AI for Green Spaces: Leveraging Autonomous Navigation and Computer Vision for Park Litter Removal

2025-01-21

IEEE/SICE International Symposium on System Integration

AI Summary

This study addresses the low efficiency and high cost of manual litter removal in park grasslands by proposing and implementing an autonomous litter-cleaning robotic system. The system integrates, for the first time in a grassland operational context, Spanning Tree Coverage (STC) for complete area coverage path planning, RTK-GPS for centimeter-level precise positioning, and a ResNet50-based deep learning vision model for litter detection, complemented by a custom-designed mechanical pickup mechanism. Experimental results in real-world grassland environments demonstrate a litter recognition accuracy of 94.52% and an overall cleaning success rate of 80%, confirming the technical feasibility and practical potential of the proposed approach.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Web Mining and Content Analysis: Web data integration and cleaning

Abstract

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.

Problem

• litter removal
• green spaces
• autonomous navigation
• computer vision
• park maintenance

Innovation

• Autonomous Navigation
• RTK GPS
• Computer Vision
• ResNet50
• Spanning Tree Coverage