Energy Consumption Optimization of UAV-Assisted Traffic Monitoring Scheme With Tiny Reinforcement Learning

Unmanned aerial vehicles (UAVs) equipped with high-definition cameras have the capability to capture comprehensive and multiangled images of road conditions, facilitating more efficient collection of pertinent road data. However, drones encounter challenges in performing related tasks for an extended period due to their limited energy capacity. Therefore, a crucial concern is how to plan the path of UAVs and minimize energy consumption. To address this problem, we propose a multiagent deep deterministic policy gradient (MADDPG)-based algorithm for UAV path planning (MAUP). Considering the energy consumption and memory usage of MAUP, we have conducted optimizations to reduce consumption on both fronts. First, we define an optimization problem aimed at reducing UAV energy consumption. Second, we transform the defined optimization problem into a reinforcement learning problem and design MAUP to solve it. Finally, we optimize energy consumption and memory usage by reducing the number of neurons in the hidden layer of MAUP and conducting fine-grained pruning on connections. The final simulation results demonstrate that our method effectively reduces the energy consumption of UAVs compared to other methods.