WO2004053814A1 - Systeme anticollision adaptatif de consultation - Google Patents

Systeme anticollision adaptatif de consultation Download PDF

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Publication number
WO2004053814A1
WO2004053814A1 PCT/SG2003/000282 SG0300282W WO2004053814A1 WO 2004053814 A1 WO2004053814 A1 WO 2004053814A1 SG 0300282 W SG0300282 W SG 0300282W WO 2004053814 A1 WO2004053814 A1 WO 2004053814A1
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WO
WIPO (PCT)
Prior art keywords
conflict
vessels
engine
determined
cpa
Prior art date
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PCT/SG2003/000282
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English (en)
Inventor
Lye Huat Tan
Hung Khoon Tan
Swee Heng Ong
Chai Swan Law
Wee Choon Teo
Teck Hwee Andrew Wong
Swee Guan Desmond Ng
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Defence Science & Technology Agency
Wee, Kok Ling
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Application filed by Defence Science & Technology Agency, Wee, Kok Ling filed Critical Defence Science & Technology Agency
Priority to AU2003283945A priority Critical patent/AU2003283945A1/en
Publication of WO2004053814A1 publication Critical patent/WO2004053814A1/fr

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    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G3/00—Traffic control systems for marine craft
    • G08G3/02—Anti-collision systems

Definitions

  • the present invention is directed towards a system to assist in the collision avoidance of vessels.
  • the present invention is directed towards implementing an adaptive knowledge-based inference engine to provide advisory information to vessels for collision avoidance.
  • the present invention provides an adaptive collision avoidance advisory system for vessels in complex situations unlike current adaptive collision avoidance systems which cater only for adaptive modeling of vessels
  • the input to the preferred system is a global sea situation picture with all the potential conflicts.
  • the system will detect and classify each conflict into a number of possible scenarios, namely head-on, crossing, or overtaking. Based on each classification, the system will derive the action to be taken by the necessary vessel(s). This constitutes one possible resolution.
  • the system can then assess the different combinations of all possible resolutions and present the recommended combinations with rankings to provide an indication of how good the recommendations are.
  • the system is able to adapt to influence from external environment, including human experts, to adjust the rankings given to the recommendations.
  • the present invention provides a collision avoidance system for three or more vessels including: a path predicting engine to predict the future position of each said vessel; a conflict detection engine to determine whether any said vessels are in conflict based on the predicted path determined by said path predicting engine; and a conflict resolution engine to determine a course of action for any said vessels determined to be in conflict by said conflict detection engine.
  • the path predicting engine may include a ship maneuvering module, and may also predict the position of vessels based on submitted planned routes which may be stored in a planned route database.
  • the path predicting engine may also take advantage of a sea situation picture.
  • the conflict detection engine may categorise the potential conflict into levels of seriousness, and the conflict resolution engine may use a scheduling engine for low level conflicts.
  • Figure 1 is a block diagram for the adaptive collision avoidance advisory system.
  • Figure 2 shows the pair-wise collision detection algorithm.
  • Figure 3 shows an example of the sweeping algorithm.
  • Figure 4 is a block diagram showing the different component of the adaptive knowledge-based inference engine.
  • Figure 5 is a typical manoeuvres chart used to classify conflicts into the various scenarios.
  • Figure 6 depicts the set of rules used to classify conflicts.
  • Figure 7 shows some examples for classifying conflicts.
  • Figure 8 shows an example of the route generation module.
  • Figure 9 shows the selection operation of the Genetic Algorithm (GA).
  • Figure 10 shows the crossover operation of the GA.
  • FIG. 11 shows the mutation operation of the GA. DESCRIPTION OF PREFERRED EMBODIMENT
  • the present invention provides a system and method for implementing adaptive collision avoidance advisory system for vessels in complex situations using a hybrid of artificially intelligent techniques.
  • a complex situation may involve three or more vessels.
  • the five main components in the preferred arrangement are the path predicting engine 3, the conflict detection engine 5, the conflict resolution engine 7, the route generation module 9, and the scheduling engine 8.
  • the planned route database 1 , the ship manoeuvring model 2, and the sea situation picture (SSP) 6 form the preferred inputs to the system, while the display module 4 presents the output from the system.
  • SSP sea situation picture
  • the adaptive collision avoidance advisory system of the present invention ideally provides two levels of alerts and advices, for multiple vessels in potential conflicting situation, one is for early warning with a further look ahead time (level 1), and the other is for close quarters within the nearer future time (level 2). It is noted that existing collision avoidance systems are only designed for a single pair of vessels, and are not able to handle three or more vessels.
  • the path predicting engine 3 can utilize a ship manoeuvring model to predict the future positions of vessels, based on submitted planned routes (if available) as well as the current position, course, and speed of vessels in the SSP.
  • the SSP can be formulated from input sensors, such as radars and an automatic identification system (AIS), which would provide position, course, and speed of all vessels detected. Alternatively these values may be inputted from another system that calculates position, course and speed based on sensor detection.
  • AIS automatic identification system
  • the ship manoeuvring model is recommended to provide high fidelity prediction based on vessel hydrodynamics and environmental data, such as weather, sea state, sea lanes, boarding ground, etc. This can be used for level 2 alerts.
  • the ship paths may be predicted based on the vessels' current speed and their submitted planned routes if available, otherwise the predicted path may be assumed to be straight ahead based on the vessels' current course.
  • a vessel's predicted route consists of a start way point, an end way point and a number of other intermediate way points. These way points can then be joined by straight lines to form a representation of a vessel's route.
  • Level 1 alerts will be given to vessels that have reported their intended sail route to the system. These are kept in the planned route database 1. Likewise, these planned routes are represented as way-points.
  • the conflict detection engine 5 will determine conflicting situations, and ideally prioritise these situations according to some predefined criteria, such as time to potential conflict.
  • a pair-wise collision detection algorithm (see figure 2), based on the closest point of approach (CPA) and danger circle 10 computation, should form the core of this engine.
  • the pair-wise collision detection algorithm is used between two way-points for a pair of vessels. If the CPA between two vessels is less than a certain well-established or predefined safety distance (i.e. within the danger circle), the algorithm should flag such a situation to be a potential collision.
  • the collision algorithm works by grouping pairs of vessels for computation. If there are 3 vessels, for eg. Vessels A, B and C, then there will be 3 possible ways of grouping pairs of vessels given by AB, BC and AC. For a multiple global conflict scenario, this may be primarily handled by the Genetic Algorithm and the Artificial Neural Network which is described later
  • the dotted lines with larger dots represent portions of the predicted path of two vessels.
  • timeCPA time of closest point of approach
  • the positions and velocities of the two vessels at the way-points just before the collision detection are given by (x1 , y1 ), (vx1 , vy1 ), (x2, y2), (vx2, vy2) respectively.
  • the closest point of approach on predicted path for both vessels, CPA1 and CPA2 can then be computed respectively.
  • the time for both vessels to reach their CPAs could be computed using the following:
  • CPA-[ and CPA 2 can then be computed by extrapolating the respective current position to this time:
  • the mid-point between CPA1 and CPA2, CPA, will be the CPA for the two vessels.
  • the danger circle 10, of diameter centered about the CPA, is used to determine if the two vessels are in potential conflict with each other.
  • the diameter of the danger circle can be computed by: speed
  • I is the higher value of the diameters for the minimum turning circle of both vessels; sepmin and sepmax denotes the safety separations between the vessels necessary given the threshold speeds of spdmin and spdmax.
  • the typical values for sepmin, sepmax, spdmin, and spdmax are 2, 4, 4, and 12 respectively. These are rule-of-thumb given by experienced mariners.
  • the engine could incorporate a sweeping algorithm, which generates areas swept out by vessels based on their motions. If there is a collision between two vessels, the areas swept by them must intersect one another. In other words if a vessel is represented as a rectangular bounding box, an area will be swept out by each vessel when they move. By extrapolating both the areas swept out by the pair of vessels without reference to the time factor, it can be determined whether the two extrapolated areas do intersect. If they do, then the pair of vessels may possibly be in potential collision and will be subjected to the pair-wise collision detection test. Otherwise, collision is impossible (see figure 3 for an example of the algorithm).
  • the conflict resolution engine 5 employs a hybrid of artificially intelligent techniques to come up with advisory information for resolving conflicts.
  • Figure 4 shows the preferred components that are used in this engine .
  • the rule-based inference engine Based on the collisions detected in the conflict detection engine, the rule-based inference engine will categorise them into level 1 and 2 conflicts. For level 1 conflicts (early warning), the rule-based inference engine will determine if such conflicts could be grouped to form congested areas which are areas of particularly high sea traffic density, that is, the number of vessels per unit area of sea coverage exceeds a certain well-established or predefined threshold value. If so, the scheduling engine will be invoked to resolve these conflicts.
  • the rule-based inference engine uses the manoeuvres chart (figure 5) and the conflict classification table (figure 6), which are derived from the COLREGs, to infer the resolutions for each pair of conflicts.
  • the manoeuvres chart divides a ship into different sectors and depending on which sector the intersecting line between the centre of a reference ship and that of another ship, the general instructions in the tabular chart should be referenced and used.
  • the classification table summarizes all the possible conflict scenarios with recommended actions and action-ships, in accordance to COLREGs, in terms of the conflicting sectors.
  • Figure 7 shows some examples on how to use the manoeuvres chart and conflict classification table.
  • the manoeuvres chart provides resolutions for the vessel that need to take action, known as the action ship (the vessel that need not take action is known as the Right-Of-Way or ROW ship).
  • Every resolution inferred from the rule-base inference engine is based on sound reasoning guidelines provided by a domain expert, such as COLREGs or experienced mariners.
  • the solutions space includes different combination of these inferred resolutions (or knowledge).
  • the Genetic Algorithm (GA) is an algorithm for searching a local optimal solution given a solution space is used to explore the solution space while the Artificial Neural Network (ANN) is employed to rank each solution.
  • the ANN could be based on a back propagation model with four input nodes, such as the number of potential conflicts, time to first potential conflict, the computed delay from expected time of arrival (ETA) and expert user's judgement.
  • the ANN provides the means for the system to adapt by "learning" the best solution with inputs from the external environment.
  • each feedback from the environment is ideally fed through the ANN to allow the adjustment of the weights.
  • the terminating condition for this single-case learning can be capped to, say 100 cycles, of the updating algorithm in the ANN. This is in addition to the threshold terminating condition in a traditional ANN so as to achieve real-timeliness of the system.
  • One problem with this is that the system tends to forget what it has learned.
  • historical knowledge can be kept and a data mining algorithm used to extract good examples to continuously refresh the memory of the ANN.
  • the data mining algorithm can also be used to discover new rules, if any, for the rule-based inference engine such as for example by a Decision Tree Learning algorithm, ID3).
  • the present invention seeks to provide a method and system for performing sound reasoning and learning, similar to a human cognitive process, to rank solutions encompassing this inferred knowledge.
  • the scheduling engine 8 will allocate time based on priorities that will be explained later, for vessels to transit the congested area, requesting them to slow down or even speed up, based on the manoeuvrability of the vessel input by the conflict resolution engine. Hence, new predicted paths returned to the conflict resolution engine 7 are the same path except that the time for some of the way- points are modified. Scheduling will be done based on certain priorities, such as the vessels' sizes, the loads they carried, risk in manoeuvrability, etc.
  • the route generation module 9 will translate these actions by modifying the original predicted path of the action vessel by changing relevant way-points and timing at each point to follow a recommended action while avoiding any danger circle.
  • the new predicted paths are fed-back to the conflict resolution engine 7 for further analysis.
  • Figure 8 shows an example of how this is done.
  • the route generation module will attempt to avoid the danger circle computed in the conflict detection engine taking into account the manoeuvrability of the vessel input by the conflict resolution engine.
  • the rule-based inference engine starts the reasoning process with facts 11, i.e. SSP and conflicts detected, inputted to the working memory of the system.
  • This reasoning process will create new knowledge in the working memory, based on a set of predefined rules such as the COLREGs, derived from human expertise.
  • the inferred knowledge can be inserted back into the working memory and could invoke further reasoning to infer more knowledge.
  • the result of this process is a set of inferred knowledge that is to be used to formulate the solution.
  • a gene Each piece of inferred knowledge that could be used to formulate the solution is denoted as a gene.
  • a possible combination of these genes will give rise to a chromosome, which represents a possible solution.
  • a chromosome For example, in a multiple ship scenario, solution to a pair among the ships concerned is a gene, a chromosome is a combination of pair-wise resolutions. The chromosome is a possible solution to the multi-ship conflict.
  • the solution space may include different combinations, and this will provide the population for the GA.
  • Each solution may be ranked by a fitness function as described earlier, which can be computed by using the ANN. Solutions can evolve by using the basic operations, namely selection, crossover, and mutation, as shown in figures 9 to 11.. Figure 9 shows the selection operation of the GA in the preferred system.
  • the GA starts with an initial set (or population) of solutions (or chromosomes). Each generation of the GA will create a new population by selecting "good" chromosomes from the previous population, based on the fitness function. To avoid being “caught” in a local optimal solution, the crossover (figure 10) and mutation (figure 11) operations are applied on the chromosomes in the new population.
  • the crossover operation involves 2 chromosomes, and choosing a crossover point; the genes after the crossover point in the 2 chromosomes are exchanged, in the hope of deriving a better solution (indicative by the fitness function).
  • the mutation operation involves just 1 chromosome. This will "change” (or flip) some genes in the chromosome to see if it would be a better solution.
  • a number of ranked solutions can then be presented to the user via the display module.
  • the best solution could then be selected from the influence of the environment, which could be the advice from a human expert.
  • the ANN is then trained based on the best solution, by rewarding the best solution (i.e. increasing the desired output value) and penalising the rest of the solutions (decreasing the desired output values). In this way the system is able to adapt whilst in operation.
  • Another unique feature of the preferred adaptive knowledge-based inference engine is the use of an offline data mining algorithm 15, whereby knowledge such as expert user's inputs and weights of the ANN are mined, to enable the ANN to retrain as and when necessary. This requires the system to keep track of historical knowledge 18 as shown in figure 4.
  • the data mining algorithm 15 may be used to automate the detection of outliers in the historical knowledge base.
  • the adaptive knowledge-based inference engine is proposed to provide a holistic view based on most (if not all) pairs of conflicting vessels so as to evaluate the various avoidance recommendations and come up with the collision avoidance solution that best resolves all the conflicts in the current environment that is, including multiple ship scenarios.
  • the present invention provides a method for knowledge-based system to adapt to the changing environment.
  • the present invention seeks to provide a method and system for performing sound reasoning and learning, similar to a human cognitive process, to rank solutions encompassing this inferred knowledge. Whilst the method and system of the present invention has been summarised and explained by illustrative examples, it will be appreciated by those skilled in the art that many widely varying embodiments and applications are within the teaching and scope of the present invention, and that the examples presented herein are by way of illustration only and should not be construed as limiting the scope of this invention.

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  • Engineering & Computer Science (AREA)
  • Ocean & Marine Engineering (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Traffic Control Systems (AREA)

Abstract

La présente invention concerne un système anticollision destiné à au moins trois navires qui comprend un moteur de prévision de route permettant de prévoir la position future de chaque navire, un moteur de détection de conflit permettant de déterminer si, partant de la route prévue déterminée par le moteur de prévision de route, des navires sont en situation conflictuelle et, un moteur de résolution de conflit permettant de déterminer un cap de réaction pour les navires dont le moteur de détection de conflit a déterminé qu'ils étaient en situation conflictuelle.
PCT/SG2003/000282 2002-12-10 2003-12-10 Systeme anticollision adaptatif de consultation WO2004053814A1 (fr)

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Cited By (15)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2009103837A1 (fr) * 2008-02-19 2009-08-27 Juan Mariano Bendito Vallori Système de pilote automatique d'évitement pour embarcations
WO2011027037A1 (fr) * 2009-09-04 2011-03-10 Valtion Teknillinen Tutkimuskeskus Système d'indication de risque de voie navigable intelligent et procédé associé
WO2011065848A1 (fr) 2009-11-26 2011-06-03 Akademia Morska W Szczecinie Procédé et système de support de décision de navigation dans le processus de navigation maritime sûre
EP2695808A1 (fr) * 2012-08-10 2014-02-12 ABB Research Ltd. Indicateur de niveau d'attention requis
ES2444566R1 (es) * 2012-04-12 2014-04-01 Universidad De Cádiz Sistema de ayuda anticolisión para buques (SAAB)
CN103714718A (zh) * 2013-12-31 2014-04-09 武汉理工大学 一种内河桥区船舶安全航行预控系统
EP2479737A3 (fr) * 2011-01-21 2014-10-22 Icom Incorporated Dispositif d'identification de cible et procédé de prédiction de mouvement de cible
WO2015181626A1 (fr) * 2014-05-28 2015-12-03 Cgg Services Sa Système et procédé pour optimiser dynamiquement des opérations sismiques à cuves multiples
EP3261078A1 (fr) * 2011-05-23 2017-12-27 ION Geophysical Corporation Système de surveillance et de défense contre les menaces maritimes
WO2019121237A1 (fr) 2017-12-22 2019-06-27 Rolls-Royce Plc Procédé et système d'évitement de collision pour navires maritimes
WO2020127923A1 (fr) * 2018-12-21 2020-06-25 Thales Procédé d'échanges de données entre des entités
CN115410419A (zh) * 2022-08-23 2022-11-29 交通运输部天津水运工程科学研究所 一种船舶系泊预警方法、系统、电子设备及存储介质
CN116343453A (zh) * 2023-05-26 2023-06-27 广州海洋地质调查局三亚南海地质研究所 基于大数据的船舶定位信息监测与预警方法及相关设备
CN116610125A (zh) * 2023-05-26 2023-08-18 北鲲睿航科技(上海)有限公司 一种用于智能船舶主动防撞系统的避碰方法及系统
EP4290497A1 (fr) * 2022-06-10 2023-12-13 Furuno Electric Co., Ltd. Appareil de planification de l'itinéraire de navigation et procédé de planification de l'itinéraire de navigation

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JPH11272999A (ja) * 1998-03-24 1999-10-08 Tokimec Inc 船舶衝突予防援助装置及び船舶衝突予防援助方法
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Cited By (22)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2009103837A1 (fr) * 2008-02-19 2009-08-27 Juan Mariano Bendito Vallori Système de pilote automatique d'évitement pour embarcations
WO2011027037A1 (fr) * 2009-09-04 2011-03-10 Valtion Teknillinen Tutkimuskeskus Système d'indication de risque de voie navigable intelligent et procédé associé
WO2011065848A1 (fr) 2009-11-26 2011-06-03 Akademia Morska W Szczecinie Procédé et système de support de décision de navigation dans le processus de navigation maritime sûre
EP2479737A3 (fr) * 2011-01-21 2014-10-22 Icom Incorporated Dispositif d'identification de cible et procédé de prédiction de mouvement de cible
EP3261078A1 (fr) * 2011-05-23 2017-12-27 ION Geophysical Corporation Système de surveillance et de défense contre les menaces maritimes
ES2444566R1 (es) * 2012-04-12 2014-04-01 Universidad De Cádiz Sistema de ayuda anticolisión para buques (SAAB)
WO2014023483A1 (fr) 2012-08-10 2014-02-13 Abb Research Ltd Indicateur d'attention
EP2695808A1 (fr) * 2012-08-10 2014-02-12 ABB Research Ltd. Indicateur de niveau d'attention requis
CN103714718A (zh) * 2013-12-31 2014-04-09 武汉理工大学 一种内河桥区船舶安全航行预控系统
CN103714718B (zh) * 2013-12-31 2016-01-13 武汉理工大学 一种内河桥区船舶安全航行预控系统
WO2015181626A1 (fr) * 2014-05-28 2015-12-03 Cgg Services Sa Système et procédé pour optimiser dynamiquement des opérations sismiques à cuves multiples
WO2019121237A1 (fr) 2017-12-22 2019-06-27 Rolls-Royce Plc Procédé et système d'évitement de collision pour navires maritimes
US11915594B2 (en) 2017-12-22 2024-02-27 Rolls-Royce Plc Collision avoidance method and system for marine vessels
WO2020127923A1 (fr) * 2018-12-21 2020-06-25 Thales Procédé d'échanges de données entre des entités
FR3090976A1 (fr) * 2018-12-21 2020-06-26 Thales Procédé d’échanges de données entre des entités
EP4290497A1 (fr) * 2022-06-10 2023-12-13 Furuno Electric Co., Ltd. Appareil de planification de l'itinéraire de navigation et procédé de planification de l'itinéraire de navigation
CN115410419B (zh) * 2022-08-23 2024-02-02 交通运输部天津水运工程科学研究所 一种船舶系泊预警方法、系统、电子设备及存储介质
CN115410419A (zh) * 2022-08-23 2022-11-29 交通运输部天津水运工程科学研究所 一种船舶系泊预警方法、系统、电子设备及存储介质
CN116343453B (zh) * 2023-05-26 2023-07-25 广州海洋地质调查局三亚南海地质研究所 基于大数据的船舶定位信息监测与预警方法及相关设备
CN116610125A (zh) * 2023-05-26 2023-08-18 北鲲睿航科技(上海)有限公司 一种用于智能船舶主动防撞系统的避碰方法及系统
CN116343453A (zh) * 2023-05-26 2023-06-27 广州海洋地质调查局三亚南海地质研究所 基于大数据的船舶定位信息监测与预警方法及相关设备
CN116610125B (zh) * 2023-05-26 2024-01-30 北鲲睿航科技(上海)有限公司 一种用于智能船舶主动防撞系统的避碰方法及系统

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