Abstract
Collision risk identification is necessary for autonomous ships to recognize the risk when facing different encounter situations and make the proper collision avoidance decision. For achieving quantitative assessment of collision risk, this paper proposed a collision risk identification method based on field theory. Specifically, the method first combines the uncertainty model of ship position prediction to improve the quaternion ship domain (QSD). Then, based on the field theory and the situation of encounters, the ship risk field is constructed, which transform the ship domain overlapping index into the field energy superposition problem. The validation and superiority of this approach is examined by simulation studies. The results demonstrate that the proposed method can overcome some deficiencies of the conventional risk assessment methods. Moreover, the approach can effectively identify the collision risk in real time and provide a reference on furthering enhance the navigational safety for the autonomous ships. Therefore, the approach gives it a significant potential for use in collision risk identification in the future.
Highlights
With the increasing demand of global commodity trade, water transport, as a more economical means of bulk cargo transport, has developed rapidly [1]
TWO SHIPS ENCOUNTER SITUATION 1) HEAD-ON SITUATION In the situation shown in figure 7(a), the initial position of ‘‘YUKUN’’ and ‘‘YUPENG’’ are (0,0) and (−0.6, 9.5) respectively, the ‘‘YUKUN’’ meets a head-on encounter situation with ‘‘YUPENG’’ from the front such that they are meeting on nearly reciprocal courses
The red curve represents the change of collision risk index (CRI) based on the Space Collision Risk (SCR) approach, the green curve shows the change of CRI based on the Fuzzy Mathematical method and the blue curve illustrates the change of CRI based on the Field Theory approach
Summary
With the increasing demand of global commodity trade, water transport, as a more economical means of bulk cargo transport, has developed rapidly [1]. Maritime traffic is becoming busier [2]. According to the Annual Overview of Marine Casualties and Incidents 2019 released by EMSA [3], the navigational casualties represent more than (54.4%) of the casualty events, with collisions (26.2%), contacts (15.3%) and grounding/stranding (12.9%). It is generally recognized that human error contributes to more than 80% of ship collision accidents [4], [5]. In order to mitigate the navigational casualties due to human factors and further increase the level of intelligence in marine transportation, the development of ship automation and Maritime Autonomous Surface Ships (MASS) have become a popular research topic. The collision risk identification is of great significance at the same time
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