Optimization of 4D path planning for urban UAV logistics considering nonlinear energy consumption and dynamic wind fields

Path planning of unmanned aerial vehicles (UAVs) is crucial for urban logistics delivery. Current studies typically focus on path planning based on the assumptions of linear energy consumption with distance and a static urban wind environment. Therefore, this study proposes a 4D path planning optimization strategy that incorporates dynamic wind fields to achieve energy-efficient UAV delivery. An urban dynamic wind field model based on a Markov-based state transition approach is developed to describe the spatiotemporal evolution of wind velocity. A mixed-integer nonlinear programming model is then established to solve the energy-efficient 4D path planning problem under dynamic wind conditions, and the model is solved using a developed bilevel collaborative algorithm. Numerical experiments on multiple scales are presented to illustrate the effectiveness of the model and the feasibility of the algorithm. A case study of the Lujiazui district in Shanghai demonstrates that the proposed strategy achieves energy consumption reductions of 12.25%, 8.56%, and 6.25% compared to the shortest path strategy under initial east wind speeds of 4, 8, and 12 m/s. Management recommendations are provided to promote energy-efficient operation.