A Decision-Making Method for Ship Collision Avoidance Based on Improved Cultural Particle Swarm

Abstract

In the process of ship collision avoidance decision making, steering collision avoidance is the most frequently adopted collision avoidance method. In order to obtain an effective and reasonable steering angle, this paper proposes a decision-making method for ship collision avoidance based on improved cultural particle swarm. Firstly, the ship steering angle direction is to be determined. In this stage, the Kalman filter is used to predict the ship’s trajectory. According to the prediction parameters, the collision risk index of the ship is calculated and the situation with the most dangerous ship is judged. Then, the steering angle direction of the ship is determined by considering the Convention on the International Regulations for Preventing Collisions at Sea (COLREGs). Secondly, the ship steering angle is to be calculated. In this stage, the cultural particle swarm optimization algorithm is improved by introducing the index of population premature convergence degree to adaptively adjust the inertia weight of the cultural particle swarm so as to avoid the algorithm falling into premature convergence state. The improved cultural particle swarm optimization algorithm is used to find the optimal steering angle within the range of the steering angle direction. Compared with other evolutionary algorithms, the improved cultural particle swarm optimization algorithm has better global convergence. The convergence speed and stability are also significantly improved. Thirdly, the ship steering angle direction decision method in the first stage and the ship steering angle decision method in the second stage are integrated into the electronic chart platform to verify the effectiveness of the decision-making method of ship collision avoidance presented in this paper. Results show that the proposed approach can automatically realize collision avoidance from all other ships and it has an important practical application value.

Step-by-Step Multi-Ship Collision Avoidance Navigation Path Planning Based on Particle Swarm Optimization

“…e Maritime Safety Committee (MSC) of the International Maritime Organization (IMO) proposed a new work plan on maritime autonomous surface ships (MASSs) and rapidly promoted it [1]. MASS has become a research hotspot in the international maritime eld or will become the development direction of shipping in the future.…”

An Optimized Path Planning Method for Coastal Ships Based on Improved DDPG and DP

“…e target network will not directly copy the parameters of the main network but will update the parameters in a soft update mode; that is, the parameters are only updated a little bit each time. e update is shown in formula (9):…”

“…e parameter ReLU is the activation function used in this paper. We refer to the selection of the literature [9] and literature [29] for the two parameters update factor τ and explore decay rate.…”

“…Traditional path planning methods require complete environmental information as a priori knowledge, but it is quite difficult to obtain the surrounding environmental information in the unknown marine environment. Bionic intelligence algorithms regard the path planning problem as an optimal path problem, using the path distance as a constraint and the collision hazard and range loss as objective functions [9]. However, this kind of method is particularly prone to local optimality, and the solution is computationally intensive and requires very high system performance.…”

An Optimized Collision Avoidance Decision-Making System for Autonomous Ships under Human-Machine Cooperation Situations

“…utilised the deep reinforcement learning method to solve the collision avoidance problem under multiship encounter situations in restricted waters; Hu et al [20]. developed a multiobjective optimization model for COLREGS-compliant path planning; improved cultural particle swarm was introduced into ship collision avoidance decision [21]. To support multiple ships anticollision decision, CAS based on multiagent was designed, in which the agents formulate collision-free strategy through information interaction [22], such as distributed algorithm [23,24] and centralized algorithm [25].…”