Comparison of Routing Strategies for AGV Systems using Simulation

In automated transport systems, the origin-destination combinations are normally connected through a fixed layout, not representing the shortest path. The flexibility of these systems is limited and often the infrastructure is not optimally used. With the introduction of more powerful onboard computers and advanced sensor technology, the positioning and navigating possibilities of AGVs increased. However the routes, although virtual, are still fixed. A new step ahead would be to determine each path dynamically. This would use the free ranging capacities of AGVs to its full potential. In this paper, the benefits of the dynamic free ranging approach are investigated; a simulation model on the strategic level is presented that compares several common fixed layouts with the shortest connection approach. Naturally, the avoidance of collisions plays a central role. It is concluded that dynamic free ranging has high potential in terms of transport capacity of the resulting system

[1]  J. T. Shwartz,et al.  On the Piano Movers' Problem : III , 1983 .

[2]  Jaap A. Ottjes,et al.  A SIMULATION MODEL FOR AUTOMATED CONTAINER TERMINALS , 2000 .

[3]  Serge P. Hoogendoorn,et al.  Pedestrian route-choice and activity scheduling theory and models , 2004 .

[4]  Osamu Takahashi,et al.  Motion planning in a plane using generalized Voronoi diagrams , 1989, IEEE Trans. Robotics Autom..

[5]  S. LaValle.  Rapidly-exploring random trees : a new tool for path planning , 1998 .

[6]  Rajan Batta,et al.  Dynamic conflict-free routing of automated guided vehicles , 1999 .

[7]  Jean-Claude Latombe,et al.  Nonholonomic multibody mobile robots: Controllability and motion planning in the presence of obstacles , 2005, Algorithmica.

[8]  P. van Turennout.  Autonomous Motion on Wheels , 1994 .

[9]  L. Dubins.  On Curves of Minimal Length with a Constraint on Average Curvature, and with Prescribed Initial and Terminal Positions and Tangents , 1957 .

[10]  Serge P. Hoogendoorn,et al.  Pedestrian Travel Behavior Modeling , 2005 .

[11]  Gary Hammond,et al.  Agvs at Work: Automated Guided Vehicle Systems , 1986 .

[12]  Rolf H. Möhring,et al.  Conflict-free Real-time AGV Routing , 2004, OR.

[13]  J. Davenport.  A "Piano Movers" Problem. , 1986 .

[14]  Ralf W. Seifert,et al.  Evaluation of AGV routing strategies using hierarchical simulation , 1995, WSC '95.

[15]  J. Schwartz,et al.  On the “piano movers'” problem I. The case of a two‐dimensional rigid polygonal body moving amidst polygonal barriers , 1983 .

[16]  Bhaba R. Sarker,et al.  An overview of path design and dispatching methods for automated guided vehicles , 1998 .

[17]  G. Lodewijks,et al.  Dynamic Free Range Routing for Automated Guided Vehicles , 2006, 2006 IEEE International Conference on Networking, Sensing and Control.

[18]  Oussama Khatib,et al.  Real-Time Obstacle Avoidance for Manipulators and Mobile Robots , 1985, Autonomous Robot Vehicles.

[19]  G. Swaminathan.  Robot Motion Planning , 2006 .

[20]  Jaap A. Ottjes,et al.  TOMAS: TOOL FOR OBJECT-ORIENTED MODELING AND SIMULATION , 2000 .

[21]  Mark B. Duinkerken,et al.  TRAVIS an engineering tool to animate and validate AGV-systems , 2001 .

[22]  G. Lodewijks,et al.  Developing an AGV motion controller using simulation, emulation and prototyping , 2005, IEEE Proceedings. Intelligent Vehicles Symposium, 2005..

[23]  Richard M. Murray,et al.  A motion planner for nonholonomic mobile robots , 1994, IEEE Trans. Robotics Autom..

[24]  Chelliah Sriskandarajah,et al.  Design and operational issues in AGV-served manufacturing systems , 1998, Ann. Oper. Res..

[25]  Jaap A. Ottjes,et al.  Intelligent infrastructures: distributed intelligence in transport system control - an illustrative example , 2004, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583).