Abstract
This paper focuses on inspection robot navigation systems based on distributed vision in order to solve the navigation problem for indoor inspection robots in an unknown environment. Firstly, the robot platform of the navigation system is designed, the system is built, and the software of the host computer interface and driver of the bottom driver are designed. Secondly, the key technologies of path planning and image processing in visual navigation are studied theoretically and experimentally. Finally, the performance of the navigation system is tested. Experimental results demonstrate that the inspection robot navigation system based on distributed vision can undertake autonomous localization and navigation tasks in unknown environments.
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References
Huang, H., Gartner, G.: A survey of mobile indoor navigation systems. In: Gartner, G., Ortag, F. (eds.) Cartography in Central and Eastern Europe. Lecture Notes in Geoinformation and Cartography, pp. 305–319. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-03294-3_20
Lambrinos, D., Möller, R., Labhart, T.: A mobile robot employing insect strategies for navigation. Robot. Auton. Syst. 30(1), 39–64 (2000)
Kawaguchi, Y., Yoshida, I., Kurumatani, H.: Internal pipe inspection robot. Proc. IEEE 1, 857–862 (1995)
Baus, J., Wahlster, W.: A resource-adaptive mobile navigation system. In: DBLP, pp. 15–22 (2002)
Phillips, J.B.: Magnetic navigation. J. Theor. Biol. 180(4), 309–319 (1996)
Barshan, B., Durrant-Whyte, H.F.: An inertial navigation system for a mobile robot. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, vol. 3, pp. 2243–2248 (1993)
Gui, Y., Guo, P., Zhang, H.: Airborne vision-based navigation method for UAV accuracy landing using infrared lamps. J. Intell. Rob. Syst. 72(2), 197–218 (2013)
Pagnottelli, S., Taraglio, S., Valigi, P.: Visual and laser sensory data fusion for outdoor robot localisation and navigation. In: Proceedings of the IEEE, pp. 171–177 (2005)
Kurz, A.: Constructing maps for mobile robot navigation based on ultrasonic range data. IEEE Trans. Syst. Man Cybern. Part B Cybern. Publ. IEEE Syst. Man Cybern. Soc. 26(2), 233–242 (1996)
Gao, Y., Liu, S., Atia, M.M.: INS/GPS/LiDAR integrated navigation system for urban and indoor environments using hybrid scan matching algorithm. Sensors 15(9), 23286–23302 (2015)
Liu, W., Zhang, S., Fan, S.A.: visual navigation method of substation inspection robot. In: International Conference on Progress in Informatics and Computing. IEEE (2017)
Indelman, V., Gurfil, P., Rivlin, E.: Distributed vision-aided cooperative localization and navigation based on three-view geometry. In: Aerospace Conference, pp. 1–20. IEEE (2011)
Nishimura, H., Nonami, T.: Image processing device and image processing method in image processing device. J. Oral Rehabil. 8(3), 203–208 (2018)
Yong, D.: Navigation for mobile robot based on uncertainty grid-map. Control Theory Appl. 23(6), 1009–1013 (2006)
Guruji, A.K., Agarwal, H., Parsediya, D.K.: Time-efficient A* algorithm for robot path planning. Proc. Technol. 23, 144–149 (2016)
Stentz, A.: Optimal and efficient path planning for partially-known environments. In: Hebert, M.H., Thorpe, C., Stentz, A. (eds.) Intelligent Unmanned Ground Vehicles, vol. 388, pp. 203–222. Springer, Boston (1997). https://doi.org/10.1007/978-1-4615-6325-9_11
Tu, J., Yang, S.X.: Genetic algorithm based path planning for a mobile robot. In: IEEE International Conference on Robotics and Automation, vol. 1, pp. 1221–1226 (2003)
Gutjahr, W.J.: Aco algorithms with guaranteed convergence to the optimal solution. Inf. Process. Lett. 82(3), 145–153 (2002)
Shang, R., Jiao, L., Gong, M., Lu, B.: Clonal selection algorithm for dynamic multiobjective optimization. In: Hao, Y., et al. (eds.) CIS 2005. LNCS (LNAI), vol. 3801, pp. 846–851. Springer, Heidelberg (2005). https://doi.org/10.1007/11596448_125
Rais, H.M., Othman, Z.A., Hamdan, A.R.: Improved Dynamic Ant Colony System (DACS) on symmetric Traveling Salesman Problem (TSP). In: International Conference on Intelligent and Advanced Systems, pp. 43–48. IEEE (2008)
Brown, M., Lowe, D.G.: Automatic panoramic image stitching using invariant features. Int. J. Comput. Vision 74(1), 59–73 (2007)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60(2), 91–110 (2004)
Bay, H., Ess, A., Tuytelaars, T.: Speeded-Up Robust Features (SURF). Comput. Vis. Image Underst. 110(3), 346–359 (2008)
Rublee, E., Rabaud, V., Konolige, K.: ORB: an efficient alternative to SIFT or SURF. In: International Conference on Computer Vision, pp. 2564–2571. IEEE (2012)
Li, P., Zhu, H.: Parameter selection for ant colony algorithm based on bacterial foraging algorithm. Math. Probl. Eng. 3, 1–12 (2016)
Acknowledgments
This work has been supported by grant of the National Key Research and Development Program of China (No. 2018YFC0808000) and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD), China.
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Wang, L. et al. (2019). The Design of Inspection Robot Navigation Systems Based on Distributed Vision. In: Yu, H., Liu, J., Liu, L., Ju, Z., Liu, Y., Zhou, D. (eds) Intelligent Robotics and Applications. ICIRA 2019. Lecture Notes in Computer Science(), vol 11744. Springer, Cham. https://doi.org/10.1007/978-3-030-27541-9_25
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