Smart Assessment of Flight Quality for Trajectory Planning in Internet of Flying Things

With the expanding applications of unmanned aerial vehicles (UAVs), precise flight evaluation has emerged as a critical enabler for efficient path planning, directly impacting operational performance and safety. Traditional path planning algorithms typically combine Dubins curves with local optimization to minimize trajectory length under 3D spatial constraints. However, these methods often overlook the correlation between pilot control quality and UAV flight dynamics, limiting their adaptability in complex scenarios. In this paper, we propose an intelligent flight evaluation model specifically designed to enhance multi-waypoint trajectory optimization algorithms. Our model leverages a decision tree to integrate attitude parameters and trajectory matching metrics, establishing a quantitative link between pilot control quality and UAV flight states. Experimental results demonstrate that the proposed model not only accurately assesses pilot performance across diverse skill levels but also improves the optimality of generated trajectories. When integrated with our path planning algorithm, it efficiently produces optimal trajectories while strictly adhering to UAV flight constraints. This integrated framework highlights significant potential for real-time UAV training, performance assessment, and adaptive mission planning applications.