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