A Generic Multi-Layer Architecture Based on ROS-JADE Integration for Autonomous Transport Vehicles

The design and operation of manufacturing systems is evolving to adapt to different challenges. One of the most important is the reconfiguration of the manufacturing process in response to context changes (e.g., faulty equipment or urgent orders, among others). In this sense, the Autonomous Transport Vehicle (ATV) plays a key role in building more flexible and decentralized manufacturing systems. Nowadays, robotic frameworks (RFs) are used for developing robotic systems such as ATVs, but they focus on the control of the robotic system itself. However, social abilities are required for performing intelligent interaction (peer-to-peer negotiation and decision-making) among the different and heterogeneous Cyber Physical Production Systems (such as machines, transport systems and other equipment present in the factory) to achieve manufacturing reconfiguration. This work contributes a generic multi-layer architecture that integrates a RF with a Multi-Agent System (MAS) to provide social abilities to ATVs. This architecture has been implemented on ROS and JADE, the most widespread RF and MAS framework, respectively. We believe this to be the first work that addresses the intelligent interaction of transportation systems for flexible manufacturing environments in a holistic form.

[1]  Maria Madalena T. de Araújo,et al.  Towards Industry 4.0: an overview of European strategic roadmaps , 2017 .

[2]  J. Y. Zhao,et al.  Simulation of Steel Production Logistics System Based on Multi-Agents , 2017 .

[3]  Andreas Kamagaew,et al.  Concept of Cellular Transport Systems in facility logistics , 2011, The 5th International Conference on Automation, Robotics and Applications.

[4]  Jo Wyns,et al.  Reference architecture for holonic manufacturing systems, the key to support evolution and reconfiguration , 1999 .

[5]  Duncan C. McFarlane,et al.  Product intelligence in industrial control: Theory and practice , 2013, Annu. Rev. Control..

[6]  Marga Marcos,et al.  Supporting Product Oriented Manufacturing: a Model Driven and Agent based Approach , 2018, 2018 IEEE 16th International Conference on Industrial Informatics (INDIN).

[7]  Kagermann Henning Recommendations for implementing the strategic initiative INDUSTRIE 4.0 , 2013 .

[8]  Zbynek Obdrzalek Mobile agents and their use in a group of cooperating autonomous robots , 2017, 2017 22nd International Conference on Methods and Models in Automation and Robotics (MMAR).

[9]  Paulo Leitão,et al.  Past, Present, and Future of Industrial Agent Applications , 2013, IEEE Transactions on Industrial Informatics.

[10]  Aljoscha Pörtner,et al.  A decoupled three-layered architecture for service robotics in intelligent environments , 2016, EISE '16.

[11]  Christian Schindelhauer,et al.  Decentralized hash tables for mobile robot teams solving intra-logistics tasks , 2010, AAMAS.

[12]  P. Leitao,et al.  ADACOR: a collaborative production automation and control architecture , 2005, IEEE Intelligent Systems.

[13]  Lander Usategui San Juan,et al.  The shift in the robotics paradigm — The Hardware Robot Operating System (H-ROS); an infrastructure to create interoperable robot components , 2017, 2017 NASA/ESA Conference on Adaptive Hardware and Systems (AHS).

[14]  K. Shadan,et al.  Available online: , 2012 .

[15]  Javier Bajo,et al.  Ambient Agents: Embedded Agents for Remote Control and Monitoring Using the PANGEA Platform , 2014, Sensors.

[16]  Luc Bongaerts,et al.  Reference architecture for holonic manufacturing systems: PROSA , 1998 .

[17]  Javier Bajo,et al.  PANGEA - Platform for Automatic coNstruction of orGanizations of intElligent Agents , 2012, DCAI.

[18]  Walter Ukovich,et al.  A decentralized control strategy for the coordination of AGV systems , 2018 .

[19]  Kai Furmans,et al.  Future of Material Handling - modular, flexible and efficient , 2011, IROS 2011.

[20]  Shuo Yang,et al.  AutoRobot: A Multi-Agent Software Framework for Autonomous Robots , 2018, IEICE Trans. Inf. Syst..

[21]  S. X. Chen,et al.  Multi-Agent System for Distributed Management of Microgrids , 2015, IEEE Transactions on Power Systems.

[22]  Lounis Adouane,et al.  MAS2CAR Architecture - Multi-agent System to Control and Coordinate Teamworking Robots , 2011, ICINCO.

[23]  Markus Kasurinen Mobile robots in indoor logistics , 2018 .

[24]  Ray Y. Zhong,et al.  Intelligent Manufacturing in the Context of Industry 4.0: A Review , 2017 .

[25]  Shinpei Kato,et al.  Exploring the performance of ROS2 , 2016, 2016 International Conference on Embedded Software (EMSOFT).

[26]  Bodo Urban,et al.  An Ontology-Based Approach to Enable Knowledge Representation and Reasoning in Worker-Cobot Agile Manufacturing , 2017, Future Internet.

[27]  Henrik I. Christensen,et al.  Dynamic, cooperative multi-robot patrolling with a team of UAVs , 2013, Defense, Security, and Sensing.

[28]  Anders Orebäck,et al.  Evaluation of Architectures for Mobile Robotics , 2003, Auton. Robots.

[29]  Fernando Díaz del Río,et al.  Robotics software frameworks for multi-agent robotic systems development , 2012, Robotics Auton. Syst..

[30]  Sen Yang,et al.  Towards a hybrid software architecture and multi-agent approach for autonomous robot software , 2017 .

[31]  Khumbulani Mpofu,et al.  Integration of agent technology into manufacturing enterprise: A review and platform for industry 4.0 , 2015, 2015 International Conference on Industrial Engineering and Operations Management (IEOM).

[32]  Eric Guizzo,et al.  Three Engineers, Hundreds of Robots, One Warehouse , 2008, IEEE Spectrum.

[33]  Itziar Cabanes,et al.  Decentralized Robot-Cloud Architecture for an Autonomous Transportation System in a Smart Factory , 2017, SEMANTiCS.

[34]  Nelson Rodrigues,et al.  Decentralized and on-the-fly agent-based service reconfiguration in manufacturing systems , 2018, Comput. Ind..

[35]  Bodo Urban,et al.  Flow Shop Scheduling Problem and Solution in Cooperative Robotics - Case-Study: One Cobot in Cooperation with One Worker , 2017, Future Internet.