用机器人自动巡检捡拾公园草地垃圾，成功率80%。
AI for Green Spaces: Leveraging Autonomous Navigation and Computer Vision for Park Litter Removal
- 用树状覆盖算法生成路径，配合厘米级定位导航
- 用ResNet50识别垃圾，准确率达94.52%
- 设计专用抓取装置，整体任务成功率80%
仅在美国就有500亿件垃圾。草地区域因野餐者随意丢弃垃圾而问题突出。本文提出构建一种可在公园中自主导航、识别并拾取垃圾的机器人。为实现自主导航，采用生成全覆盖路径的生成树覆盖（STC）算法；通过实时动态（RTK）GPS实现每秒厘米级定位。在计算机视觉方面，使用ResNet50卷积神经网络（CNN），垃圾检测准确率达94.52%。针对垃圾拾取，测试多种设计方案后，选定一种专为草地上常见垃圾优化的新抓取机制。最终系统整体成功率达80%，证明了在草地区域部署自主拾垃圾机器人的可行性。
原文摘要 · Abstract (English)
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.
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