Path Planning in the Case of Swarm Unmanned Surface Vehicles for Visiting Multiple Targets

In this study, we present a hybrid approach of Ant Colony Optimization algorithm (ACO) with fuzzy logic and clustering methods to solve multiobjective path planning problems in the case swarm Unmanned Surface Vehicles (USVs). This study aims further explore performance ACO by integrating order cope multiple contradicting objectives generate quality solutions in-parallel identifying mission areas each USV reach desired targets. The design operational for is performed comparative evaluation three popular algorithms: Mini Batch K-Means, Ward Clustering Birch. Following identification areas, perform operation based on minimization traveled distance energy consumption, as well maximization smoothness. To problem, conducted among inference systems, Mamdani (ACO-Mamdani) Takagi–Sugeno–Kang (ACO-TSK). results show that depending needs application, methodology can contribute, respectively. ACO-Mamdani generates better paths, but ACO-TSK presents higher computation efficiency.