Multi-mobile machine platform system task allocation method for smart workshop
A technology for mobile machines and system tasks, applied in the direction of control/regulation systems, instruments, motor vehicles, etc., can solve problems such as seldom used, calculation results cannot be considered at the same time, and task allocation is unbalanced, achieving time cost and energy consumption cost Optimized effect
Active Publication Date: 2020-05-08
HOHAI UNIV CHANGZHOU
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[0004] At present, most of the multi-mobile machine platform system task allocation algorithms take minimizing the total path of the multi-mobile machine platform system as the primary goal. The situation where the machine platform waits for a long time for a mobile machine platform to return is actually inefficient
There is also a combined Single-Function-Optimization (SFO) algorithm whose primary goal is to minimize the variance of the multi-mobile machine platform system and minimize the linear combination of the total path of the multi-mobile machine platform system, but this type of algorithm has a linear combination The disadvantages of difficult weight selection and calculation results that cannot take into account both time cost and energy consumption cost; there are also multi-objective genetic optimization algorithms based on genetic algorithms, but due to the premature convergence of the genetic algorithm itself, it is easy to lose high-quality solutions and space calculations The disadvantage of excessive complexity is rarely used in practice
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Embodiment
[0170] Embodiment: Taking data set A (100 task nodes) as an example, a comparative test is carried out with the NSGA-II algorithm, image 3 It is the statistical result of the NSGA-II algorithm and the algorithm described in this patent running 100 times, where the weight parameter of the feature information is w 1 =0.85,w 2 = 0.15. It can be seen from the statistical results that compared with the NSGA-II algorithm, the algorithm described in this patent is significantly better than the NSGA-II algorithm in terms of calculating the total distance and the maximum subtask distance, and the variance statistical results show that the algorithm described in this patent is stable. Sex is significantly higher than NSGA-II.
[0171] Figure 4 , 5, and 6 are the calculation results of the algorithm described in this patent when the number of mobile robots in the test data set B (51 task nodes) is 3, 4, and 5, and the statistical results are as follows:
[0172] Table 1: Comparison...
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The invention provides a multi-mobile machine platform system task allocation method for a smart workshop. A task allocation model of an intelligent workshop multi-mobile machine platform system is established, the solution of each particle is updated by using a better solution in the non-dominated set by using a greedy strategy, meanwhile, a Hamiltonian optimization algorithm is used for eliminating non-Hamiltonian circles in the sub-paths, therefore, the time cost and the energy consumption cost of task allocation of the multi-mobile-machine-platform system are optimized, the time cost refers to the longest sub-path in each mobile machine platform in the multi-mobile-machine-platform system, and the energy consumption cost refers to the total path of the multi-mobile-machine-platform system in executing an allocation task once. According to the method, the energy consumption cost and the time cost can be effectively reduced in the task allocation process of the multi-mobile-machine-platform system, the task time of the multi-mobile-machine-platform system is shortened, and the working efficiency of the multi-mobile-machine-platform system is improved.
Description
technical field [0001] The invention relates to the field of intelligent workshop mobile machine platform control, in particular to a multi-objective optimized multi-mobile machine platform system task allocation method. [0002] technical background [0003] Goods sorting and transportation are an important part of the smart workshop and an important part of the future social Internet of Things system. For the future smart workshop, the multi-mobile machine platform system can effectively improve the efficiency of goods sorting and reduce the time for parcel handling through collaboration. However, multiple mobile machine platform systems work in the same space, which is prone to task interference and conflicts, resulting in deadlock and other problems. Therefore, the task allocation of the multi-mobile machine platform system is an important part of the smart workshop. [0004] At present, most of the multi-mobile machine platform system task allocation algorithms take mi...
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IPC IPC(8): G05D1/02
CPCG05D1/0221G05D1/0276
Inventor 魏长赟蔡帛良刘增辉倪福生蒋爽顾磊李洪彬
Owner HOHAI UNIV CHANGZHOU