用无人机自动驱赶农田鹿群，实现全天候精准防护。
Geofenced Unmanned Aerial Robotic Defender for Deer Detection and Deterrence (GUARD)
- 基于YOLO的实时视觉识别鹿只，快速响应
- 高效路径规划让无人机覆盖更广，续航更久
- 适合农场主使用，解决真实农田环境难题
野生动物造成的农作物损失，尤其是鹿类，威胁农业产量。传统驱赶手段在可扩展性、响应速度和适应不同农田环境方面常显不足。本文提出一种集成式无人飞行器（UAV）系统，用于自主野生动物驱赶，作为农场机器人挑战赛的一部分。系统结合基于YOLO的实时计算机视觉模块进行鹿只检测、节能型覆盖路径规划算法以提升田间监控效率，并配备自主充电站支持持续运行。与明尼苏达州当地农民合作，系统针对地形、基础设施限制及动物行为等实际约束进行了优化。通过仿真与实地测试相结合的方式评估，结果表明系统具备高检测准确率、高效覆盖能力及长时间作业性能。研究验证了基于无人机的野生动物驱赶在精准农业中的可行性与有效性，为未来部署提供了可扩展框架。
原文摘要 · Abstract (English)
Wildlife-induced crop damage, particularly from deer, threatens agricultural productivity. Traditional deterrence methods often fall short in scalability, responsiveness, and adaptability to diverse farmland environments. This paper presents an integrated unmanned aerial vehicle (UAV) system designed for autonomous wildlife deterrence, developed as part of the Farm Robotics Challenge. Our system combines a YOLO-based real-time computer vision module for deer detection, an energy-efficient coverage path planning algorithm for efficient field monitoring, and an autonomous charging station for continuous operation of the UAV. In collaboration with a local Minnesota farmer, the system is tailored to address practical constraints such as terrain, infrastructure limitations, and animal behavior. The solution is evaluated through a combination of simulation and field testing, demonstrating robust detection accuracy, efficient coverage, and extended operational time. The results highlight the feasibility and effectiveness of drone-based wildlife deterrence in precision agriculture, offering a scalable framework for future deployment and extension.
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