Accurate Positioning Method of Indoor Plastering Robot Based on Line Laser

Authors: Guanqiao Wang, Hongyang Yu

Abstract:

There is a lot of repetitive work in the traditional construction industry. These repetitive tasks can significantly improve production efficiency by replacing manual tasks with robots. Therefore, robots appear more and more frequently in the construction industry. Navigation and positioning is a very important task for construction robots, and the requirements for accuracy of positioning are very high. Traditional indoor robots mainly use radio frequency or vision methods for positioning. Compared with ordinary robots, the indoor plastering robot needs to be positioned closer to the wall for wall plastering, so the requirements for construction positioning accuracy are higher, and the traditional navigation positioning method has a large error, which will cause the robot to move. Without the exact position, the wall cannot be plastered or the error of plastering the wall is large. A positioning method is proposed, which is assisted by line lasers and uses image processing-based positioning to perform more accurate positioning on the traditional positioning work. In actual work, filter, edge detection, Hough transform and other operations are performed on the images captured by the camera. Each time the position of the laser line is found, it is compared with the standard value, and the position of the robot is moved or rotated to complete the positioning work. The experimental results show that the actual positioning error is reduced to less than 0.5 mm by this accurate positioning method.

Keywords: Indoor plastering robot, navigation, precise positioning, line laser, image processing.

References:

[1] King N, Bechthold M, Kane A, et al. Robotic Tile Placement: Tools, Techniques and Feasibility. Automation in Construction 39(01), 161–166(2014)
[2] Elashry K, Glynn R. An Approach to Automated Construction Using Adaptive Programing. Robotic Fabrication in Architecture, Art and Design 2014. AnnArbor, Michigan : Springer : 51–66(2014)
[3] Asadi E, Li B, Chen I-M. Pictobot. IEEE Robotic and Automation Magazine, 25(2), 82–94(2018)
[4] Yan R-J, Kayacan E, Chen I-M, et al. QuicaBot: Quality Inspection and Assessment Robot. IEEE Transactions on Automation Science and Engineering, 01(99) : 1–12(2018)
[5] Dammann A, Sand S, Raulefs R. On the benefit of observing signals of opportunity in mobile radio positioning. SCC 2013; 9th International ITG Conference on Systems, Communication and Coding. VDE, 2013: 1-6(2013)
[6] Bhatt D, Babu S R, Chudgar H S. A novel approach towards utilizing Dempster Shafer fusion theory to enhance WiFi positioning system accuracy. Pervasive and Mobile Computing, 37(1): 115–123(2017)
[7] Jiao J, Deng Z, Xu L, et al. A hybrid of smartphone camera and basestation wide-area indoor positioning method. KSII Transactions on Internet and Information Systems (TIIS), 10(2): 723–743(2016)
[8] Jin M, Liu S, Schiavon S, et al. Automated mobile sensing: Towards high-granularity agile indoor environmental quality monitoring. Building and Environment, 127(1): 268-276(2018)
[9] Heath M, Sarkar S, Sanocki T, et al. Comparison of edge detectors: a methodology and initial study. Computer vision and image understanding, 69(1): 38–54(1998)
[10] FischlerM A, BollesR C.Random sample consensus:aparadigm for model fitting with application to image analysis and automated cartography.Communication Association Machine 24(6) :381-395(1981).