Noise-Aware UAV Path Planning in Urban Environment with Reinforcement Learning

This research presents a comprehensive approach for mitigating noise pollution from Unmanned Aerial Vehicles (UAVs) in urban environment by using Reinforcement Learning (RL) for flight path planning. Focusing on the city of Turin, Italy, the study utilizes its diverse urban architecture to develop a detailed 3D occupancy grid map, and a population density map. A dynamic noise source model adjusts noise emissions based on the UAV velocity, while acoustic ray tracing simulates noise propagation in the environment. The Deep Deterministic Policy Gradient (DDPG) algorithm optimizes flight paths, minimizing the noise impact, and balancing both the path length and the population density located under the UAV path. The simulation results demonstrate significant noise reduction, suggesting scalability and adaptability for global urban environments, contributing to sustainable urban air mobility by addressing noise pollution.