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Traffic management and control of automated guided vehicles using artificial neural networks

By Manuel Romano dos Santos Pinto Barbosa


An industrial traffic management and control system based on Automated Guided Vehicles faces\ud several combined problems. Decisions must be made concerning which vehicles will respond, or are\ud allocated to each of the transport orders. Once a vehicle is allocated a transport order, a route has to\ud be selected that allows it to reach its target location. In order for the vehicle to move efficiently along\ud the selected route it must be provided with the means to recognise and adapt to the changing\ud characteristics of the path it must follow. When several vehicles are involved these decisions are\ud interrelated and must take into account the coordination of the movements of the vehicles in order to\ud avoid collisions and maximise the performance of the transport system. This research concentrates on\ud the problem of routing the vehicles that have already been assigned destinations associated with\ud transport orders.\ud In nearly all existing AGV systems this problem is simplified by considering there to be a fixed route\ud between source and destination workstations. However if the system is to be used more efficiently,\ud and particularly if it must support the requirements of modern manufacturing strategies, such as Justin-\ud Time and Flexible Manufacturing Systems, of moving very small batches more frequently, then\ud there is a need for a system capable of dealing with the increased complexity of the routing problem.\ud The consideration of alternative paths between any two workstations together with the possibility of\ud other vehicles blocking routes while waiting at a particular location, increases enormously the number\ud of alternatives that must be considered in order to identify the routes for each vehicle leading to an\ud optimum solution. Current methods used to solve this type of problem do not provide satisfactory\ud solutions for all cases, which leaves scope for improvement. The approach proposed in this work\ud takes advantage of the use of Backpropagation Artificial Neural Networks to develop a solution for the\ud routing problem. A novel aspect of the approach implemented is the use of a solution derived for\ud routing a single vehicle in a physical layout when some pieces of track are set as unavailable, as the\ud basis for the solution when several vehicles are involved. Another original aspect is the method\ud developed to deal with the problem of selecting a route between two locations based on an analysis of\ud the conditions of the traffic system, when each movement decision has to be made. This lead to the\ud implementation of a step-by-step search of the available routes for each vehicle.\ud Two distinct phases can be identified in the approach proposed. First the design of a solution based on\ud an ANN to solve the single vehicle case, and subsequently the development and testing of a solution\ud for a multi-vehicle case. To test and implement these phases a specific layout was selected, and an\ud algorithm was implemented to generate the data required for the design of the ANN solution.\ud During the development of alternative solutions it was found that the addition of simple rules provided\ud a useful means to overcome some of the limitations of the ANN solution, and a "hybrid" solution was\ud originated. Numerous computer simulations were performed to test the solutions developed against\ud alternatives based on the best published heuristic rules. The results showed that while it was not\ud possible to generate a globally optimal solution, near optimal solutions could be obtained and the best\ud hybrid solution was marginally better than the best of the currently available heuristic rules

Topics: QA76, HE
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  1. (1990). 42: Khanna, T.; "Foundations of Neural Works",
  2. (1988). 82: Sedgewick, Robert; "Algorithms", 2nd Edition,
  3. A hybrid modelling approach to the design of an AGV-based material handling system for an FMS", doi
  4. A model for the design of zone control automated guided vehicle systems", doi
  5. A modular framework for the design of material flow systems", doi
  6. (1989). A Neural Network Approach to Multi-vehicle Navigation", doi
  7. A Neural Network for Resource Allocation", Conf.
  8. (1996). A neural network model for scheduling problems", doi
  9. (1993). Advanced Methods in Neural Computing",
  10. (1991). Algorithm of Fuzzy Dynamic Programming in AGV Scheduling",
  11. (1991). Algorithms from P to NP, vol.!,
  12. An algorithm for routeing control of a tandem automated guided vehicle system", doi
  13. (1987). An analogue approach to the travelling salesman problem using an elastic net method", doi
  14. (1990). An Elastic Net Solution to Obstacle Avoidance Tour Planning", doi
  15. An expert neural network system for dynamic job shop scheduling", doi
  16. (1987). An Introduction to Computing with Neural Nets", doi
  17. (1990). An Introduction to Neural Computing", doi
  18. (1986). and the PDP Research Group; "Parallel Distributed Processing. doi
  19. (1990). Artificial Neural Systems, Foundations, Paradigms, Applications and Implementations. doi
  20. (1987). Automation, Production Systems, and Computer Integrated Manufacturing", doi
  21. (1991). Autonomous Mobile Robots, Vehicles with Cognitive Control", World Scientific Publishing Co. doi
  22. (1992). Behavior-Based Mobile Robot using Active Sensor Fusion", doi
  23. Capabilities and Training of Feedforward Nets",
  24. Cell Placement by Self-Organisation", doi
  25. Complexity of the AGV shortest path and single-loop guide path layout problems", doi
  26. (1994). Computer Integrated Manufacturing and Engineering", doi
  27. (1991). Computer Systems That Learn: Classification and Prediction from Statistics, Neural Nets, Machine Learning, and Expert Systems",
  28. (1979). Computers and Intractability, A Guide to the Theory of NP-Completeness", doi
  29. (1989). Coordination of movements by self-organizing neural networks",
  30. (1989). Decentralized Control of Automated Guided Vehicles on a Simple Loop", doi
  31. Design of an automated guided vehicle-based material handling system for a flexible manufacturing system", doi
  32. Design of material transportation system for tandem automated guided vehicle systems", doi
  33. (1991). Diodelling of an automated guided vehicle system (AGVS) in a just-in-time (JTT) environment",
  34. (1996). Dynamic control systems for AGVs", doi
  35. Esperimental investigation of FMS machine and AGV scheduling rules against the mean flow-time criterion", doi
  36. (1992). Experiments with a Mobile Robot Operating in a Cluttered Unknown Environment", doi
  37. (1990). Flexible Manufacturing Cells and Systems in CIM", CiMware Ltd. doi
  38. Forming cells to eliminate vehicle interference and system locking in an AGVS",
  39. (1994). Generation of Optimal Routes in a Neural Network Based AGV Controller", doi
  40. (1989). Genetic Algorithms in Search, Optimization and Machine Learning", doi
  41. GR.; 'A review of scheduling rules in flexible manufacturing systems", doi
  42. Heuristic, storage space minimization methods for facility layouts served by looped AGV systems", doi
  43. High-order Hopfield and Tank optimization networks", doi
  44. (1994). Hopfield Net Generation, Encoding and Classification of Temporal Trajectories", doi
  45. I.; "riulti-criteria operational control rules in flexible manufacturing systems (FAMSs)",
  46. (1988). Implementing Flexible Manufacturing Systems",
  47. (1991). Introduction to the theory of neural computation". doi
  48. (1991). Learning Conditional Effects of Actions for Robot Navigation", doi
  49. Local Path Optimisation of Free-ranging Automated Guided Vehicles", doi
  50. Machining and material flow system design for minimum cost production'. doi
  51. (1990). Mobile Robot Control by a Structured Hierarchical Neural Network", doi
  52. (1993). Mobile Robot Navigation Using Neural Networks and Nonmetrical Environment Models", doi
  53. (1991). Modified Hopfield Neural Networks for Retrieving the Optimal Solution", doi
  54. (1993). Multiple Job Scheduling With Artificial Neural Networks", doi
  55. (1995). Neural Network Dynamics for Path Planning and Obstacle Avoidance", doi
  56. (1994). Neural Network Toolbox User's Guide", The MathWorks Inc.,
  57. Neural Networks Application in Autonomous Path Generation for Mobile Robots", doi
  58. Neural Networks For Automated Vehicle Dispatching", doi
  59. (1992). Neural Networks: Algorithms, Applications, and Programming Techniques. ", doi
  60. Neural" Computation of Decisions in Optimization Problems",
  61. (1994). NeuralWare, Inc.; "Neural Computing, A Technology Handbook for Prof. II/Plus and NeuralWorks Explorer",
  62. (1992). NeuralWare, Inc.; "Using NeuralWorks; an extended tutorial for NeuralWorks Prof. IUPlus and NeuralWorks Explorer",
  63. (1990). NeuralWorks: Networks II",
  64. On the path layout and operation of an AGV system serving an FMS", doi
  65. On the Stability of the Travelling Salesman Problem Algorithm of Hopfield and Tank". doi
  66. Optimal flow path design of unidirectional AGV systems", doi
  67. Path-planning For Multiple Mobile Robots By Using Genetic Algorithms", doi
  68. (1989). Planning and Scheduling in a Flexible Manufacturing System Using a Dynamic Routing Method for Automated Guided Vehicles", IFER_ doi
  69. (1991). Practical Path Planning among Movable Obstacles", doi
  70. (1989). Real-Time Obstacle Avoidance for Fast Mobile Robots", doi
  71. Route selection and flow control in a multi-stage manufacturing system with heterogeneous machines within stages", doi
  72. (1986). Simple "Neural" Optimization Networks: An A/D Converter, Signal Decision Circuit, and a Linear Programming Circuit", doi
  73. Solving the AGV Problem via a Self-Organizing Neural Network", doi
  74. Study of AGVS design and dispatching rules by analytical and simulation methods", doi
  75. The Use of Neural Networks in Determining Operational Policies for Manufacturing Systems", doi
  76. (1991). The Vector Field Histogram-Fast Obstacle Avoidance for Mobile Robots", doi
  77. (1996). Traditional heuristic versus Hopfield neural network approaches to a car sequencing problem", doi
  78. (1988). Traffic Control of Multiple Robot Vehicles. ", doi
  79. W: C.; "A load-routeing problem in a tandemconfiguration automated guided-vehicle system", doi
  80. (1992). Which one is responsible for WIP: the workstations or the material handling system? ", doi

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