There are 50 billion pieces of litter in the U.S. alone. Grass fields contribute to this problem because picnickers tend to leave trash on the field. We propose building a robot that can autonomously navigate, identify, and pick up trash in parks. To autonomously navigate the park, we used a Spanning Tree Coverage (STC) algorithm to generate a coverage path the robot could follow. To navigate this path, we successfully used Real-Time Kinematic (RTK) GPS, which provides a centimeter-level reading every second. For computer vision, we utilized the ResNet50 Convolutional Neural Network (CNN), which detects trash with 94.52% accuracy. For trash pickup, we tested multiple design concepts. We select a new pickup mechanism that specifically targets the trash we encounter on the field. Our solution achieved an overall success rate of 80%, demonstrating that autonomous trash pickup robots on grass fields are a viable solution.

翻译：仅在美国就有500亿件垃圾。草坪区域是这一问题的主要来源，因为野餐者往往将垃圾遗留在草地上。我们提出构建一种能够自主导航、识别并拾取公园垃圾的机器人。为实现公园内的自主导航，我们采用生成树覆盖（STC）算法来规划机器人可循迹的覆盖路径。在路径导航方面，我们成功应用了实时动态（RTK）GPS技术，该系统每秒可提供厘米级精度的定位数据。在计算机视觉模块中，我们采用了ResNet50卷积神经网络（CNN），其垃圾检测准确率达到94.52%。针对垃圾拾取环节，我们测试了多种设计方案，最终选定了一种专门针对野外常见垃圾的新型抓取机构。我们的解决方案整体成功率达到了80%，证明草坪自主垃圾清理机器人是一种可行的技术路径。