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Autonomous Area Exploration and Mapping in Underground Mine Environments by Unmanned Aerial Vehicles

Published online by Cambridge University Press:  17 June 2019

Hang Li ,
Andrey V. Savkin  and
Branka Vucetic
Hang Li
Affiliation:
School of Electrical Engineering and Telecommunications, University of New South Wales, Sydney, Australia
Branka Vucetic
Affiliation:
School of Electrical and Information Engineering, University of Sydney, Sydney, Australia
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Summary

In this paper, we propose a method of using an autonomous flying robot to explore an underground tunnel environment and build a 3D map. The robot model we use is an extension of a 2D non-holonomic robot. The measurements and sensors we considered in the presented method are simple and valid in practical unmanned aerial vehicle (UAV) engineering. The proposed safe exploration algorithm belongs to a class of probabilistic area search, and with a mathematical proof, the performance of the algorithm is analysed. Based on the algorithm, we also propose a sliding control law to apply the algorithm to a real quadcopter in experiments. In the presented experiment, we use a DJI Guidance sensing system and an Intel depth camera to complete the localization, obstacle detection and 3D environment information capture. Furthermore, the simulations show that the algorithm can be implemented in sloping tunnels and with multiple UAVs.

Keywords

Flying robot navigationUnmanned aerial vehicleArea exploration3D mapping3D navigation
Type
Articles
Information
Robotica , Volume 38 , Issue 3 , March 2020 , pp. 442 - 456
Copyright
© Cambridge University Press 2019 

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Footnotes

*

This work was supported by the Australian Research Council.

References

Sankarasrinivasan, S., Balasubramanian, E., Karthik, K., Chandrasekar, U. and Gupta, R., “Health monitoring of civil structures with integrated uav and image processing system,” Proc. Comput. Sci. 54, 508–515 (2015).CrossRefGoogle Scholar
Siebert, S. and Teizer, J., “Mobile 3D mapping for surveying earthwork projects using an unmanned aerial vehicle (UAV) system,” Auto. Constr. 41, 1–14 (2014).CrossRefGoogle Scholar
Gevaert, C. M., Suomalainen, J., Tang, J. and Kooistra, L., “Generation of spectral–temporal response surfaces by combining multispectral satellite and hyperspectral UAV imagery for precision agriculture applications,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 8(6), 3140–3146 (2015).CrossRefGoogle Scholar
Lee, S. and Choi, Y., “Reviews of unmanned aerial vehicle (drone) technology trends and its applications in the mining industry,” Geosyst. Eng. 19(4), 197–204 (2016).CrossRefGoogle Scholar
Li, H. and Savkin, A. V., “Wireless sensor network based navigation of micro flying robots in the industrial internet of things,” IEEE Trans. Ind. Inf. 14(8), 3524–3533 (2018).CrossRefGoogle Scholar
Nazarzehi, V. and Savkin, A. V., “Distributed self-deployment of mobile wireless 3D robotic sensor networks for complete sensing coverage and forming specific shapes,” Robotica 36(1), 1–18 (2018).CrossRefGoogle Scholar
Wang, C., Savkin, A. V. and Garratt, M., “A strategy for safe 3D navigation of non-holonomic robots among moving obstacles,” Robotica 36(2), 275–297 (2018).CrossRefGoogle Scholar
Scherer, S., Rehder, J., Achar, S., Cover, H., Chambers, A., Nuske, S. and Singh, S. “River mapping from a flying robot: state estimation, river detection, and obstacle mapping,” Auto. Robots 33(1), 189–214 (2012).CrossRefGoogle Scholar
Lucieer, A., de Jong, S. M. and Turner, D., “Mapping landslide displacements using structure from motion (SfM) and image correlation of multi-temporal UAV photography,” Prog. Phys. Geogr. Earth Environ. 38(1), 97–116 (2014).CrossRefGoogle Scholar
Nemra, A. and Aouf, N., “Robust Feature Extraction and Correspondence for UAV Map Building,” 2009 17th Mediterranean Conference on Control and Automation, Thessaloniki, Greece (2009) pp. 922–927.Google Scholar
Dryanovski, I., Morris, W. and Xiao, J., “Multi-Volume Occupancy Grids: An Efficient Probabilistic 3D Mapping Model for Micro Aerial Vehicles,” 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, Taipei, Taiwan (2010) pp. 1553–1559.Google Scholar
Bircher, A., Kamel, M., Alexis, K., Oleynikova, H. and Siegwart, R., “Receding horizon path planning for 3D exploration and surface inspection,” Auto. Robots 42(2), 291–306 (2018).CrossRefGoogle Scholar
Song, S. and Jo, S., “Online Inspection Path Planning for Autonomous 3D Modeling Using a Micro-aerial Vehicle,” 2017 IEEE International Conference on Robotics and Automation (ICRA), Singapore (2017) pp. 6217–6224.Google Scholar
Shen, S., Michael, N. and Kumar, V., “Stochastic differential equation-based exploration algorithm for autonomous indoor 3D exploration with a micro-aerial vehicle,” Int. J. Robot. Res. 31(12), 1431–1444 (2012).CrossRefGoogle Scholar
Dubins, L. E., “On curves of minimal length with a constraint on average curvature, and with prescribed initial and terminal positions and tangents,” American J. Math. 79(3), 497–516 (1957).CrossRefGoogle Scholar
Carrillo, L. R. García, López, A. E. Dzul, Lozano, R. and Pégard, C., “Combining stereo vision and inertial navigation system for a quad-rotor UAV,” J. Intell. Robot. Syst. 65(1), 373–387 (2012).CrossRefGoogle Scholar
Caballero, F., Merino, L., Ferruz, J. and Ollero, A., “Vision-based odometry and slam for medium and high altitude flying UAVs,” J. Int. Robot. Syst. 54(1), 137–161 (2009).CrossRefGoogle Scholar
Zhou, G., Fang, L., Tang, K., Zhang, H., Wang, K. and Yang, K., “Guidance: A Visual Sensing Platform for Robotic Applications,” 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Boston, Massachusetts, USA (2015) pp. 9–14.Google Scholar
Lô Medeiros, F. L. and da Silva, J. D. S., “A Dijkstra Algorithm for Fixed-wing UAV Motion Planning Based on Terrain Elevation,” In: Advances in Artificial Intelligence– SBIA 2010 (Springer, Berlin, Heidelberg, 2010) pp. 213–222.CrossRefGoogle Scholar
Yan, F., Liu, Y. S. and Xiao, J. Z., “Path planning in complex 3D environments using a probabilistic roadmap method,” Int. J. Auto. Comput. 10(6), 525–533 (2013).CrossRefGoogle Scholar
Lu, L., Zong, C., Lei, X., Chen, B. and Zhao, P., “Fixed-Wing UAV Path Planning in a Dynamic Environment via Dynamic RRT Algorithm,” In: Mechanism and Machine Science (Springer, Singapore, 2017) pp. 271–282.CrossRefGoogle Scholar
Guibas, L. J., Hsu, D., Kurniawati, H. and Rehman, E., Bounded Uncertainty Roadmaps for Path Planning (Springer, Berlin, Heidelberg, 2010) pp. 199–215.CrossRefGoogle Scholar
Kewlani, G., Ishigami, G. and Iagnemma, K., “Stochastic Mobility-Based Path Planning in Uncertain Environments,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems IROS 2009, St. Louis, Missouri, USA (2009) pp. 1183–1189.Google Scholar
Kothari, M. and Postlethwaite, I., “A probabilistically robust path planning algorithm for uavs using rapidly-exploring random trees,” J. Intell. Robot. Syst. 71(2), 231–253 (2013).CrossRefGoogle Scholar
Hoy, M., Matveev, A. S. and Savkin, A. V., “Algorithms for collision-free navigation of mobile robots in complex cluttered environments: a survey,” Robotica 33(3), 463–497 (2015).CrossRefGoogle Scholar
Matveev, A. S., Savkin, A. V., Hoy, M. and Wang, C., Safe Robot Navigation Among Moving and Steady Obstacles (Elsevier, Amsterdam, Netherlands, 2015).Google Scholar
Palmieri, L., Koenig, S. and Arras, K. O., “RRT-Based Nonholonomic Motion Planning Using Any-Angle Path Biasing,” 2016 IEEE International Conference on Robotics and Automation (ICRA), Stockholm, Sweden (2016) pp. 2775–2781.Google Scholar
Anderson, R. P. and Milutinovi, D. c′, “A stochastic approach to dubins vehicle tracking problems,” IEEE Trans. Auto. Contr. 59(10), 2801–2806 (2014).CrossRefGoogle Scholar
Di, B., Zhou, R. and Duan, H., “Potential field based receding horizon motion planning for centrality-aware multiple UAV cooperative surveillance,” Aeros. Sci. Tech. 46, 386–397 (2015).CrossRefGoogle Scholar
Savkin, A. V. and Huang, H., “Optimal aircraft planar navigation in static threat environments,” IEEE Trans. Aerosp. Elect. Syst. 53(5), 2413–2426 (2017).CrossRefGoogle Scholar
Savkin, A. V. and Li, H., “A safe area search and map building algorithm for a wheeled mobile robot in complex unknown cluttered environments,” Robotica, 36(1), 96–118 (2018).CrossRefGoogle Scholar
Savkin, A. V. and Hoy, M., “Reactive and the shortest path navigation of a wheeled mobile robot in cluttered environments,” Robotica 31(2), 323–330 (2013).CrossRefGoogle Scholar
Matveev, A. S., Teimoori, H. and Savkin, A. V., “A method for guidance and control of an autonomous vehicle in problems of border patrolling and obstacle avoidance,” Automatica 47(3), 515–524 (2011).CrossRefGoogle Scholar
Savkin, A. V. and Teimoori, H., “Bearings-only guidance of a unicycle-like vehicle following a moving target with a smaller minimum turning radius,” IEEE Trans. Auto. Contr. 55(10), 2390–2395 (2010).CrossRefGoogle Scholar
Burdette, A. C., Analytic Geometry (Academic Press, 2014) pp. 144–161.Google Scholar