Evolutionary approach to ship's trajectory planning within Traffic Separation Schemes

Szlapczynski, Rafal

doi:10.2478/v10012-012-0002-x

Cited by 6 publications

(8 citation statements)

References 17 publications

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“…Finally, based on these categories, a fitness function including penalties for TSS violations has been proposed for evaluation of the sets of trajectories. The effectiveness of correcting TSS violations is additionally increased by applying previously designed specialized TSS-dedicated EA operators (Szlapczynski, 2012). Computer simulation experiments have been carried out and they have confirmed the usefulness of the presented approach.…”

Section: Discussionmentioning

confidence: 99%

“…The best individuals (here: sets of trajectories) have the largest chance of being selected for the next generation, which results in a progress towards the final solution. The main difference between this algorithm and the traditional EA, used in (Szlapczynski, 2011;2012), is that the order of the EA phases is changed here and the specialised operators phase precede reproduction instead of following it. This change allows for applying EA operators, which take into account the data obtained during evaluation phase, without doubling the evaluation phase in the evolutionary cycle.…”

Section: O P T I M I S Atmentioning

confidence: 99%

“…Dealing with Rule 10 of COLREGS affects three phases of the evolutionary methodology: generating the initial population (which should include some predefined TSS-compliant sets of tracks), evaluation (where breaking the rules is detected and penalized by a fitness function) and specialised EA operators, which aim at eliminating the violations of these rules by adjusting the trajectories of ships. The last phase has been described extensively in (Szlapczynski, 2012). This paper focuses on the first two phases.…”

Section: Introductionmentioning

confidence: 99%

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Evolutionary Sets of Safe Ship Trajectories Within Traffic Separation Schemes

Szlapczynski

2012

J. Navigation

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The paper presents the continuation of the author's research on Evolutionary Sets of Safe Ship Trajectories (ESoSST) methodology. In an earlier paper (Szlapczynski, 2011) the author described the foundations of this methodology, which used Evolutionary Algorithms (EA) to search for an optimal set of safe trajectories for all the ships involved in an encounter. The methodology was originally designed for open waters or restricted waters when only the standard Convention on the International Regulations for Preventing Collisions at Sea (COLREGS, 1972) rules apply. However, within Traffic Separation Schemes (TSS), where additionally Rule 10 of COLREGS applies, the problem is much more complex and a new solution is needed. This paper introduces the extended ESoSST methodology, with a focus on changes that have to be made to obey Rule 10 and fully support TSS. These changes include detecting and penalizing TSS violations, as well as the pre-processing phase (generating the initial population, which includes predefined TSS-compliant tracks). The methodology has been designed for possible application in Vessel Traffic Service (VTS) centres. Its new mechanisms are presented with details. The examples are included of the results of the computer simulation tests carried out for the Gulf of Gdansk TSS to illustrate the methodology's effectiveness and functional scope.

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Section: Discussionmentioning

confidence: 99%

Section: O P T I M I S Atmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Evolutionary Sets of Safe Ship Trajectories Within Traffic Separation Schemes

Szlapczynski

2012

J. Navigation

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“…They use an evolutionary algorithm with specialised operators to shape the convergence of the optimisation. In Szlapczynski (2012Szlapczynski ( , 2013 this approach is extended to the use within traffic separation schemes (TSS). In order to limit the variety of individuals of a population during evolutionary optimisation Szlapczynskis approach generates tracks, already partially valid within a TSS after which a number of defined violation are penalised using a specialised fitness function.…”

Section: Introductionmentioning

confidence: 99%

Trajectory Planning with Negotiation for Maritime Collision Avoidance

et al. 2015

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The problem of vessel collisions or near-collision situations on sea, often caused by human error due to incomplete or overwhelming information, is becoming more and more important with rising maritime traffic. Approaches to supply navigators and Vessel Traffic Services with expert knowledge and suggest trajectories for all vessels to avoid collisions, are often aimed at situations where a single planner guides all vessels with perfect information. In contrast, we suggest a two-part procedure which plans trajectories using a specialised A* and negotiates trajectories until a solution is found, which is acceptable for all vessels. The solution obeys collision avoidance rules, includes a dynamic model of all vessels and negotiates trajectories to optimise globally without a global planner and extensive information disclosure. The procedure combines all components necessary to solve a multi-vessel encounter and is tested currently in simulation and on several test beds. The first results show a fast converging optimisation process which after a few negotiation rounds already produce feasible, collision free trajectories. http://www.transnav.eu the International Journal on Marine Navigation and Safety of Sea Transportation Volume 9 Number 3

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“…A deterministic path planning method was introduced by Chang et al in 2003 [5] and developed by Szłapczyński in 2006 [20]. Artificial intelligence methods for ship path planning were reported by Śmierzchalski & Michalewicz (2000) [22], Lisowski (2001) [15], Ito & Zeng (2001) [11], Tam & Bucknall (2010) [23], Tsou & Hsueh (2010) [25], Perera et al (2011) [18], Szłapczyński (2012) [21] and others. However, all of these methods are not deprived of disadvantages.…”

Section: Introductionmentioning

confidence: 99%