Chapter 27
Study on Auto Decision-Making and Its Simulation Control for Vessel Collision Avoidance Lina Li, Guoquan Chen and Guoding Li
Abstract A general Personifying Intelligent Decision-making for Vessel Collision Avoidance (short for PIDVCA) algorithm for simulating the ordinary practice and fine seamanship of the navigator emphatically discussed after two typical examples under the multi-ship encountering situation for marine radar simulation training are explained, and its core PIDVCA mathematical model are derived, the typical simulation examples based on the ship intelligent handling control simulation (shot for SIHCS) platform are analyzed and the predicted result is acquired.
Keywords Multi-ship encountering situation PIDVCA mathematical model PIDVCA algorithm Ordinary practice of navigator Simulation example
27.1 Introduction For the fact that more than 90 % of collisions are caused by human factors [1], it is necessary to develop the vessel intelligent collision avoidance navigator (short for VICAN) [2] with the function of intelligent decision-making for vessel collision avoidance. To simulate for experienced seamen abiding to the International Regulations for Preventing Collisions at Sea (short for COLREGS below) and the ordinary practice of them in taking action to avoid ship collision is meaningful. To solve this problem, first, a PIDVCA method and its evaluation criteria have to be constructed to ensure the rationality and effectiveness of the anti-collision decisionmaking scheme. Second, various kinds of PIDVCA mathematical models have to be established as a basis for the quantitative analysis of PIDVCA method. In addition, to complete the simulation of the fine seamanship and logic thinking of experienced seamen, we also need to design a series of PIDVCA algorithms to L. Li (&) G. Chen G. Li Institute of Navigation of Jimei University, No. 1 JiaGeng Road, Jimei District, Xiamei, China e-mail:
[email protected] © Springer-Verlag Berlin Heidelberg 2015 Z. Deng and H. Li (eds.), Proceedings of the 2015 Chinese Intelligent Automation Conference, Lecture Notes in Electrical Engineering 338, DOI 10.1007/978-3-662-46466-3_27
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realize the organic integration between qualitative and quantitative analysis. In 20-year’ theoretical research, the theory of PIDVCA has been formed [3–8]. In this paper, the general PIDVCA algorithm for simulating the ordinary practice and fine seamanship of excellent seamen under the multi-ship encountering situation emphatically is discussed, and the core PIDVCA mathematical model is derived from geometric analysis and the PIDVCA algorithm is verified by analysis of some simulation examples.
27.2 Enlightenment Received from the Training Example of Marine Radar Simulator 27.2.1 Description of Two Typical Examples The decision making for collision avoidance under the multi-ship meeting situation (simply called for multi-ship anti-collision) is hard for navigators. Therefore, the multi-ship anti-collision training is the main content in the marine radar simulator. The training practice is usually set to poor visibility in the open sea and the minimum DCPA is set to 2 nm according to the COLREGS. The geometry analysis of the two typical multi-vessel meeting situation instance used in radar simulator is shown in Fig. 27.1a, b, here the Co represent the course of own ship (short for OS). They are used to checkout whether the trainees hold the suitable opportune to take action and recovery original course and the altering course (short for AC) of own ship (short for OS). The target ships (short for TS) 1, 2, and 3 are shown in the radar display screen currently and C1, C2, and C3 are the corresponding position points, respectively. The TS1 and TS2 with OS exist potential collision risk, while the TS3 has none risk with OS. If the OS is taken the collision avoidance scheme by turning
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Fig. 27.1 Geometric analysis for the multi-vessel anti-collision instances
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course to starboard at once which will result in that the TS3 would pass from OS within the minDCPA. Whether the collision risk would really exist between OS and TS3 after OS being taken action? If so, how the trainer plans the new anti-collision scheme based on the TS3?
27.2.2 What Action Being Taken? For the students in the school, most of whom selected the most dangerous TS1 and take anti-collision action by the COLREGS of two vessel meeting situation and the AC of OS was determined by the ARPA’s trialling function of Radar. As shown in the Fig. 27.1a, b, when the heading of OS is altered AC to starboard at B3A3′ though both of the TS1 and TS2 can be avoided at the same time, the NRML3 (relative motion line) of the TS3 would intrude into the minDCPA circle which is a potential danger to OS. So the AC of OS is increased to B3A3″ until the NRML3′ (new relative motion line) of TS3 passed out of the minDCPA circle. These kinds of the anti-collision schemes can be surely safe but not be the most reasonable and economical. However, for the same problem to the experienced captain, on the opportunity of steer rudder (short for Tsr) under the AC of OS, whether or not in potential collision risk for the TS, they made different judgment and action from the students in school. They turned OS’s heading starboard AC1 when the TS3 passed from own ship’s abeam, as shown in the Fig. 27.1a, and turned AC starboard to B1A′ immediately but not AC1 to B3A″ in the Fig. 27.1b, according to the relative speed and position of the TS3 with their intuition and experience.
27.3 PIDVCA Mathematical Models One of the key techniques is the establishment of PIDVCA mathematical models that the “personification” intelligence for machine is realized. PIDVCA mathematical models are used for quantizing some important notion that is involved in COLREGS and proposed by PIDVCA theory and the dynamic anti-collision information for obtaining real time. They are Included various anti-collision parameters (such as the TS movement factor, collision parameter and PIDVCA scheme) computing model based on analytic geometry, judgment value estimating model, the TS encountering character model, the TS encountering attribute model, and the dynamic optimizing object function. They are present by mathematical equation [5]. C0 and V0 in Fig. 27.2 are represented the ship’s course and speed, Vt and Vr are stand for the speed and relative movement speed of TS; RML and NRML represent, respectively, the relative movement line before and after the course of OS is altered,
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Fig. 27.2 Geometric sketch for OS being altered AC and its restoring confine point
NRML′ is the parallel lines for NRML. SDA is stand for Safe Distance of Approach, and also the threshold for the potential risk of collision judgment.
27.3.1 Quantizing Model of PIDVCA Scheme PIDVCA scheme includes the opportunity of initial steering rudder (stand for Tisr), amplitude of altering course (stand for AC) and predicted restore point (stand for Rp) or opportunity (stand for Tr) of collision avoidance. Tisr is calculated by the same method as the last opportunity of steering rudder (Tln) [4]. If the Tisr has been missed, AC computing model is derived from the speed vector triangle A1′ B1 C1 as shown in Fig. 27.2: AC = Crn[nasin
Vt½n sin(Crn[nCt½n þ C0 Þ 180=p 180 V0
ð27:1Þ
Tr is the predicted time of dangerous TS1 sailing from ACp1ðxp1; yp1Þ to Rpðxr ; yr Þ, the predicted restoring point Rpðxr ; yr Þ is the crossover point of NRML1′ and RML1′ as shown Fig. 27.2. Obviously the computing model of Tr is as following: RML10 : y½n ¼ x½n cot Cr½n þ SDA½n=sin Cr½n
ð27:2Þ
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NRML10 : y½n ¼ x½n cot Crn½n þ SDA½n=sin Crn½n
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ð27:3Þ
Solving the crossover point above RML1 and NRML1′, then Rp(xr, yr) is:
SDA[n SDA[n 1 1 xr ¼ sin(Crn[nÞ sin(Cr[nÞ tan(Cr[nÞ tan(Crn[nÞ xr SDA[n þ yr ¼ tan(Cr[nÞ sin(Cr[nÞ
ð27:4Þ
In formula (27.4), Cr and Crn are not included with the 0, 90°, 180° and 270°. Then, the computing model of Tr is:
Tr[n ¼
qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ðXp½n xr Þ2 þ ðtextYp½n yr Þ2 60 Vrn[n
ð27:5Þ
Here, n is the number of the most dangerous TS.
27.3.2 PIDVCA Scheme’s Verifying Models Under Multi-vessel Encountering Situation The verifying models of PIDVCA scheme are composed the computing models of predicting TS’s parameters and restoring confine time (short for Tc) for new dangerous TS. As shown in Fig. 27.2, Tc is the sailing time that new dangerous TS2 from ACp2 (xp2, yp2) (altering course point) to RCp (xc, yc) (restoring confine point), and its computing model method is the same as the Tr. Firstly solving the crossover point of RML2′ and NRML2, and RCp (xc, yc) as following:
CPAn[i SDA[i 1 1 þ xc ¼ sin(Crn[iÞ sin(Cr[iÞ tan(Cr[iÞ tan(Crn[iÞ xc CPAn[i þ yc ¼ tan(Crn[iÞ sin(Crn[iÞ
ð27:6Þ
Then, the Tc is computed following:
Tc[i¼
qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ðXp½i xc Þ2 þ ðYp½i yc Þ2 60 Vrn[i
ð27:7Þ
In formula (27.6), Cr and Crn are not included with the 0, 90°, 180°and 270°.
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27.4 PIDVCA Algorithms 27.4.1 Composition of PIDVCA Algorithms The PIDVCA algorithm is used to realize the combination of the qualitative analysis based on “COLREGS” and quantitative calculation processing of the ordinary practice and fine seamanship of seamen, in order to obtain real-time dynamic collision avoidance knowledge. In addition, it is described by flowcharts, decision trees, decision tables or mathematical equations. The algorithm is divided into two part, they are initial PIDVCA scheme generation algorithm and PIDVCA scheme verification and optimization algorithm. The initial PIDVCA generation algorithm is constituted by TS parameter solving algorithm, TS rendezvous character identification algorithm, TS potential risk analysis algorithm, dynamic risk evaluation algorithm (for multi-vessel), the encountering attribute recognition algorithm of potentially dangerous TS and the anti-collision attribute recognition algorithm for OS with the TS, the analysis and classification algorithm for potentially dangerous TS encountering situation and PIDVCA scheme generating algorithm. The PIDVCA verification and optimization algorithm is consisted of forecasting TS potential risk analysis algorithm, generic algorithm for simulating the ordinary practice of experienced seamen, space searching optimization algorithm, time space searching optimization algorithm, coordination anti-collision optimization algorithm, dynamic optimization of local PIDVCA scheme algorithm, PIDVCA scheme generation algorithm and decision-making algorithm in immediately dangerous situation.
27.4.2 Generic Algorithm for Simulating the Ordinary Practice of Navigator Based on the anti-collision methods of the experienced captain and the chief mate taken in the two typical examples, the flowchart of PIDVCA generic algorithm for simulating the ordinary practice and fine seamanship of navigator is designed as shown in the virtual box of Fig. 27.3. The Trr, Tcc and L-PAE in Fig. 27.3 are separately stand for the value of max{Tr[i]}, min{Tc[i]} and level of predicted anticollision effect.
27.5 Simulation Control Test In order to ensure PIDVCA schemes to be reasonable, effective, and optimized, this method is embedded in the PIDVCA theoretical models [4], the intelligent code in the process knowledge as the carrier is compiled into an executable PIDVCA program files, guiding the PIDVCA formation.
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Initial PIDVCA scheme
If all TS been avoided clearly
TCPAm=max{TCPA[i] }for new risk TSs
Determine PIDVCA scheme by TCPAm, and forecast TS potential risk analysis after OS be taking action.
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