defining distinct areas where moving is slower or more complicated or more favorable, see e.g. [Lehtola et al, 2019]. Here, however we have treated all navigable space as similarly walkable (or runnable) and, except for the stairs that we added manually, there are no areas where moving is slower or more complicated, such as crawling spaces, or more favorable such as individually preferred routes.…”

Section: Discussion and Future Workmentioning

confidence: 99%

Point Cloud Based 3d Models for Agent Based Simulations in Social Distancing and Evacuation

Nikoohemat

Godoy²,

Valkhoff³

et al. 2021

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Point clouds serve as the raw material for various models, such as Building Information Models (BIM). In this work, we investigate the reconstruction steps needed to create models that can be utilized directly for agent-based simulations. The input data for the reconstruction is captured with an indoor mobile mapping system. To show the prominence of this idea, we run social distancing and evacuation simulations on the reconstructed models. The simulations are run with multiple agents using a vision-based pedestrian model and A*-based path finding algorithm. The limitations of this approach are discussed. The video of the simulation is shared with the audience.Link to the video: https://youtu.be/r2D3IxXt7Ls

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Section: Discussion and Future Workmentioning

confidence: 99%

Point Cloud Based 3d Models for Agent Based Simulations in Social Distancing and Evacuation

Nikoohemat

Godoy²,

Valkhoff³

et al. 2021

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…A significant advantage of the presented approach compared to the traditional mechanistic numerical simulators is that once the algorithm training is completed, ML-based simulations are expected to be computationally much less expensive. This is expected to make them particularly suitable for the massive shipping efficiency optimisation frameworks such as the one developed in Lehtola et al (2019). Also, the computational efficiency will become increasingly important in the upcoming age of autonomous ships when the operational decisions will have to be made constantly and automatically in very short time intervals based on a continuous data stream of dynamically changing environmental parameters.…”

Section: Discussionmentioning

confidence: 99%

A machine learning-based method for simulation of ship speed profile in a complex ice field

Milaković

Marouf

et al. 2019

Ships and Offshore Structures

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Computational methods for predicting ship speed profile in a complex ice field have traditionally relied on mechanistic simulations. However, such methods have difficulties capturing the entire complexity of shipice interaction process due to the incomplete understanding of the underlying physical phenomena. Therefore, data-driven approaches have recently gained increased attention in this context. Hence, this paper proposes a concept of a first machine learning-based simulator of ship speed profile in a complex ice field. The developed approach suggests using supervised machine learning to trace a function mapping several ship and ice parameters to the ship acceleration/deceleration between the two adjacent points along the route. The simulator is trained and tested on a dataset obtained from the full-scale tests of an icebreaking ship. The results show high accuracy of the developed method, with an average error of the simulated ship speed against the measured one ranging from 2.6% to 9.4%.

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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: 95%

“…Before departure, a ship route is planned. In this planning, automated route optimization is important with respect to weather conditions, especially in ice covered waters [2], [3]. While steaming, this route plan is altered if safety (or efficiency) can be increased.…”

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.

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151

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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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Finding safe and efficient shipping routes in ice-covered waters: A framework and a model

Cited by 41 publications

References 56 publications

Point Cloud Based 3d Models for Agent Based Simulations in Social Distancing and Evacuation

Point Cloud Based 3d Models for Agent Based Simulations in Social Distancing and Evacuation

A machine learning-based method for simulation of ship speed profile in a complex ice field

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

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