Abstract
Dynamic collision avoidance between multiple vessels is a task full of challenges for unmanned surface vehicle (USV) movement, which has high requirements on real-time performance and safety. The difficulty of multi-obstacle collision avoidance is that it is hard to formulate the optimal obstacle avoidance strategy when encountering more than one obstacle threat at the same time; a good strategy to avoid one obstacle sometimes leads to threats from other obstacles. This paper presents a dynamic collision avoidance algorithm for USVs based on rolling obstacle classification and fuzzy rules. Firstly, potential collision probabilities between a USV and obstacles are calculated based on the time to the closest point of approach (TCPA). All obstacles are given different priorities based on potential collision probability, and the most urgent and secondary urgent ones will then be dynamically determined. Based on the velocity obstacle algorithm, four possible actions are defined to determine the basic domain in the collision avoidance strategy. After that, the Safety of Avoidance Strategy and Feasibility of Strategy Adjustment are calculated to determine the additional domain based on fuzzy rules. Fuzzy rules are used here to comprehensively consider the situation composed of multiple motion obstacles and the USV. Within the limited range of the basic domain and the additional domain, the optimal collision avoidance parameters of the USV can be calculated by the particle swarm optimization (PSO) algorithm. The PSO algorithm utilizes both the characteristic of pursuance for the population optimal and the characteristic of exploration for the individual optimal to avoid falling into the local optimal solution. Finally, numerical simulations are performed to certify the validity of the proposed method in complex traffic scenarios. The results illustrated that the proposed method could provide efficient collision avoidance actions.
Highlights
A ship outside the ter distance which equals to the domain radius of the obstacle in this article, and d identification zone of the unmanned surface vehicle (USV) is defined as an irrelevant obstacle invisible to the USV, such distance between and obstacle when the USV
(3) In each cycle, any optimal solution obtained by the particle swarm optimization (PSO) algorithm may be changed to the limit values of the USV according to Equations (12) and (13) due to the limitations of its manipulating capability
The new most urgent obstacle (MUO) and secondary urgent obstacle (SUO) may be other obstacles that were not considered in the previous calculation period, while the threats they pose to the USV continue to increase in the following period
Summary
A variety of representative methods have been designed to implement dynamic collision avoidance for USVs, including artificial potential field, neural network, and velocity obstacle approaches. In the case of multi-obstacle dynamic collision avoidance, the feasible space is small and changes in real-time, requiring higher accuracy and real-time performance This method is of limited use in practical maritime navigation. In combination with the VO algorithm and fuzzy theory, avoidance strategies of the two obstacles are optimized to reduce the collision risk of the obstacles. Through this unified strategy and rolling mechanism, all obstacles are gradually considered over time as the USV moves.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
