緑地のためのAI：公園ゴミ拾いにおける自律ナビゲーションとコンピュータビジョンの活用
AI for Green Spaces: Leveraging Autonomous Navigation and Computer Vision for Park Litter Removal
公園の芝生で自律走行し、ゴミを検出して拾い上げるロボットを開発。RTK-GPSとResNet50による検出、新たな回収機構を組み合わせ、80%の成功率を達成した。
著者: Christopher Kao, Akhil Pathapati, James Davis
分類: cs.RO, cs.AI, cs.CV, cs.SY, eess.SY
原文アブストラクト
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.