Addressing the challenge of roadside litter in the United States, which has traditionally relied on costly and ineffective manual cleanup methods, this paper presents an autonomous multi-robot system for highway litter monitoring and collection. Our solution integrates an aerial vehicle to scan and gather data across highway stretches with a terrestrial robot equipped with a Convolutional Neural Network (CNN) for litter detection and mapping. Upon detecting litter, the ground robot navigates to each pinpointed location, re-assesses the vicinity, and employs a "greedy pickup" approach to address potential mapping inaccuracies or litter misplacements. Through simulation studies and real-world robotic trials, this work highlights the potential of our proposed system for highway cleanliness and management in the context of Robotics, Automation, and Artificial Intelligence

翻译：针对美国高速公路路边垃圾清理长期依赖成本高昂且效率低下的人工方法这一挑战，本文提出了一种用于高速公路垃圾监测与收集的自主多机器人系统。该方案集成了一台飞行器，用于扫描并采集高速公路沿路数据，同时配备基于卷积神经网络（CNN）的地面机器人，实现垃圾检测与地图构建。当地面机器人检测到垃圾时，会导航至每个定位点，重新评估周围环境，并采用“贪心拾取”策略以应对潜在的地图误差或垃圾位置偏移。通过仿真实验与真实机器人测试，本文展示了所提系统在机器人技术、自动化与人工智能框架下，对于提升高速公路清洁度与管理的潜力。