Electric Wheelchair with Avoiding Obstacle Feature using LiDAR and CNN Method

Shiddiqi; Muhammad Rivai; Rudy Dikairono; Djoko Purwanto; Sheva Aulia

doi:10.1109/ICITISEE57756.2022.10057839

Electric Wheelchair with Avoiding Obstacle Feature using LiDAR and CNN Method

Shiddiqi
, Muhammad Rivai
, Rudy Dikairono
, Djoko Purwanto
, Sheva Aulia

Institut Teknologi Sepuluh Nopember

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

7 Citations (Scopus)

Abstract

Wheelchairs are one of the important medical instruments for the mobility of patients with disabilities to carry out several activities. Wheelchair has been significantly developed from the structures to features such as electric motors, controllers, and location detection system to improve the quality of safety and comfort. However, aspects of the smart obstacle avoider are currently being developed. In this study, an Advanced Driver-Assistance System (ADAS) has been developed using 2D LiDAR as an area mapping sensor, and Convolution Neural Network (CNN) to control a wheelchair embedded in a Raspberry Pi single board computer. The experimental results show that the success rate in avoiding obstacles automatically is between 76.6% to 100%.

Original language	English
Title of host publication	Proceeding - 6th International Conference on Information Technology, Information Systems and Electrical Engineering
Subtitle of host publication	Applying Data Sciences and Artificial Intelligence Technologies for Environmental Sustainability, ICITISEE 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	618-623
Number of pages	6
ISBN (Electronic)	9798350399615
DOIs	https://doi.org/10.1109/ICITISEE57756.2022.10057839
Publication status	Published - 2022
Event	6th International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2022 - Virtual, Online, Indonesia Duration: 13 Dec 2022 → 14 Dec 2022

Publication series

Name	Proceeding - 6th International Conference on Information Technology, Information Systems and Electrical Engineering: Applying Data Sciences and Artificial Intelligence Technologies for Environmental Sustainability, ICITISEE 2022

Conference

Conference	6th International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2022
Country/Territory	Indonesia
City	Virtual, Online
Period	13/12/22 → 14/12/22

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

ADAS
CNN
Disability
LiDAR

Access to Document

10.1109/ICITISEE57756.2022.10057839

Cite this

Shiddiqi, Rivai, M., Dikairono, R., Purwanto, D., & Aulia, S. (2022). Electric Wheelchair with Avoiding Obstacle Feature using LiDAR and CNN Method. In Proceeding - 6th International Conference on Information Technology, Information Systems and Electrical Engineering: Applying Data Sciences and Artificial Intelligence Technologies for Environmental Sustainability, ICITISEE 2022 (pp. 618-623). (Proceeding - 6th International Conference on Information Technology, Information Systems and Electrical Engineering: Applying Data Sciences and Artificial Intelligence Technologies for Environmental Sustainability, ICITISEE 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICITISEE57756.2022.10057839

Shiddiqi, ; Rivai, Muhammad ; Dikairono, Rudy et al. / Electric Wheelchair with Avoiding Obstacle Feature using LiDAR and CNN Method. Proceeding - 6th International Conference on Information Technology, Information Systems and Electrical Engineering: Applying Data Sciences and Artificial Intelligence Technologies for Environmental Sustainability, ICITISEE 2022. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 618-623 (Proceeding - 6th International Conference on Information Technology, Information Systems and Electrical Engineering: Applying Data Sciences and Artificial Intelligence Technologies for Environmental Sustainability, ICITISEE 2022).

@inproceedings{49567582a65344f9ae0ebe79b5efbd4e,

title = "Electric Wheelchair with Avoiding Obstacle Feature using LiDAR and CNN Method",

abstract = "Wheelchairs are one of the important medical instruments for the mobility of patients with disabilities to carry out several activities. Wheelchair has been significantly developed from the structures to features such as electric motors, controllers, and location detection system to improve the quality of safety and comfort. However, aspects of the smart obstacle avoider are currently being developed. In this study, an Advanced Driver-Assistance System (ADAS) has been developed using 2D LiDAR as an area mapping sensor, and Convolution Neural Network (CNN) to control a wheelchair embedded in a Raspberry Pi single board computer. The experimental results show that the success rate in avoiding obstacles automatically is between 76.6\% to 100\%.",

keywords = "ADAS, CNN, Disability, LiDAR",

author = "Shiddiqi and Muhammad Rivai and Rudy Dikairono and Djoko Purwanto and Sheva Aulia",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 6th International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2022 ; Conference date: 13-12-2022 Through 14-12-2022",

year = "2022",

doi = "10.1109/ICITISEE57756.2022.10057839",

language = "English",

series = "Proceeding - 6th International Conference on Information Technology, Information Systems and Electrical Engineering: Applying Data Sciences and Artificial Intelligence Technologies for Environmental Sustainability, ICITISEE 2022",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "618--623",

booktitle = "Proceeding - 6th International Conference on Information Technology, Information Systems and Electrical Engineering",

address = "United States",

}

Shiddiqi, , Rivai, M , Dikairono, R , Purwanto, D & Aulia, S 2022, Electric Wheelchair with Avoiding Obstacle Feature using LiDAR and CNN Method. in Proceeding - 6th International Conference on Information Technology, Information Systems and Electrical Engineering: Applying Data Sciences and Artificial Intelligence Technologies for Environmental Sustainability, ICITISEE 2022. Proceeding - 6th International Conference on Information Technology, Information Systems and Electrical Engineering: Applying Data Sciences and Artificial Intelligence Technologies for Environmental Sustainability, ICITISEE 2022, Institute of Electrical and Electronics Engineers Inc., pp. 618-623, 6th International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2022, Virtual, Online, Indonesia, 13/12/22. https://doi.org/10.1109/ICITISEE57756.2022.10057839

Electric Wheelchair with Avoiding Obstacle Feature using LiDAR and CNN Method. / Shiddiqi, ; Rivai, Muhammad ; Dikairono, Rudy et al.
Proceeding - 6th International Conference on Information Technology, Information Systems and Electrical Engineering: Applying Data Sciences and Artificial Intelligence Technologies for Environmental Sustainability, ICITISEE 2022. Institute of Electrical and Electronics Engineers Inc., 2022. p. 618-623 (Proceeding - 6th International Conference on Information Technology, Information Systems and Electrical Engineering: Applying Data Sciences and Artificial Intelligence Technologies for Environmental Sustainability, ICITISEE 2022).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Electric Wheelchair with Avoiding Obstacle Feature using LiDAR and CNN Method

AU - Shiddiqi,

AU - Rivai, Muhammad

AU - Dikairono, Rudy

AU - Purwanto, Djoko

AU - Aulia, Sheva

PY - 2022

Y1 - 2022

N2 - Wheelchairs are one of the important medical instruments for the mobility of patients with disabilities to carry out several activities. Wheelchair has been significantly developed from the structures to features such as electric motors, controllers, and location detection system to improve the quality of safety and comfort. However, aspects of the smart obstacle avoider are currently being developed. In this study, an Advanced Driver-Assistance System (ADAS) has been developed using 2D LiDAR as an area mapping sensor, and Convolution Neural Network (CNN) to control a wheelchair embedded in a Raspberry Pi single board computer. The experimental results show that the success rate in avoiding obstacles automatically is between 76.6% to 100%.

AB - Wheelchairs are one of the important medical instruments for the mobility of patients with disabilities to carry out several activities. Wheelchair has been significantly developed from the structures to features such as electric motors, controllers, and location detection system to improve the quality of safety and comfort. However, aspects of the smart obstacle avoider are currently being developed. In this study, an Advanced Driver-Assistance System (ADAS) has been developed using 2D LiDAR as an area mapping sensor, and Convolution Neural Network (CNN) to control a wheelchair embedded in a Raspberry Pi single board computer. The experimental results show that the success rate in avoiding obstacles automatically is between 76.6% to 100%.

KW - ADAS

KW - CNN

KW - Disability

KW - LiDAR

UR - https://www.scopus.com/pages/publications/85150422844

U2 - 10.1109/ICITISEE57756.2022.10057839

DO - 10.1109/ICITISEE57756.2022.10057839

M3 - Conference contribution

AN - SCOPUS:85150422844

T3 - Proceeding - 6th International Conference on Information Technology, Information Systems and Electrical Engineering: Applying Data Sciences and Artificial Intelligence Technologies for Environmental Sustainability, ICITISEE 2022

SP - 618

EP - 623

BT - Proceeding - 6th International Conference on Information Technology, Information Systems and Electrical Engineering

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 6th International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2022

Y2 - 13 December 2022 through 14 December 2022

ER -

Shiddiqi , Rivai M , Dikairono R , Purwanto D, Aulia S. Electric Wheelchair with Avoiding Obstacle Feature using LiDAR and CNN Method. In Proceeding - 6th International Conference on Information Technology, Information Systems and Electrical Engineering: Applying Data Sciences and Artificial Intelligence Technologies for Environmental Sustainability, ICITISEE 2022. Institute of Electrical and Electronics Engineers Inc. 2022. p. 618-623. (Proceeding - 6th International Conference on Information Technology, Information Systems and Electrical Engineering: Applying Data Sciences and Artificial Intelligence Technologies for Environmental Sustainability, ICITISEE 2022). doi: 10.1109/ICITISEE57756.2022.10057839

Electric Wheelchair with Avoiding Obstacle Feature using LiDAR and CNN Method

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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