UAV Trajectory Planning with Non-Renewable Energy Consumption Minimization

Unmanned aerial vehicles (UAVs) have recently attracted significant attention in both industry and academia. However, limited battery capacity remains a major constraint, making the planning of UAV routes involving charging stations a challenging problem. As the world strives toward net-zero carbon emission, charging stations are increasingly powered by renewable energy. This paper investigates the UAV path planning problem of minimizing the dependence on the non-renewable energy, under the constraint of limited renewable energy availability. To address this issue, we propose an ant colony optimization (ACO)-based algorithm. Simulation results show that the proposed ACO-based algorithm delivers the near optimal solution with low complexity, reducing the nonrenewable energy consumption by 95% and 86% when serving one and six users, respectively.