intelligent guidance in collision avoidance of maritime transportation

Maritime Engineering and Technology – Guedes Soares et al.© 2012 Taylor & Francis Group, London, ISBN 978-0-415-62146-5

Intelligent guidance in collision avoidance of maritime transportation

Lokukaluge P. PereraCentre for Marine Technology and Engineering (CENTEC), Instituto Superior Tecnico,Technical University of Lisbon, Portugal

J.P. CarvalhoINESC-ID, Instituto Superior Tecnico, Technical University of Lisbon, Portugal

C. Guedes SoaresCentre for Marine Technology and Engineering (CENTEC), Instituto Superior Tecnico,Technical University of Lisbon, Portugal.

ABSTRACT: This paper focuses on an overview of an autonomous navigation system in maritime transporta-tion that is supplied with intelligent guidance to avoid collision, while respecting the COLREGs rules andregulations and using expert knowledge in navigation for close quarter’s manoeuvres. The proposed collisionavoidance process consists of two modules: a fuzzy logic based parallel decision making module and a Bayesiannetwork based sequential action formulation module. Successful simulation results of the intelligent guidance incollision avoidance of maritime transportation that is capable of making multiple collision avoidance decisionsand actions in order to avoid complex multi-vessel collision situations are also illustrated in this study.

1 INTRODUCTION

The intelligent guidance in maritime transportation isstill underdeveloped when compared with the landand air transportation systems. In conventional mar-itime transportation, the most important navigationfactor is still the human guidance, and wrong judg-ment and miss-operations by humans have resultedin many casualties and environmental disasters. Thereported data shows human errors are still one of themajor causes of maritime accidents (Guedes Soares, &Teixeira, 2001) and 75–96% of marine accidents andcausalities are caused by some types of human errors(Rothblum et al. 2002, and Antão & Guedes Soares,2008).

Therefore, the implementation of intelligent andautonomous capabilities and the limitation of humansubjective factors in navigation, to increase the safetyand security of ocean navigation, are proposed inthis study. Similarly, the replacement of human infer-ence by an intelligent decision formulation processresulting in feasible actions for navigation and col-lision avoidance can reduce the number of maritimeaccidents and their respective causalities.

To avoid collision situations, all vessels should fol-low the law of the sea. The current law of the sea wasformulated by the International Maritime Organiza-tion (IMO) in 1972 by the Convention on the Inter-national Regulations for Preventing Collisions at Sea(COLREGs). However, the reported data of the mar-itime collisions presented by Statheros et al. (2008),show that 56% of major maritime collisions involve

violation of the COLREGs rules and regulations. Thedetailed description of the COLREGs rule and regu-lations and their interpretation for autonomous oceannavigation with respect to the collision avoidance arepresented in Perera et al. (2010a).

This paper focuses on an overview of an intel-ligent maritime transportation system that includesan autonomous collision avoidance process whilerespecting the COLREGs rules and regulations andexpert knowledge in navigation. The autonomousguidance in collision avoidance described in this workconsists mainly of a fuzzy logic based parallel decisionmaking module whose decisions are formulated intosequential actions by a Bayesian network based mod-ule.The proposed collision avoidance process capabil-ities of making multiple parallel collision avoidancedecisions regarding several target vessels and thosedecisions are executed as sequential actions in the ves-sel navigation to avoid complex collision situations arealso presented.

2 RELATED WORK

The decision making process and strategies in inter-action situations of avoidance of collision in maritimetransportation are presented in Chauvin & Lardjane(2008). Sato & Ishii (1998) proposed combining radarand infrared imaging method to detect the targetvessel position conditions as a part of the collisionavoidance system. Lisowski et al. (2000) used neural-classifiers to support the navigator in the process

9

of determining the vessel’s domain. On a similarapproach, Pietrzykowski and Uriasz (2009) proposedthe notion of a vessel domain in a collision situation asdepending on the parameters like vessel size, courseand heading angle of the encountered vessels.

Kwik (1989) presented collision risk calculationsfor a two-ship collision encounter based on the kine-matics and dynamics of marine vessels. Yavin et al.(1995) considered the collision avoidance conditionsof a ship moving from one point to another in a nar-row zig-zag channel proposing a computational openloop command strategy for the rudder control system.Sutulo et al. (2002) studied the problem of predict-ing a ship trajectory based on simplified maneuveringmodels. An alternative approach based on neuralnetworks is also proposed by Moreira and GuedesSoares (2003). Smierzchalski and Michalewicz (2000)modeled safe ship trajectory in navigation using anevolutionary algorithm and considering static anddynamic constraints for the optimization process.

Ito et al. (1999) used genetic algorithms to searchfor safe trajectories on collision situations in oceannavigation. Similarly, experimental results on the sametopic are presented in Zeng et al. (2001). Hong et al.(1999) presented a collision free trajectory in oceannavigation based on a recursive algorithm that is for-mulated by analytical geometry and convex set theory.Similarly, Cheng et al (2006) have presented trajec-tory optimization for ship collision avoidance systembased on a genetic algorithm.

Statheros et al. (2008) give an overview of computa-tional intelligence techniques that are used in collisionavoidance in ocean navigation. Liu and Liu (2006)used Case-Based Reasoning to illustrate learning ofcollision avoidance techniques in ocean navigationusing previous recorded data of collision situations.Furthermore, an intelligent anti-collision algorithm fordifferent collision conditions is designed and testedon the computer based simulation platform by Yanget al. (2007). Zhuo and Hearn (2008) presented a studyof two vessel collision avoidance in ocean navigationusing a self learning neuro-fuzzy network based onlineand offline training scheme.

A fuzzy logic approach for collision avoidance con-ditions with the integration of a virtual force field isproposed by Lee et al. (2004). Similarly, automaticcollision avoidance facilities for ship system using afuzzy logic based controller is proposed by Hasegawa(1987). Benjamin et al. (2006) propose behavior basedcontrols formulated with interval programming forcollision avoidance of maritime transportation. Thecollision avoidance behavior is illustrated in accor-dance with the Coast Guard Collision Regulations.Benjamin and Curcio (2004) present the decision mak-ing process of ocean navigation based on an intervalprogramming model for a multi-objective decisionmaking algorithm.

A computational algorithm based on the If-Thenlogic is defined and tested under simulator condi-tions by Smeaton and Coenen (1990) regarding dif-ferent collision situations. Cockcroft and Lameijer

Figure 1. Two Vessel Collision Situation.

(2001) present a detailed description of the COLREGsrules and regulations, implementation, interpretationof judicial authorities of vessel collision and near-misssituations.

3 COLLISION AVOIDANCE SITUATIONSIN OCEAN NAVIGATION

3.1 Two vessel collision situation

A two-vessel collision situation is presented in Fig-ure 1, where the own vessel, which is the one thatis equipped with the collision avoidance system, islocated in the point O(k)(xo(k), yo(k)), at the kthtime instant. The ith target vessel, which is theone that needs to be avoided, is located at pointPi(k)(xi(k), yi(k)), with the estimated navigational tra-jectory of Pi(k)Bi(k).

The target vessel estimated trajectory Pi(k)Bi(k)will intercept the own vessel domain with the clos-est distance of RDCPA(k), around the points of Bi(k).The own vessel speed and course conditions are repre-sented by Vo(k) and ψo(k); the ith target vessel speedand course conditions are represented by Vi(k) andψi(k); the ith target vessel bearing and relative bear-ing conditions are represented by θi(k) and θi,o(k); therelative speed and course conditions of the ith targetvessel are represented by Vi,o(k) and ψi,o(k).All anglesare measured with respect to the positive Y axis.

The own vessel navigational space is divided intothree circular regions with radii Rvd , Rb and Ra. Theradius Ra represents the approximate range to the tar-get vessel detection when the own vessel is in a “Giveway” situation, i.e., when the own vessel has low prior-ity for navigation and should take appropriate actionsto avoid collision situations. The radius Rb representsthe approximate distance to the target vessel, when

10

Figure 2. Multi-Vessel Collision Situation.

the own vessel is in a ”Stand on” situation with thehigher priority for navigation should take appropriateactions to avoid collision due to absence of the appro-priate actions from the target vessel. The radius Rvdrepresents the vessel domain.

3.2 Multi-vessel collision situation

The expansion of a two vessel collision situationinto a multi-vessel collision situation is presentedin Figure 2. The own vessel is located in the pointO(k). The target vessels are located at the points ofP1(k), P2(k), . . . , Pn(k) with the navigational trajecto-ries of S1(k), S2(k), . . . , Sn(k) at the kth time instantrespectively.The own vessel trajectory S0(k) will inter-cept the trajectories S1(k), S2(k), . . . , Sn(k) around thepoints of C1(k), C2(k), . . . , Cn(k) at the time instantsof T1(k), T2(k), . . . , Tn(k) respectively.

3.3 Modules of collision avoidance system

A block diagram for the proposed Collision Avoid-ance System (CAS) is presented in the Figure 3. Thecomplete CAS consists of four modules: the VesselTracking & Trajectory Prediction (VTTP) module, theCollision RiskAssessment (CRA) module, the ParallelDecision Making (PDM) module, and the SequentialAction Formation (SAF) module.

The inputs into the VTTP module are the real-time position of the own vessel (xo(k), yo(k)), thatcan be measured or estimated by the GPS integratedInertial navigational systems, in the Cartesian coordi-nates, and the real-time position of each target vessel’s(Ri(k), θi(k)) that is measured in Polar coordinates.The Range (Ri(k)) and Bearing (θi(k)) values of theith target vessel can be obtained using Radar or Lasermeasurement systems by the kth time instant as furtherdiscussed by Perera and Guedes Soares (2010a).

The VTTP module consists of four units: the ScanUnit, the Data Classification Unit, the Clustered Data

Figure 3. Block diagram for Collision Avoidance System.

Tracking Unit and the Trajectory Prediction Unit. Theintegrated Radar or Laser measurement system is con-sidered for the Scan Unit where the real-time positiondata of the target vessels is collected. The target ves-sel’s data are used in the Data Classification Unit toidentify each vessel and the Clustered Data Track-ing Unit tracks each target vessel separately usingthis data. Finally the collected tracking data is usedto predict each target vessel’s trajectory in the Trajec-tory Prediction Unit. Due to the target vessels constantspeed and course condition assumptions, this processis simplified in this study. Further details on targetvessel tracking under ocean navigation conditions arepresented in Perera and Guedes Soares (2010b).

The main objective of the CRA module is to evalu-ate the collision risk of each target vessel with respectto the own vessel navigation. This is achieved by theRelative Trajectory Formation Unit and the CollisionTime and Point Estimation Unit. The inputs into theCRA module are the measured/estimated position dataof the own vessel and target vessels. The outputs of theCRA module are Range (Ri(k)), Bearing (θi(k)), Rela-tive course (ψi,o(k)) and Relative speed (Vi,o(k)) of ithtarget vessel. These outputs will be used as the inputsof the PDM module at the kth time instant.

In addition, the Time until the collision situationTi(k) of the ith target vessel will input into the SAFmodule from the CRA module. The PDM module con-sists of a fuzzy logic based decision making process

11

that generates parallel collision avoidance decisionswith respect to each target vessel at kth time instant.

As the next step, the parallel ith decision of collisionavoidance, Di(k), will forward from the PDM moduleto the SAF module. The main objective of the SAFmodule is to organize the parallel decisions made bythe PDM module into sequential actions, Ai(k), con-sidering the Time until the collision situation, Ti(k),that will be executed on the own vessel navigation.These actions are further divided into two categoriesof Course and Speed control actions that will be imple-mented on the propeller and rudder control systems ofthe own vessel.

4 PARALLEL DECISION MAKING MODULE

The main objective of the PDM module is to make thecollision avoidance decisions considering the collisionrisk warnings.An overview of the PDM module is alsopresented in Figure 3. The module consists of 3 mainunits: the Fuzzification Unit, the Fuzzy Rules Unit andthe Defuzzification Unit.

4.1 Fuzzification unit

The inputs from the CRA module, Range (Ri(k)),Bearing (θi(k)), Relative course (ψi,o(k)) and Rel-ative speed (Vi,o(k)) of the ith target vessel at thekth time instant are fuzzified in this unit withrespect to the input Fuzzy Membership Functions(FMFs): the Range FMF (Ri(k)), the Speed Ratio FMF(Vi(k)/Vo(k)), the Bearing FMF (θi(k)), and the Rela-tive Course FMF (ψi,o(k)). Then, the fuzzified resultsfrom the Fuzzification unit will be transferred to theFuzzy Rules Unit for further analysis.

4.2 Fuzzy inference and rules

A Mamdani type IF <Antecedent> THEN<Consequent> rule based system is developed andinference via Min-Max norm is considered in theFuzzy Rules Unit. The IF-THEN Fuzzy rules aredeveloped in accordance with the Knowledge Base(i.e. the COLREGs rules and regulations). Howeverthe expert navigational knowledge is also consideredin the Fuzzy rules development process. Overtaking,Head-on and Crossing, the three distinct situationsthat involve risk of collision are considered for thedevelopment of the IF-THEN Fuzzy rules.

4.3 Defuzzification

The collision avoidance decisions, Di(k), for each tar-get vessel are generated by the Defuzzification Unit.The Fuzzy inference results from the previous unitare defuzzified based on the output Course ChangeFMF and Speed change FMF to obtain the Coursechange decisions, Dδψi(k), and Speed change deci-sions, DδVi(k), that will be formulated for collisionavoidance actions in the own vessel navigation.

Figure 4. Course CAAF and Speed CAAF.

The collision avoidance decisions of Course changeand Speed change are categorized as: Course to Star-board δψo > 0; course to Port δψo < 0; no coursechange δψo = 0; increase speed δVo > 0; decreasespeed δVo < 0; no speed change δVo = 0. The defuzzi-fication process uses the centre of gravity method.The further details on the Fuzzy logic based paralleldecision making module are presented in Perera et al.(2010b).

5 SEQUENTIAL ACTION FORMATIONMODULE

5.1 Action execution in navigation

The main objective of the SAF module is to trans-form the parallel collision avoidance decisions thatare generated by the PDM module into a sequen-tial action formulation that can be executed in theown vessel. This can be achieved by collecting thePDM module multiple collision avoidance decisionsfor the kth time instant, Di(k) ≡ (Dδψi(k), DδVi(k)), andevaluating them using the Time until the collisionsituation, Ti(k), from the CRA module with respectto each target vessel.

Final results are arranged as a sequential forma-tion of actions, Ai(k) ≡ (Aδψi(k), AδVi(k)) involvingthe course and speed action at given time instants(Tδψi(k), TδVi(k)). Figures 4 gives examples of thesequential course collision avoidance action func-tion (CCAF) and the speed collision avoidance actionfunction (CCAF). Dδψi(k), DδVi(k) and Aδψi(k), AδVi(k)represent the course and speed change decisions andactions at kth time instant respectively.

The overview of the SAF module is also presentedin Figure 4. The SAF module consists of 2 units: theCourse and Speed Actions Formation Units. The mainobjective of these 2 units is to formulate the collisionavoidance course and speed control actions.

5.2 Bayesian network

The continuous Bayesian Network module that is for-mulated to update the parallel collision avoidancedecisions into the sequential actions is presented inFigure 5. The Bayesian network consists of four

12

Figure 5. Bayesian Network Structure for CollisionAvoidance.

nodes: Collision Time Estimation, Collision Risk,Actions Delay and Collision Avoidance Actions. Theinputs of the Bayesian network are the CollisionAvoid-ance Decisions, Di(k), and the Time until the collisionsituation, Ti(k), generated respectively by the PDMand CRA modules .

The main objective of the CollisionTime Estimationnode is to estimate the Time until the collision situa-tion, Ti(k), between the own vessel and each of thetarget vessels. The node Collision Risk inferences thecollision risk with respect to each target vessel con-sidering the Collision Time Estimation. The ActionsDelay node is designed to formulate the appropriatetime to take collision avoidance actions. The Colli-sion Avoidance Action node will inference by the twonodes of Actions Delay and Collision Risk and theCollision Decisions as presented in Figure 5.

5.3 Collision risk functions

The Collision Risk Functions (CRF) of the own ves-sel (�i(k)) due to the ith target vessel in the kthtime instant are modeled as a Gaussian distribu-tion �i(k) ∼ N (µ�i(k), σ2

�i(k)), with the mean valueµ�i(k) that is estimated by the average time untilcollision, Ti(k), which can be defined as:

where |OPi(k)| is the relative range and Vi,o(k) therelative speed of the ith target vessel with respect tothe own vessel at the kth time instant. Furthermore, thecovariance σ2

�i(k) is considered in this distribution.It is assumed that the CRF, �i(k), can be obtainedfrom noisy Observation Function of Zi(k) and can bewritten as:

where ωzi(k) ∼ N (0, σ2z (k)), is a Gaussian observation

noise with the mean 0 and the constant covarianceσ2

z (k). Hence the prior distribution of the CRF due tothe ith target vessel at kth time instant can be writtenas a Gaussian distribution:

where 1αi is the normalization constant. The transitionmodel of the CRF is considered as a Gaussian pertur-bation of the constant covariance σ2

� to the currentstates of the CRF and can be written as:

where 2αi is the normalization constant. The condi-tional observation model for collision risk is assumedto be a Gaussian distribution with the constant covari-ance σ2

z and can be written as:

where 3αi is the normalization constant. Consider-ing the Bayesian Rule, the CRF update from theobservations can be written:

5.4 Collision avoidance action function

The own vessel Collision Avoidance Action Func-tion (CAAF) is modeled as a Gaussian distribution�i(k) ∼ N (µ�i(k), σ2

�i(k)) with the mean µ�i(k) andthe covariance σ2

�i(k). The CAAF with respect to theCRF can be written as:

where i(k) ∼ N (µ, σ2), is the time delay function

that is approximated by a Gaussian distribution witha constant mean, µ, and covariance, σ2

. The condi-tional CRF with respect to the CAAF as a Gaussiandistribution can be written as:

where 3βi is the normalization constant. The priordistribution of the CAAF due to the ith target vesselat kth time instant can be written as a Gaussiandistribution:

where 1βi is the normalization constant. The transitionmodel of the CAAF considered as Gaussian perturba-tion of constant covariance σ2

� to the current states ofthe CAAF and can be written as:

13

where 2βi is the normalization constant. Consideringthe Bayesian Rule, the CAAF update from the CRFcan be written as:

5.5 Implementation of collision avoidance actions

The implementation of the accumulated CAAF, Ai(k)is divided into two sections of Course Control (Aδψi(k))and Speed Control (AδVi(k)) CAAFs as presented inFigures 4.The CAAFs are generated from the collisionavoidance decisions of Di(k), from the course controldecisions (Dδψi(k)) and the speed control decisions(DδVi(k)) as described previously. Hence, the accumu-lated CAAFs of Course Control (Aδψ(k)) and SpeedControl (AδV (k)) actions can be written as:

These accumulated CAAFs are implemented inthe computational simulations of collision avoidance.The further details on the Bayesian network basedsequential action formulation module includingmathematical derivation of the CRF and CAAF arepresented Perera et al. (2010c).

6 COMPUTATIONAL SIMULATIONS

The computational simulations for the multi-vesselcollision situation are presented in Figures 6 to 16.As presented in Figures 6, the own vessel starts nav-igation from the origin (0 (m), 0 (m)) and the first,second and third target vessels start from positions(4238 (m), 10238 (m)), (8790 (m), 10000 (m)), and(−10714 (m), 12400 (m)) respectively. All startup andfinal positions of the own and the target vessels arepresented by vessel shape icons at the kth time instantin the Figures.

It is assumed that the target vessels are moving inconstant speed and course and don’t honor any nav-igational rules and regulations of the sea. The CRFassessment is formulated by a Gaussian distributionand presented in the x = −12000 (m) axis. Similarly,the CAAFs for the course and speed changes formu-lated by the Gaussian distributions are presented inthe x = −9000 (m) and x = −6000 (m) axis respec-tively. The scaled Time axis (Actual Time × 5 (s)) ispresented in the y axis, and the scaled Collision Risk(%) , Course Actions (%) and Speed Actions (%) arepresented in the x axis.

In Figure 6, the collision avoidance system hasobserved the first possible collision situation and the

Figure 6. Simulations of multi-vessel Collision Situation.


accumulated CRF is presented in the x = −12000 (m)axis as the first peaks in a time instant. Furthermore,the respective accumulated CAAFs of course andspeed, with respect to the collision situation are pre-sented in the same Figure in the axis of x = −9000 (m)and x = −6000 (m). The accumulated CAAFs, thecourse to starboard and speed reduction to avoid thefirst target vessel are also presented in the same Figure.

In Figure 7, the collision avoidance system hasobserved the second possible collision situationsand the accumulated CRF is presented in thex = −12000 (m) axis as the second peak in a differenttime instant. Furthermore, the respective accumulatedCAAFs of course and speed, with respect to each colli-sion situations are presented in the same Figure in theaxis of x = −9000 (m) and x = −6000 (m). The accu-mulated CAAFs, the course to starboard and speedreduction, to avoid the second target vessel are alsopresented in the same Figure.

Figure 8 presents the sub-completion of the firstaction segment of the CAAFs, consisting in speedreduction and continuation of course change to star-board side by the own vessel. However, due the firstaction segment of the CAAFs, the partially reducedcollision actions (i.e. the speed reduction for secondcollision situation no longer required) between the own

14




vessel and the second target vessel could also observedin Figures 8 and 9.

The completion of the first action segment of theCAAFs in Figure 10 and the own vessel is aboutto safe-pass the first target vessel in Figure 11 arepresented. One should note that, due the first actionsegment of the own vessel, the collisions situation withthe second target vessel was eliminated.

The own vessel is about to safe-pass the first targetvessel trajectory in Figure 12 and about to safe-pass




the second target vessel trajectory in Figure 13 arepresented.

Furthermore, the collision avoidance systemobserving the third possible collision situation withthe respective accumulated CAAFs, the course to portand speed reduction to avoid the third target vessel arealso presented in Figure 13.

Figure 14 presents the sub-completion of the thirdaction segment of the CAAFs, consisting in speed

15




reduction and continuation of course change to portby the own vessel.

Figure 15 presents the completion of executionof the CAAFs in order to avoid the third targetvessel. Finally the completion of all the CAAFswith zero CRF and safe passing of the third targetvessel trajectory are presented in Figures 16.

7 CONCLUSIONS

This paper introduces a decisions formulation andaction execution process of collision avoidance inmaritime transportation. As presented in the computa-tional results, the autonomous Fuzzy-Bayesian baseddecision-action formulation process could be used toavoid complex navigational conditions.The successfulresults obtained in this study show the system capabil-ities of collision avoidance involving multiple vesselsunder various collision conditions in ocean naviga-tion while still respecting the COLREGs rules andregulations.

ACKNOWLEDGEMENTS

The first author has been supported by the DoctoralFellowship of the Portuguese Foundation for Scienceand Technology (Fundação para a Ciência e a Tec-nologia) under contract no. SFRH/BD/46270/2008.Furthermore, this work contributes to the project of“Methodology for ships manoeuvrability tests withself-propelled models”, which is being funded by thePortuguese Foundation for Science and Technology(Fundação para a Ciência e Tecnologia) under contractno. PTDC/TRA/74332/2006.

REFERENCES

Antão, P. ]and Guedes Soares, C. 2008, “Causal factors inaccidents of high speed craft and conventional oceangoing vessels,” Reliability Engineering and System Safety,vol. 93, pp. 1292–1304.

Benjamin, M. R., and Curcio, J. A., 2004, “COLREGS– based navigation of autonomous marine vehicles,” inIEEE/OES Autonomous Underwater Vehicles, pp. 32–39.

Benjamin, M. R., Curcio, J. A., and Newman, P. M., 2006,“Navigation of unmanned marine vehicles in accordancewith the rules of the road,” in Proceedings of the 2006IEEE International Conference on Robotics and Automa-tion, pp. 3581–3587.

Chauvin, C., and Lardjane, S. 2008, “Decision making andstrategies in an interaction situation: Collision avoidanceat sea,” Transportation Research, vol. Part F, no. 11,pp. 259–262.

Cheng, X. D., Liu, Z. Y., and Zhang, X. T., 2006, “Trajectoryoptimization for ship collision avoidance system usinggenetic algorithm,” in OCEANS – Asia Pacific, pp. 1–5.

Cockcroft, A. N., and Lameijer, J. N. F., 2001, A Guide toThe Collision Avoidance Rules. Burlington, MA. USA:Elsevier Butterworth-Heinemann.

Guedes Soares, C. and Teixeira, A. P. 2001, Risk Assessmentin Maritime Transportation. Reliability Engineering andSystem Safety, Vol. 74, pp. 299–309.

Hasegawa, K., 1987, “Automatic collision avoidance systemfor ship using fuzzy control,” in Proceedings of 8th ShipControl System Symposium, pp. 234–258.

Hong, X., Harris, C. J., and Wilson, P.A., 1999, “Autonomousship collision free trajectory navigation and controlalgorithms,” in Proceedings of 7th IEEE InternationalConference on Emerging Technologies and FactoryAutomation, vol. 2, pp. 923–929.

16

IMO 1972, “Convention on the international regulations forpreventing collisions at sea (COLREGs),”, URL http://www.imo.org/conventions/.

Ito, M., Zhang, F., and Yoshida, N., 1999, “Collisionavoidance control of ship with genetic algorithm,” inProceedings of the IEEE International Conference onControl Applications, pp. 1791–1796.

Lee, S., Kwon, K., and Joh, J., 2004, “A fuzzy logicfor autonomous navigation of marine vehicles satisfy-ing colreg guidelines,” International Journal of ControlAutomation and Systems, vol. 2, no. 2, pp. 171–181.

Lisowski, J., Rak,A., and Czechowicz, W., 2000, “Neural net-work classifier for ship domain assessment,” Mathematicsand Computers in Simulations, vol. 51, pp. 399–406.

Liu, Y., and Liu, H., 2006, “Case learning base on evaluationsystem for vessel collision avoidance,” in Proceedings ofthe Fifth International Conference on Machine Learningand Cybernetics, pp. 2064–2069.

Kwik, K. H., 1989, “Calculations of ship collision avoidancemanoeuvres : A simplified approach,” Ocean Engineer-ing, vol. 16, no. 5/6, pp. 475–491.

Perera, L. P., Carvalho, J. P., and Guedes Soares, C., 2010,Eds., Autonomous guidance and navigation based on theCOLREGs rules and regulations of collision avoidance,1st ed., Advanced Ship Design for Pollution Prevention.London, UK: Taylor and Francis Group.

Perera, L. P., Carvalho, J. P., and Guedes Soares, C., 2010,“Fuzzy-logic based parallel collisions avoidance decisionformulation for an ocean navigational system,” in Proc.8th IFAC Conference on Control Applications in MarineSystems, Rostock, Germany, pp. 295–300.

Perera, L. P., Carvalho, J. P., and Guedes Soares, C., 2010,“Bayesian network based sequential collision avoidanceaction execution for an ocean navigational system,” inProc. 8th IFAC Conference on Control Applications inMarine Systems, Rostock, Germany, pp. 301–306.

Perera, L. P. and Guedes Soares, C., 2010, “Laser mea-surement system based maneuvering target tracking for-mulated by adaptive competitive neural networks,” in InProc. 2nd International Conferrence on Adaptive andSelf-adaptive Systems andApplications, Lisbon, Portugal,pp. 84–90.

Perera, L. P., and Guedes Soares, C., 2010, “Ocean vesseltrajectory estimation and prediction based on extended

kalman filter,” in Proc. 2nd International Conferenceon Adaptive and Self-adaptive Systems and Applications,Lisbon, Portugal, pp. 14–20.

Pietrzykowski, Z., and Uriasz, J., 2009, “The ship domain – acriterion of navigational safety assessment in an open seaarea,” The Journal of Navigation, vol. 63, pp. 93–108.

Rothblum, A. M., Wheal, D., Withington, S. , Shappell,S. A. , Wiegmann, D. A., Boehm, W., and Chaderjian,M., 2002, “Key to successful incident inquiry,” in 2ndInternational Workshop on Human Factors in OffshoreOperations (HFW), pp. 1–6.

Sato, Y., and Ishii, H., 1998, “Study of a collision-avoidancesystem for ships,” Control Engineering Practice, vol. 6,pp. 1141–1149.

Smeaton, G. P., and Coenen, F. P., 1990, “Developing anintelligent marine navigation system,” Computing &Control Engineering Journal, pp. 95–103.

Smierzchalski, R., and Michalewicz, Z., 2000, “Modelingof ship trajectory in collision situations by an evolu-tionary algorithm,” IEEE Transactions on EvolutionaryComputation, vol. 4, no. 3, pp. 227–241.

Statheros, T., Howells, G., and McDonald-Maier, K. 2008,“Autonomous ship collision avoidance navigation con-cepts, technologies and techniques,” The Journal ofNavigation, vol. 61, pp. 129–142.

Sutulo, S., Moreira, L., and Guedes Soares, C., 2002,“Mathematical models of ship path prediction in manoeu-vring simulation systems,” Ocean Engineering, vol. 29,pp. 1–19.

Moreira, L., and Guedes Soares, C., 2003, “Dynamic modelof maneuverability using recursive neural networks,”Ocean Engineering, vol. 30, no. 13, pp. 1669–1697.

Yang, S., Li, L., Suo, Y., and Chen, G., 2007, “Study onconstruction of simulation platform for vessel automaticanti-collision and its test methods,” in IEEE InternationalConference on Automation and Logistics, pp. 2414–2419.

Zeng, X., Ito, M., and Shimizu, E., 2001, “Building an auto-matic control system of maneuvering ship in collisionsituation with genetic algorithms,” in Proceedings of theAmerican Control Conference, pp. 2852–2853.

Zhuo, Y., and Hearn, G. E., 2008, “A ship based intelli-gent anti-collision decision-making support system utiliz-ing trial manoeuvers,” in Chinese Control and DecisionConference, pp. 3982–3987.

17

intelligent guidance in collision avoidance of maritime transportation

Documents