Automated Guided Vehicle Routing: Static, Dynamic and Free Range
Hameedah Sahib Hasan1, MSZ Abidin2, MSA Mahmud3, M. F. Muhamad Said4
1Hameedah Sahib Hasan, Assistant lecturer, Ministry of Higher Education and Scientific Research, Iraq.
2Mohamad Shukri Zainal Abidin, Department of Control and Mechatronics Engineering, School of Electrical Engineering, Universiti Teknologi Malaysia Johor Bahru, Malaysia.
3Mohd Saiful Azimi Mahmud, Department of Control and Mechatronics Engineering, School of Electrical Engineering, Universiti Teknologi Malaysia Johor Bahru, Malaysia.
4Mohd Farid Muhamad Said, Associate Professor, School of Mechanical Engineering, Universiti Teknologi Malaysia (UTM), Johor Bahru, Malaysia.
Manuscript received on 01 September 2019 | Revised Manuscript received on 10 September 2019 | Manuscript Published on 23 September 2019 | PP: 1-7 | Volume-8 Issue-5C, May 2019 | Retrieval Number: E10010585C19/19©BEIESP | DOI: 10.35940/ijeat.E1001.0585C19
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Nowadays, automated guided vehicles (AGV) play very important role in modern factory automations. The AGV systems provide efficient routing for material flow and distribution among workstations at the exact time and place. Routing of AGV is the process of determining routes to fulfill their respective transport jobs. The routing problem of AGV in production system can be studied as a shortest path problem on a transfer network. It aims to find the shortest path between two vertices or nods. Most transport systems using AGV are centrally controlled and use static routing (pre-defined routes), which follows fixed line. Instead of using fixed path, dynamic routing for AGV can be used to add a high flexibility to the system. To accommodate the increased flexibility and reduced time, new operational controllers using Labview software must be able to adapt to small deviations. In this paper, routing of AGV, different AGV shortest path algorithms such as Dijkstra algorithm, A-star algorithm, Short Path Problem with Time Window (SPPTW), hybrid partial swarm optimization and genetic algorithm PSO-GA are discussed with highlights of their main differences and comparison between them. AGV Scheduling is presented. In addition, the controller developed within Labview environment directs an AGV in real time using local position system are achieved.
Keywords: Automated Guided Vehicle, AGV Routing, Shortest Path Algorithm, Labview environment, Scheduling, Local Position System
Scope of the Article: Automated Software Specification