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Table Cleaning Task by Human Support Robot Using Deep Learning Technique.

Jia Yin¹, Koppaka Ganesh Sai Apuroop¹, Yokhesh Krishnasamy Tamilselvam²

¹Engineering Product Development Pillar, Singapore University of Technology and Design (SUTD), Singapore 487372, Singapore.

Sensors (Basel, Switzerland)

|March 22, 2020

Summary

This summary is machine-generated.

A Human Support Robot (HSR) uses deep learning for food litter detection and a planner framework for efficient table cleaning in food courts, achieving 96% accuracy.

Keywords:

CNN deep learning food litter detection human support robot inspection table cleaning

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Area of Science:

Robotics
Artificial Intelligence
Computer Vision

Background:

Maintaining cleanliness in public food courts is challenging.
Automated solutions are needed to improve efficiency and hygiene.

Purpose of the Study:

To develop and evaluate a Human Support Robot (HSR) system for table cleaning and inspection in food courts.
To implement a deep learning model for food litter detection and a planner framework for efficient cleaning path generation.

Main Methods:

A lightweight Deep Convolutional Neural Network (DCNN) was developed for food litter recognition.
A planner framework was designed to generate optimal cleaning paths for the HSR.
The system was tested on a Toyota HSR in a simulated food court environment.

Main Results:

The DCNN achieved an average detection accuracy of 96% for food litter.
The planner framework generated optimal cleaning paths in real-time.
The system demonstrated reduced cleaning time through grouped cleaning actions.

Conclusions:

The proposed HSR system is effective for automated table cleanliness inspection and cleaning in food courts.
The deep learning and planner framework contribute to efficient and accurate food litter removal.
This technology has the potential to significantly improve hygiene standards in public dining areas.