| Popis: |
Maritime autonomous surface ships (MASS) need to be sufficiently safe to gain commercial acceptance. Collision avoidance strategies in such MASS should comply with the International Regulations for Preventing Collision at the Sea (COLREGs). According to the COLREGs, collision risk assessment, which determines the optimal positioning and timing via all available means appropriate to the prevailing circumstances and conditions, is crucial for preventing collisions. However, existing collision risk assessment methods do not consider all vital factors for the COLREGs rules compliant collision avoidance. We propose a collision risk inference system for MASS that complies with COLREGs vital rules for collision avoidance as follows: 1) actions to avoid collision are defined according to the degree of danger, and a suitable response distance is determined; 2) a collision risk index according to the enlarged ship domain based on the designated response distance by each level is set; 3) all vital factors of the COLREGs rules compliant collision avoidance are extracted as the data when the ship domain enlarged by each level is overlapped; 4) the collision risk inference system is developed by learning extracted data via the adaptive neuro fuzzy inference system. In contrast to existing research, the proposed system considers all vital variables in the COLREGs rules compliant collision avoidance guidelines, thereby improving the timings and positionings of the potential collision warning. Consequently, it could secure more time for decision making to take necessary collision prevention action. |
