To realize unmanned pose detection of a coalmine boom-type roadheader, an ultra-wideband (UWB) pose detection system (UPDS) for a roadheader is designed, which consists of four UWB positioning base stations and three roadheader positioning nodes. The positioning base stations are used in turn to locate the positioning nodes of the roadheader fuselage. Using 12 sets of distance measurement information, a time-of-arrival (TOA) positioning model is established to calculate the 3D coordinates of three positioning nodes of the roadheader fuselage, and the three attitude angles (heading, pitch, and roll angles) of the roadheader fuselage are solved. A range accuracy experiment of a UWB P440 module was carried out in a narrow and closed tunnel, and the experiment data show that the mean error and standard deviation of the module can reach below 2 cm. Based on the TOA positioning model of the UPDS, we propose a fusion-positioning algorithm based on a Caffery transform and Taylor series expansion (CTFPA). We derived the complete calculation process, designed a flowchart, and carried out a simulation of CTFPA in MATLAB, comparing 1000 simulated positioning nodes of CTFPA and the Caffery positioning algorithm (CPA) for a 95 m long tunnel. The positioning error field of the tunnel was established, and the influence of the spatial variation on the positioning accuracy of CPA and CTFPA was analysed. The simulation results show that, compared with CPA, the positioning accuracy of CTFPA is clearly improved, and the accuracy of each axis can reach more than 5 mm. The accuracy of the X-axis is higher than that of the Y-and Z-axes. In section X-Y of the tunnel, the root mean square error (RMSE) contours of CTFPA are clear and orderly, and with an increase in the measuring distance, RMSE increases linearly. In section X-Z, the RMSE contours are concentric circles, and the variation ratio is nonlinear.

Based on the Lagrange equation in system dynamics, aiming at the horizontal cutting process, the dynamical coupling model of boom-type roadheader’s body pose was established. According to input problem of solving the model, a calculation method of the cutting head load was proposed, and the relationship between the cutting head load and pressure of the driving cylinders and swing angle of the cutting arm was obtained through simulating analysis. The simulation model was established to solve the dynamical coupling model. The cutting head load, horizontal swing angle of the cutting arm, and dip angle of coal seam were regarded as independent variables to perform changing parameter analysis in variations of the body pose. The field experiment was carried out, and the measured data is basically consistent with the simulation values. The results show that lateral displacement of the body can reach up to 6.5 cm, backward displacement can reach up to 5.2 cm, floor-based quantity can reach up to 11 cm, pitch angle of the body can reach up to 7.8°, and roll angle can reach up to 2.1°. Variations of the body pose parameters are influenced greatly by the cutting head load, while the influence from horizontal swing angle of the cutting arm and dip angle of coal seam is slighter. Among the pose parameters, floor-based quantity and pitch angle of the body vary relatively greatly, which tend to seriously influence forming quality of the roadway and should be mainly considered in deviation rectification of the roadheader’s body pose.

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Boom-type roadheader is the most widely used equipment in the fully mechanized excavation face of coal mine. Under complicated operation conditions of underground coal mine, multibody large-scale displacement phenomenon of roadheader will occur during cutting process. Specifically, the position and attitude of the body are changing continuously, and radial runout phenomenon of the cutting arm will take place, which will seriously reduce the overall stability of roadheader and drivage efficiency of the roadway. Focused on the vertical cutting condition, the coupling dynamical model of position and attitude of the body is established based on Lagrange equation. Combined with the calculated cutting load, the dynamical model is solved through Simulink simulation method, and the solving results are analysed under different influence factors. Consequently, the response regularities of position and attitude of the body under influence of different factors are obtained. In the similar way, the dynamical model of radial runout quantity of the cutting arm is established, and the dynamic response of radial runout of the cutting arm during vertical cutting process is obtained as well. Finally, the simulation analysis results are validated through field experiments. The research work of this paper provides useful theoretical basis for deviation rectification control of the multibody structures of roadheader, fills the vacancy of theoretical study in this aspect, and contributes to improving the dynamics theory of mining machinery.

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An adaptive control method to improve the cutting head speed of roadheaders using multisensor information is proposed, so as to solve the problems of low cutting efficiency and low intelligence of roadheaders during underground tunnelling. The operation of a roadheader is analysed, and a control strategy for its cutting head speed is proposed. In addition, the cutting head speed is categorised into five gears according to the multisensor information of different cutting states. The controller for speed estimation is designed using a back propagation neural network optimised using an improved particle swarm optimisation algorithm. A control system is established in MATLAB to analyse the effectiveness of the method. The simulation results show that an IPSO-BP controller has the best control effect and can attain the target speed. The response time was lower than those of fuzzy logic controllers and traditional PI controllers by 46% and 68%, respectively, and the overshoot decreased by 4.69% and 12.19%, respectively. Furthermore, experimental research verified the effectiveness of this method. This method can adaptively adjust the cutting head speed of a roadheader using multisensor information and is important (both theoretical and practically) for extending the service life of roadheaders and improving tunnelling efficiency.

This paper proposes a path correction scheduling strategy for the underground mining boom roadheader by ably combining a back propagation (BP) neural network and state estimation. First, a pose deviation-based tracking model is designed for the roadheader, and it is then further studied and optimized by incorporating the benefits of BP neural networks into the model adaptation. Considering the fact that there is skidding between tracks on the ground and errors during the instant pose detection of the roadheader underground, singular value decomposition (SVD)–Unscented Kalman filtering is applied to estimate the real pose deviation, based on the summarized distribution regularities of the track skidding ratios and the pose detection errors, instead of complicated analysis mechanisms. The BP neural network and states estimation are well combined in structure, enabling this scheduling strategy to update the control law and revise the control instruction simultaneously in the procedure. The proposed path tracking model for the roadheader is simple and clear, without adding extra devices or massive algorithms, which is attractive in terms of industrial use. The path tracking simulations show that this proposed strategy achieves path tracking well in different scenarios and is of high adaptability when facing complex trajectory while still giving stable control instructions for the roadheader.

Trajectory planning and tracking control algorithm based on a position and orientation deviation model are proposed to achieve path correction for the mining boom road-header working underground. The proposed strategy is assessed to be feasible and potentially practicable by simulations, from which the following statements are summarized. Firstly, trajectory planning is necessary since different trajectories correspond to different scenarios about undesirable excavation space, slipping level, and power consumption. Secondly, using the proposed tracking control algorithm, the road-header is guided back onto the expected path in limited adjusting steps, with smoothly varying rotation speeds of the driving wheels and regularly reducing pose errors. Lastly, it shows that by implying the SVD-unscented Kalman filtering in the tracking control, the adverse impacts of process and measurement noises are appeased obviously. This research provides an advisable modeling and valuable simulation for the road-header to achieve robotic operation underground.