TY - GEN
T1 - Design of Path Planning System for Multi-Agent AGV Using A∗Algorithm
AU - Pratama, Andi Yuda
AU - Ariyadi, Muhammad Rizal
AU - Tamara, Mohamad Nasyir
AU - Purnomo, Didik Setyo
AU - Ramadhan, Novan Asdianto
AU - Pramujati, Bambang
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - This study aims to design an efficient path-planning system for Multi-Agent AGV. The path planning system developed is based on an improved A∗ algorithm to be able to control the path division for several AGVs that operate without a collision. Path planning will be placed on the server to receive input from each AGV in the form of start coordinates and goal coordinates that they will achieve. From some of the incoming input from several AGVs, A∗ which has been added to the collision calculation function will look for the multi-AGV collision points. A collision point is defined as a point from different AGVs that have the same coordinates and are on the same road order. The stopping point of one AGV will also be the crash point of another AGV. The crash point is then defined as a new obstacle point which is used as input for the next path-planning process. The path planning process is iterated until all AGVs find path points that are safe from collisions. The simulation results show that using the collision calculation method, the enhanced A∗ algorithm produces multi-paths with zero collisions.
AB - This study aims to design an efficient path-planning system for Multi-Agent AGV. The path planning system developed is based on an improved A∗ algorithm to be able to control the path division for several AGVs that operate without a collision. Path planning will be placed on the server to receive input from each AGV in the form of start coordinates and goal coordinates that they will achieve. From some of the incoming input from several AGVs, A∗ which has been added to the collision calculation function will look for the multi-AGV collision points. A collision point is defined as a point from different AGVs that have the same coordinates and are on the same road order. The stopping point of one AGV will also be the crash point of another AGV. The crash point is then defined as a new obstacle point which is used as input for the next path-planning process. The path planning process is iterated until all AGVs find path points that are safe from collisions. The simulation results show that using the collision calculation method, the enhanced A∗ algorithm produces multi-paths with zero collisions.
KW - Collision Management
KW - Mobile Robot
KW - Multi AGV
KW - Path Allocation
KW - Positioning
UR - http://www.scopus.com/inward/record.url?scp=85173609083&partnerID=8YFLogxK
U2 - 10.1109/IES59143.2023.10242585
DO - 10.1109/IES59143.2023.10242585
M3 - Conference contribution
AN - SCOPUS:85173609083
T3 - IES 2023 - International Electronics Symposium: Unlocking the Potential of Immersive Technology to Live a Better Life, Proceeding
SP - 335
EP - 341
BT - IES 2023 - International Electronics Symposium
A2 - Yunanto, Andhik Ampuh
A2 - Ramadhani, Afifah Dwi
A2 - Prayogi, Yanuar Risah
A2 - Putra, Putu Agus Mahadi
A2 - Ruswiansari, Maretha
A2 - Ridwan, Mohamad
A2 - Gamar, Farida
A2 - Rahmawati, Weny Mistarika
A2 - Rusli, Rusli Muhammad
A2 - Humaira, Fitrah Maharani
A2 - Adila, Ahmad Firyal
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 25th International Electronics Symposium, IES 2023
Y2 - 8 August 2023 through 10 August 2023
ER -