Wind dynamic and energy-efficiency path planning for unmanned aerial vehicles in the lower-level airspace and urban air mobility context

• Published in: Sustainable energy technologies and assessments 2023-06, Vol.57, p.103202, Article 103202
• Main Authors: Chan, Y.Y., Ng, Kam K.H., Lee, C.K.M., Hsu, Li-Ta, Keung, K.L.
• Format: Article
• Language: English
• Subjects: Dynamic wind turbulence and disturbance; Energy efficiency; Path planning; Unmanned aerial vehicle; Urban air mobility; Wind dynamic
• ISSN: 2213-1388
• DOI: 10.1016/j.seta.2023.103202

•An energy-efficient path planning model for UAVs is proposed under wind dynamics.•Voronoi Diagram is used to decompose the urban environment into a network model.•The proposed model could predetermine the route for the airmobile for UAM.•The model achieved a 5% to 22% improvement in different wind scenarios. Unmanned aerial vehicles (UAVs) have been extensively used in urban environments for logistics, parcel delivery and surveillance, and the development of air taxi services. Given the dynamic nature of urban air mobility in terms of decision-making time limit, wind dynamics and other external factors, one should consider their safe and efficient operations in an urban context. Therefore, we propose an energy-efficient path-planning model for UAVs under large and complex urban environments and wind dynamics. The proposed method adopted Voronoi Diagram to decompose the complex urban environment into a simplified network model, given the presence of no-fly zones and restricted areas as obstacles. One could obtain the feasible initial path by solving the network model using the Dijkstra shortest path algorithm concerning the distance matrix. Given the nonlinearity of energy consumption along a path. We further model the UAV energy consumption and propose an efficient particle swarm optimisation (PSO) metaheuristic algorithm to achieve better solution quality. Compared to traditional PSO, the proposed algorithm achieved a 5% to 22% improvement under different wind scenarios by simulating real-life situations. In conclusion, the proposed method can achieve safe UAV operations with sufficient separation and less energy consumption.