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In the vision measurement system for cutting head position of cantilever roadheader based on infrared LED characteristics, the stability of external calibration and the accuracy of infrared LED spot center extraction have an important influence on the cutting head position detection accuracy. The existing external parameter calibration method relies on experience to swing the cutting arm to the center position (unknown), and the calibration results have large fluctuations. In order to solve the above problem, a multi-point fixed external parameter calibration method is proposed. This method controls the cutting arm of the roadheader to swing to the four known limit positions of upper left corner, upper right corner, lower left corner and lower right corner respectively, and collects the target images. The method calculates the value of the external parameter matrix, which can improve the stability of the external parameter calibration effectively. The existing gray-scale centroid method uses the grayscale value of the pixel as the weight to calculate the spot centroid. And the accuracy can only reach the pixel level, which only roughly meets the practical application requirements. In order to solve this problem, a sub-pixel edge detection algorithm is proposed to improve the spot center extraction method. Firstly, the gray-scale centroid method is used for coarse extraction of the spot center. Secondly, the sub-pixel level edge detection algorithm is used to find the sub-pixel level edge coordinates. Finally, the least squares method is used to fit the spot center to achieve accurate extraction of the spot center. The experimental results show that the improved spot center extraction method reduces the maximum measurement error of the target LED lamp spacing from 3.2 mm to 1 mm, which improves the detection accuracy. The external parameter matrix obtained by the multi-point fixed external parameter calibration method is relatively stable, the maximum variation of displacement in the translation matrix is 15 mm, and the maximum variation of angle in the rotation matrix is 1°. Before the improvement of the vision measurement system, the measurement error of the cutting head swing angle was within ［-1.2°,1.7°］. After the improvement, the error of the horizontal swing angle of the cutting head is within ［-0.5°,0.5°］ and the error of the vertical swing angle is within ［-0.6°,0.6°］. The results show that the improved method improves the detection accuracy of the cutting head swing angle effectively.

Aiming at problems of insufficient positioning and control accuracy of tunneling robot in environment with high dust and low illumination of coal mine, a deviation correction control system of rapid tunneling robot based on vision measurement is designed. The explosion-proof camera installed on rapid tunneling robot is used to collect image of laser pointing instrument at the rear, and the collected image information is transmitted to explosion-proof computer through Ethernet. The explosion-proof computer is used to preprocess the image and then the pose of the rapid tunneling robot is calculated according to pose calculation model. The calculated pose information is compared with designed roadway axis information, and the pose deviation between the pose of the rapid tunneling robot and the roadway design axis is calculated. According to type and size of the pose deviation, different control strategies are adopted to calculate corrected control quantity and control instructions are output. The control command controls spool movement of solenoid proportional valve to control telescopic oil cylinder. According to the different expansion amount of the telescopic cylinder in each area, the pose of the rapid tunneling robot is adjusted to realize deviation correction control. The experimental results show that the average deviation of vision meosurement accuracy in X direction is 21.334 mm, that in Y direction is 34.154 mm, that of yaw angle is 0.493 ° and the error of deviation correction control in X direction is less than 30 mm, which meets requirements of actual working conditions.

Existing remote cutting control methods of roadheader are mostly based on video monitoring and planar information, and roadway section forming quality depends on manual operation experience with low reliability. For the above problems, digital twin driven remote automatic cutting control technology of roadheader was researched, and implementation process of digital twin driven automatic cutting of roadheader was introduced from aspects of data sensing, data driving, data transmission and remote automatic cutting control. Position and attitude data of roadheader is obtained by use of vision measurement technology and tunneling environment data is gathered by gas and other sensors, which form data source of data twin driving. Vitual model of roadheader and virtual scene are established, and action programming and coupling between the virtual model and virtual scene are finished. Interaction between roadheader and the virtual model is realized by normal and inverse kinematics of roadheader. 'S' shape cutting trajectory of roadway section is planned based on cubic polynomial method, and linear integral feedback sliding mode controller is designed to control cutting trajectory. MySQL database is taken as medium of data interaction, and remote data transmission is realized through underground industrial ethernet ring network and mine-used 5G technology. The experimental results show that the cutting head of roadheader can stably follow the planed trajectory, and position and attitude of cutting head of roadheader in virtual interface is accordant with the actual ones at the same moment.

In view of low maintenance efficiency and long cycle caused by insufficient on-site maintenance knowledge and professional guidance for mine-used equipments, and virtual reality and augmented reality technologies are generally realized on head mounted device, mobile phones or tablet computers with good mobility, but users can only rely on interactive handle and touch screen to interact, and visual experience lacks authenticity, a maintenance guidance system of mine-used equipments based on mixed reality was designed. Based on Unity3D virtual reality platform, the system adopts mixed reality device HoloLens to real-timely display maintenance instruction schemes, which realizes functions of virtual simulation of mine-used equipments principle, equipments management based on QR code, equipments maintenance guidance and remote expert collaboration. HoloLens visually presents maintenance instructions to maintenance personnel through virtual text, virtual device models, animations and voice prompts. Maintenance personnel can interact with HoloLens through line-of-sight tracking, voice interaction and gesture recognition. Application example of shearer gear wear fault maintenance guidance verifies practicality and effectiveness of the system.

The Real Driving Emission test (RDE) required in China 6 regulation (GB 18352.6-2016) followed the EU 6 regulation (EC 2016/646), where cold start is excluded in emission calculation. Since cold start is an important stage of pollutant formation, the European Union had developed further requirements for cold start in EU 6 regulation (EC 2017/1154 and EC 2018/1832), which created generation gaps between China 6 and EU 6 RDE test requirements. This work is based on real RDE tests on public road and repetition tests on Chassis dynamometer with relevant cold start requirements of EC 2017/1154 described and demonstrated, the data evaluation method difference between China 6 regulation including cold start and new requirements in EC 2018/1832 are compared, and the following cold start requirements of China 6 RDE regulation is discussed and proposed.