ADC Model-Based Collaborative Effectiveness Evaluation Method for Unmanned Surface Vehicle Clusters

To address the need for evaluating the collaborative effectiveness of unmanned surface vehicle (USV) clusters, this paper proposes an effectiveness evaluation method based on the ADC (Availability, Dependability, Capability) model. By constructing an evaluation framework for system availability, dependability, and capability, combined with the dynamic task characteristics of USV clusters, an analytical model for cluster collaborative effectiveness is established. In availability analysis, the state reliability of individual USVs and the cluster is quantified. In dependability analysis, a cluster operational state transition matrix is introduced. In capability analysis, collaborative effectiveness quantification parameters such as perception capability coefficient, decision-making capability coefficient, and communication capability are proposed, and a multi-dimensional capability characterization vector is constructed. Simulation verification in typical scenarios demonstrates that the proposed method effectively evaluates the collaborative effectiveness of USV clusters, providing data support for collaborative task planning and resource scheduling optimization.