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

TL;DR

This paper presents an autonomous robot that navigates parks using GPS and coverage algorithms, detects trash with high accuracy, and successfully picks up litter, offering a practical solution to park litter problems.

Contribution

The paper introduces an integrated autonomous trash collection system combining GPS navigation, coverage algorithms, and CNN-based trash detection, with a novel pickup mechanism.

Findings

Achieved 94.52% trash detection accuracy.

Reached 80% overall success rate in trash pickup.

Demonstrated viability of autonomous trash robots in park environments.

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.