Multitask allocation model of the coal foreign object sorting robot based on optimal capacity and benefit.

Robot technology has important application value in coal mines, non-coal mines, and other fields. However, the existing coal gangue multi-task allocation methods cannot be used for the coal foreign object with multi kinds, complex features, and spatiotemporal randomness, and it is difficult to solve comprehensively improve coal quality, human-machine safety and environmental pollution. Firstly, this paper constructs a multi-task allocation model of coal foreign object sorting robot (CFoSR) based on optimal capacity and benefit. The matching function, optimal capacity function and benefit function were used to calculate the environmental state matrix, and the state transfer function was designed to update the environmental state, which described the multi-task allocation problem of coal foreign object sorting. Then, a multi-evaluation index fitness function was designed that comprehensively considered the total mass of gangue sorting and the total number of sundry sorting. According to the scheduling rules such as first-in-first-out (FIFO), benefit first (BF) and shortest process time (SPT), a combined rule strategy based on genetic algorithm (GACRS) is proposed. Finally, the CFoSR simulation environment was built. The action mechanism of scheduling rules, heterogeneous manipulators, belt speed and coal flow upper limit on the multi-task allocation results of CFoSR is deeply analyzed. The simulation results show that the optimal fitness value of GACRS is 24.73% higher than that of adaptive weights based on greedy algorithm(GAW). The comparison results based on CFoSR experimental platform show that the optimal fitness value of GACRS is 14.02% higher than that of GAW. This paper provides a multi-task allocation model and its solution method for intelligent coal foreign object sorting.