Multi-objective collaborative path planning for multiple water-air unmanned vehicles in cramped environments

Water-air amphibious unmanned vehicle (WAAUV) systems are highly adaptable to complex and confined workspaces, offering tremendous potential for tasks such as search and rescue. However, planning safe and efficient cooperative paths for multiple WAAUVs in crowded environments remains challenging due to trajectory conflicts associated with high-density navigation and increased collision risks under space constraints. This paper proposes an improved multitasking-constrained multi-objective optimization (IMTCMO) for the collaborative path planning problem. The algorithm employs a multitasking coevolutionary framework with dynamic constraint relaxation and hybrid differential evolution operators. It optimizes main and auxiliary tasks simultaneously, balancing global exploration and local exploitation. A multi-vortex superposition model is employed to quantify environmental disturbances, and a model for WAAUV path planning is constructed, incorporating objectives for task collaboration efficiency, threat risk cost, and energy consumption. In addition, an adaptive coding strategy is designed to improve solution quality. Experiments in six complex scenarios show that IMTCMO outperforms seven advanced algorithms in convergence, diversity, and robustness, improving average hypervolume by 1.59 %. Even in multi-threat areas with complex fluid dynamic interference, IMTCMO can still generate efficient, safe, and low-energy cooperative paths.