S.I. : Machine Learning Applications for Self-Organized Wireless Networks
Traditional assembly process design tasks are generally performed manually by experienced craftsmen using 2D drawings, which often require the use of physical prototypes. This kind of assembly process design mode inevitably has the defects of low optimization degree, low design efficiency, and high cost. The current computer-aided assembly process design also has problems such as “combination explosion.” Assembly process design technology and algorithm based on virtual reality and artificial intelligence is an effective way to solve the above problems. The application of the system in a virtual reality system provides an accurate positioning method in a virtual reality system. The ultrasonic three-dimensional space positioning system uses a differential method to improve the ranging accuracy. The system has the advantages of strong anti-electromagnetic interference, insensitivity to light and no electromagnetic radiation; thus, it is suitable for application in virtual reality systems. In the paper, two different methods of assembly process planning are proposed for interactive constraint definition assembly and automatic constraint recognition assembly, which make up for the lack of a single method, make the assembly process more realistic, and realize the assembly path by acquiring sampling points. The recording and playback of the assembly process planning process is achieved using screenshots and video compression techniques.
High resolution Improved projection 3D localization algorithm Precision assembly of parts Virtual reality
This is a preview of subscription content, log in to check access.
The authors acknowledge the Research Center of Industrial Design, Research Base of Humanities and Social Sciences, Sichuan Provincial Department of Education (Grant: GY-14YB-06), the Modern Design and Culture Research Center, Research Base of Sichuan Philosophy and Social Science(Grant: MD17Z001), the Research Center of Industrial Design, Research Base of Humanities and Social Sciences, Sichuan Provincial Department of Education (Grant: GY-16ZD-01), the Research Center of Industrial Design, Research Base of Humanities and Social Sciences, Sichuan Provincial Department of Education (Grant: GY-13YB-09).
Hesch JA, Kottas DG, Bowman SL, Roumeliotis SI (2014) Camera-IMU-based localization: observability analysis and consistency improvement. Int J Robot Res 33(1):182–201CrossRefGoogle Scholar
Zuo C, Huang L, Zhang M, Chen Q, Asundi A (2016) Temporal phase unwrapping algorithms for fringe projection profilometry: a comparative review. Opt Lasers Eng 85:84–103CrossRefGoogle Scholar
Li H, Ding H, Huang D, Wang Y, Zhao X, Morvan JM, Chen L (2015) An efficient multimodal 2D + 3D feature-based approach to automatic facial expression recognition. Comput Vis Image Underst 140:83–92CrossRefGoogle Scholar
Han G, Zhang C, Liu T, Shu L (2016) MANCL: a multi-anchor nodes collaborative localization algorithm for underwater acoustic sensor networks. Wirel Commun Mob Comput 16(6):682–702CrossRefGoogle Scholar
Huang J, Sun M, Gumpper K, Chi Y, Ma J (2015) 3D multifocus astigmatism and compressed sensing (3D MACS) based superresolution reconstruction. Biomed Opt Express 6(3):902–917CrossRefGoogle Scholar
Kumar A, Khosla A, Saini JS, Sidhu SS (2015) Range-free 3D node localization in anisotropic wireless sensor networks. Appl Soft Comput 34:438–448CrossRefGoogle Scholar
Svärm L, Enqvist O, Kahl F, Oskarsson M (2017) City-scale localization for cameras with known vertical direction. IEEE Trans Pattern Anal Mach Intell 39(7):1455–1461CrossRefGoogle Scholar
Barnowski R, Haefner A, Mihailescu L, Vetter K (2015) Scene data fusion: real-time standoff volumetric gamma-ray imaging. Nucl Instrum Methods Phys Res Sect A 800:65–69CrossRefGoogle Scholar
Kanaris L, Kokkinis A, Fortino G, Liotta A, Stavrou S (2016) Sample size determination algorithm for fingerprint-based indoor localization systems. Comput Netw 101:169–177CrossRefGoogle Scholar
Li X, Deng ZD, Rauchenstein LT, Carlson TJ (2016) Contributed review: source-localization algorithms and applications using time of arrival and time difference of arrival measurements. Rev Sci Instrum 87(4):041502CrossRefGoogle Scholar
Charmette B, Royer E, Chausse F (2016) Vision-based robot localization based on the efficient matching of planar features. Mach Vis Appl 27(4):415–436CrossRefGoogle Scholar