TY - GEN
T1 - Path Planning for 4-Wheeled Omnidirectional Cellular Conveyor using Q-Learning Algorithm
AU - Kautsar, Syamsiar
AU - Aisjah, Aulia Siti
AU - Syai'in, Mat
AU - Indriawati, Katherin
AU - Biyanto, Totok Ruki
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - The manufacturing industry has implemented various technologies, such as robot arms, AGVs (Automated Guided Vehicles), and conveyors, to enhance the efficiency of material flow. Conveyors facilitate the transportation of objects over long distances. The fixed belt conveyor is the most commonly used type of conveyor across various industries. The fixed belt conveyor follows a predetermined path to distribute items. Nevertheless, during its development, the material distribution process requires various paths. Therefore, the development of flexible conveyors was necessary to meet the requirements of various delivery routes. The most recent innovation in flexible conveyors is the conveyor with omnidirectional wheel drive. This study involved developing a path-planning algorithm utilizing classic Q-Learning (CQL), Double Q-Learning(DQL), and Action Restrictions Q-Learning (ARQL) to calculate the optimal path on an four-wheeled omnidirectional conveyor. The results show that the ARQL algorithm generates the optimal trajectory, achieving a processing time of less than 100 milliseconds.
AB - The manufacturing industry has implemented various technologies, such as robot arms, AGVs (Automated Guided Vehicles), and conveyors, to enhance the efficiency of material flow. Conveyors facilitate the transportation of objects over long distances. The fixed belt conveyor is the most commonly used type of conveyor across various industries. The fixed belt conveyor follows a predetermined path to distribute items. Nevertheless, during its development, the material distribution process requires various paths. Therefore, the development of flexible conveyors was necessary to meet the requirements of various delivery routes. The most recent innovation in flexible conveyors is the conveyor with omnidirectional wheel drive. This study involved developing a path-planning algorithm utilizing classic Q-Learning (CQL), Double Q-Learning(DQL), and Action Restrictions Q-Learning (ARQL) to calculate the optimal path on an four-wheeled omnidirectional conveyor. The results show that the ARQL algorithm generates the optimal trajectory, achieving a processing time of less than 100 milliseconds.
KW - flexible conveyor
KW - omnidirectional drive
KW - path planning
KW - q-learning
UR - http://www.scopus.com/inward/record.url?scp=85205015018&partnerID=8YFLogxK
U2 - 10.1109/IES63037.2024.10665817
DO - 10.1109/IES63037.2024.10665817
M3 - Conference contribution
AN - SCOPUS:85205015018
T3 - 2024 International Electronics Symposium: Shaping the Future: Society 5.0 and Beyond, IES 2024 - Proceeding
SP - 466
EP - 472
BT - 2024 International Electronics Symposium
A2 - Yunanto, Andhik Ampuh
A2 - Ramadhani, Afifah Dwi
A2 - Prayogi, Yanuar Risah
A2 - Putra, Putu Agus Mahadi
A2 - Rahmawati, Weny Mistarika
A2 - Rusli, Muhammad Rizani
A2 - Humaira, Fitrah Maharani
A2 - Nadziroh, Faridatun
A2 - Sa'adah, Nihayatus
A2 - Muna, Nailul
A2 - Rizki, Aris Bahari
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 26th International Electronics Symposium, IES 2024
Y2 - 6 August 2024 through 8 August 2024
ER -