用智能体模拟解决无人机续航难题，实现无通信持久作业。
Agent-Based Simulation of UAV Battery Recharging for IoT Applications: Precision Agriculture, Disaster Recovery, and Dengue Vector Control
- 采用智能体仿真建模无人机群充电协调机制，无需通信
- 6000次仿真显示阈值充电策略在极端场景更稳定可靠
- 适合需要长时间无人值守的农业、灾后救援等场景
无人机（UAV）电池续航能力低，给精准农业、灾后救援及登革热媒介控制等物联网应用带来挑战。本文分析三类应用特征，基于智能体仿真（ABS）建模无人机群充电协调机制，提出两种策略：基准（BL）与充电阈值（CT）。各无人机不进行通信，降低能耗，支持远程运行。共开展6000次仿真，评估两种策略在30种不同情境下的表现，结果表明在高负载情况下CT策略更具可靠性。研究验证了三类应用可在无无人机间或与地面站通信的前提下实现持续服务，为未来策略优化与参数调整提供基准参考。
原文摘要 · Abstract (English)
The low battery autonomy of Unnamed Aerial Vehicles (UAVs or drones) can make smart farming (precision agriculture), disaster recovery, and the fighting against dengue vector applications difficult. This article considers two approaches, first enumerating the characteristics observed in these three IoT application types and then modeling an UAV's battery recharge coordination using the Agent-Based Simulation (ABS) approach. In this way, we propose that each drone inside the swarm does not communicate concerning this recharge coordination decision, reducing energy usage and permitting remote usage. A total of 6000 simulations were run to evaluate how two proposed policies, the BaseLine (BL) and ChargerThershold (CT) coordination recharging policy, behave in 30 situations regarding how each simulation sets conclude the simulation runs and how much time they work until recharging results. CT policy shows more reliable results in extreme system usage. This work conclusion presents the potential of these three IoT applications to achieve their perpetual service without communication between drones and ground stations. This work can be a baseline for future policies and simulation parameter enhancements.
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