In: 2014b 10th France-Japan/8th Europe-Asia Congress on Mecatronics (MECATRONICS2014b-Tokyo), pp. Guizani, A., Hammadi, M., Choley, J.Y., Soriano, T., Abbes, M.S., Haddar, M.: Agent-based approach for collaborative distributed mechatronic design. In: 2014a IEEE/ASME International Conference on Advanced Intelligent Mechatronics, pp. S., Haddar, M.: Multidisciplinary approach for optimizing mechatronic systems: application to the optimal design of an electric vehicle. In: 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE), pp. 27(2), 206–217 (2016)įerraguti, F., Pertosa, A., Secchi, C., Fantuzzi, C., Bonfè, M.: A methodology for comparative analysis of collaborative robots for industry 4.0. IFAC-PapersOnLine 53(2), 10861–10866 (2020)Įditions Comparison - AnyLogic Simulation Software in (consulté le 8 juin 2016)įerreira, T., Gorlach, I.A.: Development of an automated guided vehicle controller using a model-based systems engineering approach. 11(13), 6187 (2021)Ĭaruntu, C.F., Pascal, C.M., Maxim, A., Pauca, O.: Bio-inspired coordination and control of autonomous vehicles in future manufacturing and goods transportation. IEEE (2021a)Īloui, K., Guizani, A., Hammadi, M., Soriano, T., Haddar, M.: Integrated design methodology of automated guided vehicles based on swarm robotics. In: 2021a 18th International Multi-Conference on Systems, Signals & Devices (SSD), pp. In: 13ème CONFERENCE INTERNATIONALE DE MODELISATION, OPTIMISATION ET SIMULATION (MOSIM2020), 12–, AGADIR, MarocĪloui, K., Guizani, A., Hammadi, M., Haddar, M., Soriano, T.: A top-down approach to ensure the continuity of the different design levels of swarm robots. Our design approach was applied on an Automated Guided Vehicle (AGV) system to serve items and deliver parts and supplies to stations in a smart factory.Īloui, K., Hammadi, M., Soriano, T., Guizani, A., Haddar, M.: On the continuity of the swarm robot design using MBSE method and simulation. It is a top-down approach from requirements specification, functional and structural modeling using the systems modeling language (SysML) to model simulation with the multi-agent tool (Anylogic). In this article, we propose a new design approach based on multi-agent technology and the Model-based systems engineering method (MBSE) to meet the challenges of functional, physical, and software integration. However, many developers and producers of industrial robots face several challenges in designing AGV systems, such as the difficulty of defining a decentralized system decision as well as the discontinuity and complexity of the design process. Many commercially available AGVs provide a self-guided navigation system to find their way to target workstations. They can move materials and products without a predefined route. They are widely considered as one of the most important tools for flexible logistics in workshops. In fact, Automatic Guided Vehicles (AGVs) are widely used in intelligent industries due to their productivity, flexibility, and versatility. For example, Industry 4.0 is based on collaborative robots that digitize and simplify manufacturing processes. Human capabilities must be enhanced by intelligently designing a customized solution for a specific domain. Today, smart manufacturing is differentiated from many other initiatives by its emphasis on human ingenuity.
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