| Due to the harsh environment in coal mines and frequent safety accidents,the intelligent and unmanned mining has become an inevitable trend of technological development at the comprehensive mining surface.It is of great significance to improve the coal production efficiency,reduce the working intensity and guarantee the personnel safety.As the most important part,the roadway accurate excavation is the foundation of the mine construction,the mining area ventilation,the coal mining and transportation.Therefore,in order to achieve the roadway intelligent construction,we need to accomplish the roadheader automatical control in the first place.During the boom-type roadheader working process,the walking mechanism drives the tracks to push the fuselage forward,and the cutting mechanism changes the cutting head position through hydraulic cylinders.It finally completes the tunnel drilling operation under the cooperation of above two.Hence,for the purpose of realizing the roadway section automatic cutting as required,it is necessary to solve the problem of the machine body pose perception and the cutting head positioning.With the fast development of science and technology,advanced positioning and measurement techniques have been gradually applied into this field.After analyzing various measuring methods,this paper firstly studies the roadheader body position and pose detection technique to determine the coordinate base point for the cutting head location in the tunnel space.Then,a binocular system is designed for cutting head positioning based on contours recognition to acquire the 3D coordinates of cutting head relative to the roadheader body.Finally,a boom-type roadheader pose perception and positioning system is proposed to realize the real-time location monitoring of the machine body and the cutting head in the tunnel coordinate system.The main work and achievements are as follows:(1)By comparing and analyzing existing researches,the overall scheme is proposed based on machine vision technique for the roadheader pose perception and positioning.Firstly,the pose detection coordinate systems are constructed in the tunnel space according to the machine mechanical structure.Afterwards,for the first time,five parameters are chosen to describe the roadheader body position,which are the deflection angle ?,the pitch angle ?,the roll angle ?,the horizontal offset displacement ?x and the vertical offset displacement ?z.Then,a double cross laser instrument is used to measure these parameters.Finally,based on the obtained position,the cutting head location can be precisely achieved by using a binocular vision system in the tunnel.(2)In order to finish the low-light-level mine image features extraction,an edge detection method is proposed in this paper based on the improved Retinex theory and wavelet multi-scale product algorithm.The particular environment in mines makes it difficult to extract features in underground images with dark hue and low target discrimination.Therefore,after the spectrum analysis of images under different illuminance,captured images are decomposed by a wavelet function into four parts.Following,according to the difference of information contained in different frequency domains,the multi-scale Retinex theory is exploited to eliminate the radiation component in low frequency domains,and the fuzzy enhancement algorithm is used to improve the edge information contrast.After these,an improved wavelet multi-scale product algorithm is employed to finish the edge detection for non-uniform illumination images.(3)According to the overall scheme,a roadheader body pose monitoring system is designed by using a double cross laser instrument.It takes the cross lasers and laser targets as information sources to establish the machine body position calculating model by analyzing the characteristics of laser line imaging on the front and rear targets.After the laser target design and the monitoring equipment installation,a simulation platform is utilized to complete the automatic roadheader pose detection experiment in real time.The experimental results show that within 2 to 100 metres measuring range,the angles measurement accuracy is 16° and the displacement detection accuracy is 10 mm.We can find that the proposed system can satisfy the requirements of automatic,precise,and real-time positioning for the roadheader during the process of tunnel construction.(4)After the construction of the cutting head binocular positioning system,an accurate calibration method is presented for binocular systems based on geometric features of vanishing points formed by two sets of parallel lines.It can solve shortcomings of these existing methods which own low precision and high computation complexity.In order to reduce the effect of lens distortion on the reconstruction accuracy,internal parameters of the monocular camera are calibrated by using the central symmetry of distorted images and the proportional in-variance of perspective projection.Afterwards,external parameters are figured out on the basis of geometric properties of orthogonal vanishing points.Finally,geometry constraint between epipolar lines is adopted for optimizing structure parameters of the binocular system by minimizing matching errors under different views.Since the using of a pair of orthogonal parallel lines in the calibration template,the proposed method can exactly finish the parameters calibration for stereo systems with impressive precision and stability.(5)After comparing existing image matching methods for target recognition,the cutting head matching algorithm is proposed on the basis of target contour features.The shape descriptor,simplified intersection angle of tangent lines(SIATL),is firstly proposed on the basis of spatial positions of contour points,which owns great self-contained property and features describing ability for both partial and whole shapes.To achieve best matching,the MVM algorithm is improved with skipping and multiple matching penalties to complete multiple mapping and skip existing outliers in sequences.More precisely,these advantages make the proposed algorithm less sensible to local variations caused by varied views.Our experimental results demonstrate that the proposed method outperforms existing ones with strong robustness and high accuracy.In actual pictures test,the recognition rate of cutting head can reach 100%,which lays the foundation for accurate location in real-time.(6)By using a binocular vision system installed on the machine to collect cutting head images,the recognition and positioning of the cutting head are completed in the simulated tunnel coordinate system.Based on the 3D coordinate solution principle of binocular system,feature points of the target are picked out for precise 3D reconstructed and plane fitting.After removing outliers from the fitting plane,the mean value of the upper edge is regarded as the anchor point of the cutting head in the tunnel space.Then,the cutting head position can be obtained by using the coordinate transformation method combined with the machine body pose.In order to verify the feasibility of the proposed scheme,the positioning experiment is carried out by manually manipulating the roadheader in the simulated roadway.The results show that,in the roadway coordinate system,the anchor point projection error on the tunnel section is 3 cm,which satisfies the requirement of cutting head precise location in the process of roadway excavation.In this paper,the advanced machine vision technology is applied into coal mines for the roadheader positioning underground,which significantly improves the positioning efficiency,accuracy and real-time of roadheader body and the cutting head.It also makes up shortcomings of traditional methods brought by harsh measuring environment in mines,such as the easily blocked measuring path,thick dust concentration,excessive vibration of fuselage,etc.In addition,the proposed positioning system has important theoretical significance and application value for improving the efficiency of underground tunneling,reducing construction risk and realizing intelligent tunneling. |
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