On Energy Replenishment Station Site Selection and Path Planning for Drone Video Streaming

In recent years, with the advancement of autonomous aerial vehicles (AAV) technologies, small AAVs have been utilized for borderline patrol, especially for real-time video transmission without interruption. However, these small AAVs face limitations in conducting long-endurance and long-distance missions solely relying on their initial onboard resources. To address this issue, this paper introduces a novel combined AAV air resupply system based on energy cycle resupply. In this system, a ground energy resupply station dispatches a replenishing AAV (AAV-R) to dock with it along the border and transmit energy to the task AAV (AAV-T), when its energy resources are depleted, ensuring continuous energy supply. To tackle the challenge of siting the energy recharge station, we propose a greedy siting algorithm utilizing Monte Carlo methods and an algorithm based on ant colony and clustering. Simulations demonstrate that the number of energy recharge stations can be reduced to 47.6% - 52.9% compared to the AAV-T autonomous return recharge scheme. Additionally, we present a Q Learning-based energy cycle resupply algorithm for AAV-R path planning, offering practical applications in real-world borderline patrol scenarios.