###### Abstract  
Collision prevention is critical for navigational safety at sea, which has developed rapidly in the past decade and attracted a lot of attention. In this article, an improved velocity obstacle (IVO) algorithm for intelligent collision avoidance of ocean-going ships is proposed in various operating conditions, taking into count both a ship's manoeuvrability and Convention on the International Regulations for Preventing Collisions at Sea (COLREGs). An integrated model combines a three-degree-of-freedom manoeuvring model with ship propeller characteristics to provide a precise prediction of ships in various manoeuvring circumstances. In the given case, what is different to present studies, this improved algorithm allows for decision-making in two ways: altering course and changing speed. The proposed technique is demonstrated in a variety of scenarios through simulation. The findings reveal that collision-avoidance decision-making can intelligently avoid collisions with the target ships (TSs) in multi-ship situations. intelligent collision avoidance; improved velocity obstacle; COLREGs; variable speed; improved MMG +
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present, much research and technologies have been developed for the intelligent collision avoidance problem. For decades, the geometric analysis approach, which includes the Distance to the Closest Point of Approaching (DCPA) and the Time to Closest Point of Approaching (TCPA), has advised OOWs and ship captains to conduct effective collision-prevention procedures in sea practice [8]. Then, in the early 1970s the ship domain which refers to the area around a ship that prevents other ships from entering to maintain navigational safety, was introduced. Many researchers have presented multiple definitions of ship domain over the years, with varied forms and sizes [9; 10]. Some researchers evaluated the ship domain of the target ship (TS) [10], while others consider the ship domain of their ship [11]. The velocity obstacle algorithm was presented to give greater support for the OOW in collision prevention [12], which described a set of velocities to determine whether their own velocity fell into the velocity obstacle zone, hence reducing the danger of collision. As a result, a growing number of researchers were focusing on the use of velocity obstacle (VO) algorithms for collision avoidance in a variety of settings, including confined waters [13], and considering rules [14]. Furthermore, nonlinear VO based on ship motion was proposed in [15], and [PERSON] [16] adopted an improved time-discretized non-linear velocity obstacle technique for multi-ship encounter detection. With the advancement of computer technology, the study of vessel collision avoidance has progressed to a new level, and deep reinforcement learning based on COLREGs has been proposed for use in multi-ship collision avoidance [17]. Simultaneously, genetic algorithms for ship collision avoidance and path planning were made public [18; 19]. [PERSON] [20], established a ship manoeuvrability-based simulation for ship navigation in collisions, and [PERSON] [21], presented a ship manoeuvrability-based collision avoidance dynamic support system in close-quarters situations. [PERSON] adopted linear extension algorithms for a multi-ship anti-collision decision in the manner of course alternation and speed reduction [22]. To decide the path planning for autonomous ships, [PERSON] used a modified artificial potential field and a modified velocity obstacle algorithm method [23]. [PERSON] suggested a system that combines offline optimum path planning with a constrained A* algorithm which employs an artificial potential field to increase online adaptive weighting based on USV manoeuvrability reaction time [24; 25]. For the study of collision avoidance in the multi-ship situation, many algorithms have been applied in recent years. The paper [26], proposes a unique maximum-course and minimum-speed change approach for decision-making on the basis of a novel Fuzzy-logic algorithm.