Advanced function of the computer-based river traffic management system should automatically predict and prevent possible conflict and deadlock states between vessels by using adequate control policy (supervisor). This paper proposes a formal method for calculating maximally permissive deadlock prevention supervisor. To model the river system, the authors use a class of Petri net suitable for describing multiple re-entrant flowlines with disjoint sets of resources, jobs and control places, and matrix-based formal method to analyze the system. By using matrix algebra, the structural characteristics of the Petri net (circular waits, P-invariants, critical siphons and subsystem, key resource) have been analyzed and the steps for supervisor design proposed. The first and the second level deadlocks can be avoided by maintaining the number of tokens in the critical subsystems and ensuring that the key resource would not be the last available resource in the system. The derived supervisor has been verified by a computer simulation using MATLAB environment. KEYWORDS: traffic management system, deadlock avoidance, discrete event system, Petri net
This paper deals with the automatic traffic control of vessels moving through the marine canal traffic system. Dangerous vessel deadlock situations may occur in case of vessels' irregular moving through the system. To avoid this, the vessel traffic is supervised and controlled by traffic lights. Derived supervisor is maximally permissive (responsible for vessels' stopping only in the case of dangerous situation and until this situation elapses). This paper shows a formal method of calculating such supervisor by using Petri net. To ensure deadlock free operation of supervisor, the paper proposes finding and controlling critical minimal siphons (specific set of places in the Petri net which are responsible for deadlock). The supervisor is verified using computer simulation.
Automatic Identification Systems (AIS) and Automatic Radar Plotting Aids (ARPA) are commonly used to detect targets for collision avoidance. However, AIS cannot detect targets without AIS transmitters and ARPA has limitations due to blind sector and small targets may not be detected. Advances in computer performance and video-based detection generated much interest in developing intelligent video surveillance systems to achieve autonomous navigation. To develop a reliable collision avoidance system, we propose the use of a visual camera for real-time object detection and target tracking. Moreover, the system should follow the International Regulations for Preventing Collisions at Sea (COLREGs) to avoid catastrophic accidents. In this paper only a part of the system is presented. For real-time object detection, the You Only Look Once (YOLO) ver. 3 convolutional neural network is used, and the target tracking filter based on a Kalman filter with built-in estimated relative position and velocity.
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