Virtual reality based annular RGV semi-physical simulation system and non-empty running scheduling algorithm

Abstract

The invention relates to a virtual reality based annular RGV semi-physical simulation system and a non-empty running scheduling algorithm. According to the invention, an RGV three-dimensional dynamic simulation system, an RGV management control system and a scheduling algorithm are designed by adopting a virtual reality technology, and semi-physical simulation running of an RGV system is realized. The virtual reality based annular RGV semi-physical simulation system and the non-empty running scheduling algorithm have the advantages that a low-cost and high-efficiency debugging method is provided for studying the scheduling algorithm and trial operations of a new task; the non-empty running scheduling algorithm achieves global quasi-optimization and local optimization and meets actual requirements; the virtual reality based annular RGV semi-physical simulation system realizes communication with RGV carrying vehicles of different communication forms and has universality; and the scheduling algorithm adopts an open design, reserves interfaces for loading other algorithms, can allow loading of other scheduling algorithms and can be implemented by adopting a dynamic library mode.

Description

technical field [0001] The invention relates to a semi-physical simulation system, in particular to a virtual reality-based circular RGV semi-physical simulation system and a no-idle-run scheduling algorithm. Background technique [0002] Under the existing technology, the overall layout of the RGV system is described based on the specific tasks of a chemical plant, as shown in Figure 1. [0003] The system mainly consists of a circular track, various transport vehicles that do not run on the track, a pick-up station, a delivery station, and goods to be transported. In the figure, each transport vehicle 1-10 picks up the goods from the pick-up station 1-6, and transports them to the delivery station 7-10 along the track. One transport task is performed by one transport vehicle, including walking with an empty vehicle, parking to pick up goods, loading There are 5 links of moving goods, parking and unloading, and empty vehicles leaving. Since all trucks walk along the track...

AI Technical Summary

Problems solved by technology

[0008] (2) Then P2 has a task and can only assign the task to V2;

[0009] (3) V2 has to wait for V1 to finish picking up the goods, and can only pick up the goods after passing through P2, which may take a long time

[0016] At present, many people have proposed many scheduling algorithms based on optimization theory, such as: improved quantum particle swarm optimization algorithm, hybrid genetic algorithm, self-learning fuzzy neural network control method, etc. These algorithms are perfect in theory, but in specific applications Sometimes there may be poor ability to deal with special situations or emergencies, and the algorithm is complex, difficult to program and verify, and some algorithms need to be improved through online learning, affecting normal production

[0017] 4. The complexity of algorithm debugging

[0021] 3) Due to the complexity of the scheduling rules, it is also difficult for the debugger to analyze correctly;

[0022] 4) If an operation error occurs, it may require complex manual processing, such as manual unloading, manual reset, etc., and accidents may occur in severe cases

However, the usual simulation algorithm can only give mathematical descriptions such as the content, position, speed and other data and curves of the tasks performed by each truck.

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