AI for Green Spaces: Leveraging Autonomous Navigation and Computer Vision for Park Litter Removal
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866909993277587456 |
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| author | Kao, Christopher Pathapati, Akhil Davis, James |
| author_facet | Kao, Christopher Pathapati, Akhil Davis, James |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2601_11876 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | AI for Green Spaces: Leveraging Autonomous Navigation and Computer Vision for Park Litter Removal Kao, Christopher Pathapati, Akhil Davis, James Robotics Artificial Intelligence Computer Vision and Pattern Recognition Systems and Control 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. |
| title | AI for Green Spaces: Leveraging Autonomous Navigation and Computer Vision for Park Litter Removal |
| topic | Robotics Artificial Intelligence Computer Vision and Pattern Recognition Systems and Control |
| url | https://arxiv.org/abs/2601.11876 |