Abstract
Cooperative encirclement using multiple unmanned underwater vehicles (UUVs) is a critical task in underwater base defense, yet existing approaches typically rely on static risk rules that cannot fuse multi-source information dynamically and decouple coordination-point allocation from live risk assessment, limiting adaptive response in evolving adversarial conditions. To address these limitations, this paper proposes a continuous risk-driven adaptive weighting strategy for coordination-point allocation, which dynamically adjusts evaluation metric weights as a smooth function of real-time Bayesian risk intensity, enabling seamless transitions between time-efficiency-oriented and synchronization-oriented encirclement modes without discrete switching. This strategy is embedded within a closed-loop decision framework that integrates a Bayesian network for dynamic risk quantification and an online corner-based deployment model to guarantee continuous execution feasibility from approach to final task completion. Simulations validate that the proposed framework ensures stable, adaptive encirclement as risk levels evolve dynamically throughout the mission. Compared to the greedy priority-based allocation and the time-balance allocation as conventional baselines, the proposed strategy reduces task completion time by up to 22.8% and 9.5%, respectively, demonstrating strong robustness in dynamic, uncertain environments.