Kitting Demonstrator based on a Plug and Produce Concept: Implementation of Multi-Agent System to simulate a flexible kitting system
2023 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE credits
Student thesis
Abstract [en]
The study delves into the implementation of Plug and Produce as a flexible kitting operation, comparing its advantages with conventional methods that utilizes a Programmable Logic Controller (PLC). Plug and Produce is a pioneering concept aimed at minimizing software and hardware reconfigurations in a system, leading to significant reductions in setup time and costs. Unlike centralized control systems found in traditional setups, Plug and Produce adopts a Multi-Agent System (MAS), empowering resources with equal control and seamless communication interfaces.
The kitting process is governed by agents programmed through AgentEditor software, and the simulation is demonstrated using ABB RobotStudio. Data transfer between the two software is achieved through REST API, enabling bidirectional communication for position data and robot control signals. Synchronization is ensured through continuous updates of robot commands in the physical space, communicated to AgentEditor via REST API, ensuring a smooth execution as the robot awaits the next command.
The advantages of the Multi-Agent Concept for Just-In-Time (JIT) kitting simulation include: (1) dynamic determination of the number of parts in the kit based on user input and order requests, (2) easy adjustment of offset values between successive part’s drop-positionas needed, and (3) faster reconfiguration to add or remove part agents or resource agents from the kit. The paper also presents the steps required to reconfigure the system for the addition of a new part agent. Additionally, differences between MAS and PLC systems in terms of certain Key Performance Indicators are highlighted.
Place, publisher, year, edition, pages
2023. , p. 43
Keywords [en]
flexible kitting operation, Plug and Produce, Multi-Agent System, Programmable Logic Controller
National Category
Robotics
Identifiers
URN: urn:nbn:se:hv:diva-20915Local ID: EXC915OAI: oai:DiVA.org:hv-20915DiVA, id: diva2:1810590
Subject / course
Robotics
Educational program
Master i robotik och automation
Supervisors
Examiners
2023-11-152023-11-082023-11-15Bibliographically approved