Existing studies on multi-UAV inspection methods largely overlook path overlap avoidance among UAVs and differentiated quality control of inspection tasks.As a result,such systems are unable to ensure both cooperative...Existing studies on multi-UAV inspection methods largely overlook path overlap avoidance among UAVs and differentiated quality control of inspection tasks.As a result,such systems are unable to ensure both cooperative efficiency and the inspection quality of critical areas in practical operations.To address these limitations,this paper proposes a framework that integrates a path overlap avoidance mechanism with a task importance grading strategy.The framework adopts a hybrid decision-making architecture combining centralized task allocation with distributed reinforcement learning control.Specifically,a central controller performs global task assignment based on overall task importance,real-time UAV status,and available bandwidth.Meanwhile,individual Unmanned Aerial Vehicles(UAVs)employ reinforcement learning to autonomously conduct local trajectory planning and imaging decisions.Through the path overlap avoidance mechanism,redundant inspection of the same area is effectively prevented,while bandwidth is dynamically requested or released according to task importance.Simulation results demonstrate that,compared with existing approaches,the proposed method improves inspection efficiency by approximately 24%and enhances overall inspection quality by 20%.展开更多
文摘Existing studies on multi-UAV inspection methods largely overlook path overlap avoidance among UAVs and differentiated quality control of inspection tasks.As a result,such systems are unable to ensure both cooperative efficiency and the inspection quality of critical areas in practical operations.To address these limitations,this paper proposes a framework that integrates a path overlap avoidance mechanism with a task importance grading strategy.The framework adopts a hybrid decision-making architecture combining centralized task allocation with distributed reinforcement learning control.Specifically,a central controller performs global task assignment based on overall task importance,real-time UAV status,and available bandwidth.Meanwhile,individual Unmanned Aerial Vehicles(UAVs)employ reinforcement learning to autonomously conduct local trajectory planning and imaging decisions.Through the path overlap avoidance mechanism,redundant inspection of the same area is effectively prevented,while bandwidth is dynamically requested or released according to task importance.Simulation results demonstrate that,compared with existing approaches,the proposed method improves inspection efficiency by approximately 24%and enhances overall inspection quality by 20%.
