Agent-Based Emulation of an Electric Overhead Monorail System Pilot Plant

Transkript

1 Agent-Based Emulation of an Electric Overhead Monorail System Pilot Plant Dipl.-Ing. Peter Tenerowicz-Wirth , ImViReLL 12 fml Lehrstuhl für Fördertechnik Materialfluss Logistik Prof. Dr.-Ing. Dipl.-Wi.-Ing. W. A. Günthner Technische Universität München TU München Faculty of Mechanical Engineering Lehrstuhl fml fml Institute for Materials Handling, Material Flow, Logistics Prof. Dr.-Ing. Dipl.-Wi.-Ing. Willibald A. Günthner 51 Employees Teaching Research Industrial Projects 2 1

2 Research Profile Areas of Work Automation & Identification Calculation & Design Digital Tools & Simulation Methods & Concepts Research Focuses Changeable Material Flow Systems Internet of Things RFID in Logistics Virtual and Augmented Reality Logistics Planning Automotive Logistics Construction Logistics Conveyors in Intralogistics Storage and Order Picking Systems Crane Engineering & Design of Load-supporting Structures FEM-Analysis & Multi-body Simulation Bulk Material Conveying 3 1. Introduction Summary & Outlook 4 2

3 Introduction Demands on Material Flow Systems Initial Situation Increasing variety Shorter innovation and product life cycles Difficulties in predicting order loads and structures Increasing importance of order-based production Dynamically adjustable material handling systems Low-cost Approach Modularization and decentralization of control software in the design of conveyor systems Increasing flexibility, robustness and reusability 5 Introduction Impact of the Internet of Things Paradigm on Intralogistics Conventional material flow control material flow control programmable logic controllers (PLCs) routing deadlock prevention coordination status monitoring I/O control Internet of Things approach module physics electrics mechanics transport unit transport unit module Entity communication cooperation transport units & materials handling equipment module Entity logistics function as mechatronical entity 6 3

4 Control Architecture of the Pilot Plant Trolley 1 Crane Switch 1 Switch 2 Trolley 2 Switch 3 7 Control Architecture of the Pilot Plant 8 4

5 Functionality and Processes Order Negotiation and Processing START Get transport order(s) Go to next reserved waypoint Order Negotiation no competitors? yes Calculate transport costs lowest costs? no no no position = destination? yes Coordinate / execute load transfer order finished? Order Processing yes yes Take job / reserve route Jump to START 9 Findings (I) Agent-based control demonstrates interoperability of different program types: Java module agents on Windows CE embbeded PCs Net service agents (blackboard, visualization) in C# on Windows XP-based PC IEC based machine control Agent-based material flow control implements complex functions: Order disposition Path planning and reservation Automatic generation of alternative (evasive) jobs Deadlock-free operation 10 5

6 Findings (II) Algorithms independent of topology, order structure, number of trolleys System extension/modification possible at any time Recognition and automatic bypassing of faulty/blocked routes (if alternative route is available) Robust and flexible system 11 Agent-Based Emulation (I) Objective: Extending the pilot plant to a more complex scenario General conditions in complex scenario: Interaction between many different material flow components Stochastic influences Simulation model Well suited for analysis of highly dynamic processes Special application of simulation: Emulation Java agent replaces machine control layer Agent imitates functionality of machine control (e.g. change of position) Test of software functionality in virtual environment No mechanical or electrical components affected 12 6

7 Agent-Based Emulation (II) Basic Internal Structure of an Entity System Design for the Testing of Machine-oriented Software Software agent (non-real-time) Software agent (non-real-time) Emulator Middleware Middleware Machine control (real-time) Emulator Machine control (real-time) Simulation 13 Advantages of Emulation Efficient debugging/troubleshooting Less effort and errors during implementation of algorithms Supporting specification of functional requirements Supporting creation of control strategies Shorter launch time Analysis of impact of changes (control software / real plant) Possibility of training/optimizing self-learning agents System control training tool 14 7

8 Mixed Operation of Real and Simulated Components (I) 2 real trolleys 4 virtual trolleys 15 Mixed Operation of Real and Simulated Components (II) Mixed Operation Real Operation 16 8

9 Summary & Outlook Chances of Agent-based Control and Emulation (I) Remote access to logistics labs / Cooperative research scenarios Software agents and machine control connected by middleware Agents can remote control a pilot plant at another location via VPN Real-time communication executed on machine control layer Non-real-time functions executed by remote multi-agent system Remote testing of agents algorithms and of material flow strategies 17 Summary & Outlook Chances of Agent-based Control and Emulation (II) Pilot plants at different locations share their software agents can be virtually merged Agents of foreign plant are used to generate emulator agents to expand the own pilot plant Implementation of extensive test scenarios with little effort Vision requires Compatible software agents & Compatible interfaces to machine control Close cooperation between logistics labs 18 9

10 Thank you for your attention! 19 10