LOW-NOISE FLIGHT ROUTING PLANNING OF DRONES BASED ON A VIRTUAL FLIGHT NOISE SIMULATOR

The last-mile delivery based on drones can cause severe noise pollution. For each individual vehicle, the noise emission feature is dependent on the operation modes, and the perceived noise levels at given observers vary with the flights. The environmental noise impact is further complicated if there are multiple vehicles to achieve the given tasks. In this work, we propose an optimization method for drone scheduling in a vehicle routing problem to reduce the en-route sound exposure of drones. A three-step hybrid method is employed, including cluster construction, cluster adjustment, and route establishment. The customers at different positions are firstly classified into clusters according to their relative positions. The clusters are then modified according to the noise characteristics and assigned to different drones to conduct delivery tasks. Finally, the visiting sequence is optimized for each drone according to the noise exposure level. The core task of flight path optimization is the efficient estimation of the drone noise distribution in the community environment using an established virtual flight noise simulation platform. The computed sound distributions are employed to determine the cost function for optimization. In this work, the strategy is applied to the delivery tasks in a representative urban community. Results show that this approach can effectively reduce the accumulated noise exposure for mass delivery tasks. This study provides a promising approach for low-noise multi-drone operation management in future green smart cities.