1 / 17

CIS Structure

Collaborative Intelligent Systems William A. Gruver Dilip B. Kotak FLINT – CIBI 2003 UC Berkeley - 15 December 2003. CIS Structure. Monitoring Identification. Communi- cations. Collaboration Coordination. Systems Architecture. Manufac. Supply Chain. Service. Infrastructure

tuyet
Télécharger la présentation

CIS Structure

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Collaborative Intelligent SystemsWilliam A. GruverDilip B. KotakFLINT – CIBI 2003UC Berkeley - 15 December 2003

  2. CIS Structure Monitoring Identification Communi- cations Collaboration Coordination Systems Architecture Manufac. Supply Chain Service Infrastructure Elec. Power Infrastructure Transport.

  3. Technologies • Self Monitoring, Identification & Control • Hardware – sensors, monitoring & actuators Auto-ID Software Soft Computing • Agent Communications Infrastructure • Hardware – wired, wireless RF, 802.11 • Software – ontology, protocols, security FIPA, JADE • Agent Collaboration & Coordination • Negotiation Contract Net, Blackboard, Auctions • Soft Computing Fuzzy Logic, Neural Networks, Genetic Algorithms • Agent Architecture • Hardware Distributed Computing • Software PROSA, Multi-Agent Architectures

  4. Applications • Manufacturing & Supply Chain Sector • Wood Products Manufacturing Loewen Windows • Service Sector • Health, Banking, Insurance, Food • Transportation Infrastructure • Public, Fleet • Energy Infrastructure • Electrical – Distributed Power • Hydrogen & Alternative Fuels – Mobile & Stationary Fuel Cells

  5. Typical Rough Mill Layout ChopSaw Scanner RipSaw Scanner LUMBER WAREHOUSE Lumber

  6. Summary of Key Issues • Select most suitable jag – 2,500 choices 30-50 times / day • Assign arbor/ripping priority – 18choices 5-10 times / day • Schedule components on kickers – 5 x 1020 choices 150 times / day • Coordinate these three • Optimizing the overall performance ofthe following: • Costs • Yield • Productivity • Grade & length utilization • Order-file satisfaction • On time delivery

  7. System Architecture - for Rough Mill Production Local optimization Rip each piece of lumber into a best combination of strip by width Agent 1 Agent 2 Mediator Agent Select a best load or jag of lumber from the warehouse • Facilitator’s functions • Static optimized overall schedule • Task decomposition and allocation • System coordination and decision making • Learning Agent 3 Optimize component schedule to produce a best combination of components for each strip

  8. Applications – Three Layers • Manufacturing • Supply Chain & Factory coordination of logistics • Work-Cell coordination of production • Device intelligent autonomous devices • Energy Infrastructure • Inter Community coordination of supply & demand • Intra-Community coordination of devices • Devices • Photovoltaic cell, wind turbine, electrolyzer, reformers, storage, compressor, dispensers • Generalize to Multi-Layered Holarchy • Hierarchy of collaborative agents

  9. Intelligent Wireless MicroRouter • Multi-point, multi-hop, wireless connectivity of computers, sensors, displays, PDAs • Intelligent routing and priority scheduling over extended distances at 11-54 Mbs • Based on IEEE 802.11x protocol standards • Distributed systems implementation in JADE • In development by Intelligent Robotics Corporation and Simon Fraser University

  10. Extended Coverage using 802.11

  11. Features • Hubless • Self organizing: connectivity without centralized communication • Intelligent • Self configuring: without server or external router • Distributed scheduling of tasks by priority, time, due dates • Wireless • Protocol: IEEE 802.11b/g • Interfaces: USB2.0, RS232, IEEE 802.3 • Scalable • Additional network devices and services may be easily added • Robust • Adaptability to user demands and network failures • Secure • Advanced encryption algorithms

  12. Industrial ApplicationUtility Metering • Enables utility meters to be networked without the need for hubs • Utility meter data intelligently routed over networks with millions of nodes • Provides network management, load balancing, and priority scheduling of services • Applications also to transportation and other infrastructure systems

  13. Holon 1 Holon 2 Time Distributed Combinatorial Scheduler • Process • task performed by one agent • Operation • sequence of processes • never revisits an agent • Fully distributed algorithm • Software • multi-threaded C++ and OpenGL • being ported to Java for operation in JADE environment DCS1 DCS2

  14. Conclusions • Collaborative intelligent systems have broad applications • manufacturing resource management, planning, and control • transportation, energy, and other infrastructure systems • service systems • Collaborative intelligent systems provide • improved flexibility • reduced setup-time • higher robustness • integration of human intelligence • Collaborative intelligent systems are characterized by • reconfigurability • task level programming • high-level cooperation

  15. Further information … www.ensc.sfu.ca/irms Intelligent Robotics and Manufacturing Systems Laboratory www.ieeesmc.org IEEE-SMC Technical Committee “Collaborative Intelligent Systems” hms.ifw.uni-hannover.de Holonic Manufacturing Systems Consortium www.fipa.org Foundation for Intelligent Physical Agents www.ims.org Intelligent Manufacturing Systems Program

More Related