ZHANG Jie¹，XIA Rui¹，LI Bo¹，WANG Xuewen¹，LI Juanli¹，XU Wenjun^1，2

（1. Faculty of Mechanical and Vehicle Engineering， Taiyuan University of Technology， Taiyuan 030000， China； 2. Shanxi Liangjie Digital Technology Corporation， Taiyuan 030000， China）

Abstract

Objective Gangue is the waste and impurity produced during the process of coal mining and handling. Separating the coal gangue can reduce environmental pollution， improve energy efficiency， and provide economic benefits. Intelligent coal gangue sorting commonly employs robotic sorting and air-blowing separation. However， the use of a manipulator brings high costs， complexity， and failure rates. Additionally， a single air-blowing separation is not adaptable to gangue with significant differences in quality. By analysing the working characteristics of the two separation methods and designing a synergistic sorting system， the adaptability of the gangue sorting system can be improved， and the equipment cost can be reduced.

Methods This paper proposed a collaborative sorting model using heterogeneous robots. The model combined deep reinforcement learning with heterogeneous sorting robots. The continuous sorting process of coal gangue was divided into a number of task segments. Overall planning was carried out for each task segment to develop a feasible cooperative work scheme for actuators. The third task set for gangue sorting and actuator collection was presented. To meet the continuity requirements for gangue sorting， we proposed splitting the continuous task into several subsets. Tasks were allocated using a buffer between identification and sorting. Furthermore， this paper proposed a reinforcement learning decision-making framework based on LSTM-DQN （long short term memory， LTSM； deep Q network， DQN） to design an interaction environment for reinforcement learning during the coal gangue sorting process. The framework includes state space， action space， and reward function. Additionally， a cross-attention mechanism was used to compute the actuator preferences for tasks， which accelerated the model convergence speed. Also， this paper constructed a core network of the model and introduced LSTM to handle state sequences for temporal and longterm dependencies. The DQN structure was then optimized. Samples with different gangue rates were set up， and the proposed method was compared with the sequential allocation model across different gangue rates and band speeds to demonstrate its superiority.

Results and Discussion Based on the proposed LTSM-DQN model， a method for sorting coal gangue using heterogeneous robots was developed. Six groups of samples with varying gangue rates were prepared to simulate different workloads. The experiment showed that the LTSM-DQN model is effective for task assignment in heterogeneous robot cooperation. Fig. 7 shows that various loads can be converged within 500 rounds of training. Samples with gangue rates ranging from 4. 73% to 30. 45% are sorted using the LTSM-DQN-based sorting model， which can limit the reduction of sorting efficiency to within 8%. When compared to the traditional sequential assignment， the sorting model based on LTSM-DQN can improve sorting efficiency by 2. 41% to 8. 98% under a gangue rate of 21. 61% and an adjusted belt speed of 0. 4~0. 6 m/s， as shown in Table 2. This improvement is significant and demonstrates the effectiveness of the LTSM-DQN model.

Conclusion A collaborative method for sorting heterogeneous robots and an optimised task allocation strategy using a reinforcement learning algorithm are proposed to achieve efficient and cost-effective sorting. The experiment demonstrates that this collaborative sorting method for coal gangue sorting can maintain the overall sorting benefit of the system at over 90% under different loads and is less affected by the belt speed compared to the traditional allocation square method， under different belt speeds and gangue content conditions. The cooperative sorting method is expected to evolve into the pneumatic sorting method and the multi-mechanic cooperative operation method. The system will be optimized in terms of multi-mechanic cooperation， air blowing， and robot cooperation. Reasonable and customized expansion will be carried out according to the actual needs of the mining area to satisfy specific sorting needs in a cost-effective manner.

Keywords：heterogeneous robots；cooperative sorting；reinforcement Learning；long short term memory；deep Q network

Get Citation:ZHANG J， XIA R， LI B， et al. Heterogeneous robot coal gangue collaborative sorting method based on long short term memory-deep Q network［J］. China Powder Science and Technology，2024，30（3）：28−38.

Received:2024-01-01.Revised:2024-03-15,Online:2024-04-22。

Funding Project:国家自然科学基金项目，编号 ：52204149；山西省自然科学基金项目，编号：202103021223080，202203021221051。

First Author:张杰（1998—），男，硕士生，研究方向为智能煤矸分选。E-mail：zhangjie815921@163.com。

Corresponding Author:李博（1979—），男，副教授，硕士生导师，三晋英才，研究方向为煤矸智能分选与控制。E-mail：libo@tyut. edu. cn。

DOI：10.13732/j.issn.1008-5548.2024.03.003

CLC No:TP23； TH6； TB4               Type Code:A

Serial No:1008-5548（2024）03-0028-11