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Abstract The rapid advancement of artificial intelligence and robot technology has spurred the proposal and innovation of a coal gangue sorting robot system (CGSRS) paradigm. The time-varying raw coal flow (TVRCF) with multi-scene and full working conditions affects the gangue queue. Configuring the CGSRS scheme correctly is combative. The field environment puts forward higher requirements for the time complexity of the CGSRS multi-task allocation strategy. Therefore, this paper proposes a scheme evaluation method of the CGSRS with time-varying multi-scenario based on deep learning. Firstly, the gangue queue data set of multi-scene and full-condition TVRCF was obtained according to the belt speed, the maximum coal flow, and the uncorrelated nonlinear changes of coal flow and gangue content. The CGSRS scheme is established based on robot number and rule combination, and the multi-task allocation strategy is adjusted to generate the labels of the gangue queue. Then, the RGB sample set is established based on the labels of the gangue queue. The CGSRS scheme evaluation model is trained based on DenseNet. Finally, the CGSRS scheme evaluation method was designed to realize the prediction of a random gangue queue. In this paper, the CGSRS scheme evaluation model, the stability of the solution, and the comparison of methods are carried out. Experimental results show that the solution of the CGSRS scheme evaluation model is accurate and stable. The time complexity is significantly reduced and very stable. The CGSRS scheme evaluation method is applied to the CGSRS multi-task allocation problem, and the stability of the solution is not affected by the data. It is significantly better than the multi-task allocation strategy. The proposed method is the first attempt to apply deep learning to a multi-task allocation problem in CGSRS.

The multi-task allocation has become the key to be solved urgently in applying a coal gangue sorting robot system (CGSRS) in raw coal with high throughput. This paper constructs a multi-task allocation framework of CGSRS for time-varying raw coal flow (TVRCF), including task decomposition, task allocation, and collaborative strategy. Establish the TVRCF decomposition model using the tumbling time window method to obtain the gangue model. A multi-task allocation model based on M/M/s queuing model with impatient customers is proposed because arrival time, granularity, and spatial position of the gangue model are uncertain. By introducing trajectory function and designing benefit function, to solve environment states of sorting area. Creating an iterative mechanism for environment states based on the infinite Markov process, using service time and arrival time interval. An adaptive weights rule based on the greedy algorithm (GAW) is proposed to improve the benefit of multi-task allocation. Simulating 18 groups of TVRCF with 9 productivity. Experimental results show that the proposed framework suits the multi-task allocation of CGSRS to TVRCF. The GAW rule always ensures the optimal sorting rate, the near-optimal task completion success rate, and the manipulator utilization rate. In addition, the stability of the GAW rule is best. [ABSTRACT FROM AUTHOR]