Planning effective collision avoidance routes is a crucial measure for ensuring ship safety. However, position uncertainty caused by sensor noise, communication delays, and sudden changes in the maneuvering of target vessels severely restricts the reliability of traditional collision avoidance methods. To address this, this study integrates the velocity obstacle method and conditional value at risk theory to design a ship collision avoidance framework under position uncertainty. The position uncertainty of the target vessel is modeled using a Gaussian distribution. By fusing multi-source sensor data from radars and the Automatic Identification System through Bayesian inference, the posterior estimate of the vessel’s position is dynamically updated, thereby constructing an uncertainty velocity obstacle region. The Gaussian posterior distribution of the position is incorporated into a stochastic loss function to formulate a stochastic optimization model that balances navigation efficiency and collision risk. The model is solved using the sample mean approximation method and strictly complies with the International Regulations for Preventing Collisions at Sea. The results of two sets of multi-vessel encounter simulations demonstrate that, compared with traditional methods, the proposed method achieves superior performance in terms of total path length and algorithm runtime. It is capable of generating compliant collision avoidance strategies in complex dynamic crossing scenarios, attaining optimal comprehensive performance with respect to safety, economy, and regulatory compliance.
Wu et al. (Thu,) studied this question.