动态时空约束下多无人设备协同路径规划方法研究OA

To address the challenges in dynamic ocean monitoring missions,such as complex spatiotemporal constraints,time-varying en-vironments,and limited device resources,we propose a cooperative path planning method for multiple unmanned devices under dynamic spatiotemporal constraints.The approach establishes a heterogeneous cooperative system model,which uses an unmanned surface vehicle(USV)as a mobile base station and multiple unmanned aerial vehicles(UAVs)as multi-source sensing nodes.Task decomposition and dynamic scheduling are achieved through task partitioning and energy consumption modeling.To overcome the limitations of traditional genetic algorithms,such as easily falling into local optima and slow convergence in dynamic environments,an improved genetic algorithm based on dynamic spatiotemporal constraints(IGA-D)is introduced.The proposed algorithm incorporates a spatiotemporal constraint encoding strategy,an adaptive mechanism for dynamically adjusting crossover and mutation probabilities,and a periodic variable neighborhood search(VNS)module,thereby enhancing both global search capability and responsiveness to dynamic conditions.Simulation experiments were conducted in a scenario simulating the dynamic diffusion of pollutants in the Zhoushan sea area.It is dem-onstrated that the proposed algorithm significantly outperforms improved DWA,PSO-GA,and conventional algorithms in key performance metrics,including path length,mission completion time,load balancing,and dynamic response speed,with high stability.It is thus well-suited for cooperative detection tasks in complex and dynamic environments.