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 proposes the collaborative sorting hardware composition of heterogeneous robots. The paper combines deep reinforcement learning with the heterogeneous sorting robot of coal gangue， discretizing the continuous sorting process of coal gangue into a number of task segments. Overall planning is carried out for each task segment to give a feasible actuator cooperative work scheme. The third task set for gangue sorting and actuator collection is presented. To meet the continuity requirements of gangue sorting， we propose splitting the continuous task into several subsets. We allocate tasks using a buffer between the identification and sorting processes. Fourthly， this paper proposes a reinforcement learning decision-making framework based on LSTM-DQN （long short term memory， LTSM； deep Q network， DQN） to design the interaction environment of the model for reinforcement learning in the coal gangue sorting process. The framework includes the state space， action space， and reward function. Additionally， a cross-attention mechanism is used to compute the preference scores of different actuators for the task， which accelerates the convergence speed of the model. Fifthly， this paper constructs the core network of the model and introduces LSTM to handle state sequences with temporal and long-term dependencies. The DQN network structure is then optimized. Samples with different gangue rates are set up， and the proposed method and the sequential allocation model are compared in different gangue rates and different band speeds to reflect the superiority of the proposed method.

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