Smart Solutions for Automated Restaurant Service with Machine Learning

The Robotic technology has dramatically improved, sometimes exceeding even human capabilities and is now even invading every sphere of our lives. This development is used in hospitality industry where robots are increasingly being applied in speeding up processes in service deliveries, thereby optimizing service quality. Restaurants have recently become trendy towards a widespread use of robotic systems that can undertake an extremely wide range of tasks. These include taking orders, retrieving items from the kitchen, delivering food to tables, and even processing billing transactions as part of making the dining experience beautiful. This project deals with a smart delivery robot to deliver food in an efficient manner. The primary challenge in traditional restaurants has been the ability to deal with inconsistency in service, human errors, quicker and faster food and beverage delivery. Mechanically, the system is a line-following robot vehicle equipped with an Arduino module and advanced algorithms designed to navigate through a restaurant environment. The use of an RFID-based table identification and localization technique ensures precise delivery. The robot can follow a predetermined path into the destination with minimal deviation to ensure that orders are delivered accurately and promptly. The robots are to manage typical orders as best it could to retain the quality of service in a restaurant setting. Furthermore, distance travelled by the delivery robot and percentage of error is predicted through Gaussian Regression model.

Authors

Institutions

Publication Details

Published
2024-11-18
DOI
https://doi.org/10.1109/icdici62993.2024.10810935
Primary Topic
Consumer Retail Behavior Studies
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Smart Solutions for Automated Restaurant Service with Machine Learning

M. Srilekha, Sanjay Saxena, S. Umamaheswari, Sunitha. R
Consumer Retail Behavior Studies
article

Smart Solutions for Automated Restaurant Service with Machine Learning

M. Srilekha, Sanjay Saxena, S. Umamaheswari, Sunitha. R
article en

Abstract

The Robotic technology has dramatically improved, sometimes exceeding even human capabilities and is now even invading every sphere of our lives. This development is used in hospitality industry where robots are increasingly being applied in speeding up processes in service deliveries, thereby optimizing service quality. Restaurants have recently become trendy towards a widespread use of robotic systems that can undertake an extremely wide range of tasks. These include taking orders, retrieving items from the kitchen, delivering food to tables, and even processing billing transactions as part of making the dining experience beautiful. This project deals with a smart delivery robot to deliver food in an efficient manner. The primary challenge in traditional restaurants has been the ability to deal with inconsistency in service, human errors, quicker and faster food and beverage delivery. Mechanically, the system is a line-following robot vehicle equipped with an Arduino module and advanced algorithms designed to navigate through a restaurant environment. The use of an RFID-based table identification and localization technique ensures precise delivery. The robot can follow a predetermined path into the destination with minimal deviation to ensure that orders are delivered accurately and promptly. The robots are to manage typical orders as best it could to retain the quality of service in a restaurant setting. Furthermore, distance travelled by the delivery robot and percentage of error is predicted through Gaussian Regression model.

SRM Institute of Science and Technology (IN)
Openalex Percentile: Top 65%
Consumer Retail Behavior Studies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Smart Solutions for Automated Restaurant Service with Machine Learning — M. Srilekha, Sanjay Saxena, et al. · (2024) | TGRS Research Map | TGRS