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A method for autonomous collision-free navigation of a quadrotor UAV in unknown tunnel-like environments

Published online by Cambridge University Press:  24 June 2021

Taha Elmokadem Open the ORCID record for Taha Elmokadem [Opens in a new window]  and
Andrey V. Savkin
Taha Elmokadem*
Affiliation:
School of Electrical Engineering and Telecommunications, The University of New South Wales, Sydney 2052, Australia
Andrey V. Savkin
Affiliation:
School of Electrical Engineering and Telecommunications, The University of New South Wales, Sydney 2052, Australia
*
*Corresponding author. Email: [email protected]
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Abstract

Unmanned aerial vehicles (UAVs) have become essential tools for exploring, mapping and inspection of unknown three-dimensional (3D) tunnel-like environments which is a very challenging problem. A computationally light navigation algorithm is developed in this paper for quadrotor UAVs to autonomously guide the vehicle through such environments. It uses sensors observations to safely guide the UAV along the tunnel axis while avoiding collisions with its walls. The approach is evaluated using several computer simulations with realistic sensing models and practical implementation with a quadrotor UAV. The proposed method is also applicable to other UAV types and autonomous underwater vehicles.

Keywords

3D autonomous navigationquadrotor UAVscollision avoidanceflying robotsnavigation in unknown environmentsautonomous tunnel inspection
Type
Research Article
Information
Robotica , Volume 40 , Issue 4 , April 2022 , pp. 835 - 861
Copyright
© The Author(s), 2021. Published by Cambridge University Press

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References

Özaslan, T., Shen, S., Mulgaonkar, Y., Michael, N. and Kumar, V., “Inspection of Penstocks and Featureless Tunnel-like Environments Using Micro UAVs,” In: Field and Service Robotics (Springer, 2015) pp. 123–136.CrossRefGoogle Scholar
Özaslan, T., Mohta, K., Keller, J., Mulgaonkar, Y., Taylor, C. J., Kumar, V., Wozencraft, J. M. and Hood, T., “Towards Fully Autonomous Visual Inspection of Dark Featureless Dam Penstocks Using MAVs,” 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE, 2016) pp. 4998–5005.10.1109/IROS.2016.7759734CrossRefGoogle Scholar
Özaslan, T., Loianno, G., Keller, J., Taylor, C. J., Kumar, V., Wozencraft, J. M. and Hood, T., “Autonomous navigation and mapping for inspection of penstocks and tunnels with MAVs,” IEEE Rob. Autom. Lett. 2(3), 1740–1747 (2017).10.1109/LRA.2017.2699790CrossRefGoogle Scholar
Özaslan, T., Loianno, G., Keller, J., Taylor, C. J. and Kumar, V., “Spatio-temporally smooth local mapping and state estimation inside generalized cylinders with micro aerial vehicles,” IEEE Rob. Autom. Lett. 3(4), 4209–4216 (2018).10.1109/LRA.2018.2861888CrossRefGoogle Scholar
Quenzel, J., Nieuwenhuisen, M., Droeschel, D., Beul, M., Houben, S. and Behnke, S., “Autonomous MAV-based indoor chimney inspection with 3D laser localization and textured surface reconstruction,” J. Intell. Rob. Syst. 93(1–2), 317–335 (2019).10.1007/s10846-018-0791-yCrossRefGoogle Scholar
Tan, C. H., Ng, M., Shaiful, D. S. B., Win, S. K. H., Ang, W. J., Yeung, S. K., Lim, H. B, Do, M. N. and Foong, S., “A smart unmanned aerial vehicle (UAV) based imaging system for inspection of deep hazardous tunnels,” Water Practice Technol. 13(4), 991–1000 (2018).CrossRefGoogle Scholar
Tan, C. H., D. S. bin Shaiful, W. J. Ang, S. K. H. Win and S. Foong, “Design optimization of sparse sensing array for extended aerial robot navigation in deep hazardous tunnels,” IEEE Rob. and Autom. Lett. 4(2), 862–869 (2019).10.1109/LRA.2019.2892796CrossRefGoogle Scholar
Mansouri, S. S., Kanellakis, C., Georgoulas, G. and Nikolakopoulos, G., “Towards MAV Navigation in Underground Mine Using Deep Learning,” 2018 IEEE International Conference on Robotics and Biomimetics (ROBIO) (IEEE, 2018) pp. 880–885.CrossRefGoogle Scholar
Mascarich, F., Khattak, S., Papachristos, C. and Alexis, K., “A Multi-Modal Mapping Unit for Autonomous Exploration and Mapping of Underground Tunnels,” 2018 IEEE Aerospace Conference (IEEE, 2018) pp. 1–7.Google Scholar
Kanellakis, C., Karvelis, P. and Nikolakopoulos, G., “Open Space Attraction Based Navigation in Dark Tunnels for MAVs,” International Conference on Computer Vision Systems (Springer, 2019) pp. 110–119.CrossRefGoogle Scholar
Li, D., Yang, W., Shi, X., Guo, D., Long, Q., Qiao, F. and Wei, Q., “A visual-inertial localization method for unmanned aerial vehicle in underground tunnel dynamic environments,” IEEE Access 8, 76809–76822 (2020). https://ieeexplore.ieee.org/document/9076004 10.1109/ACCESS.2020.2989480CrossRefGoogle Scholar
Kominiak, D., Mansouri, S. S., Kanellakis, C. and Nikolakopoulos, G., MAV Development Towards Navigation in Unknown and Dark Mining Tunnels. arXiv preprint arXiv:2005.14433 (2020).CrossRefGoogle Scholar
Mansouri, S. S., Kanellakis, C., Karvelis, P., Kominiak, D. and Nikolakopoulos, G., “MAV Navigation in Unknown Dark Underground Mines Using Deep Learning,” European Control Conference (2020).CrossRefGoogle Scholar
Li, H., Savkin, A. V and Vucetic, B., “Autonomous area exploration and mapping in underground mine environments by unmanned aerial vehicles,” Robotica 38(3), 442–456 (2020).10.1017/S0263574719000754CrossRefGoogle Scholar
Mansouri, S. S., Kanellakis, C., Kominiak, D. and Nikolakopoulos, G., “Deploying MAVs for autonomous navigation in dark underground mine environments,” Rob. Auton. Syst. 126, 103472 (2020). https://www.sciencedirect.com/science/article/pii/S0921889019306256CrossRefGoogle Scholar
Turner, R. M., MacLaughlin, M. M. and Iverson, S. R., “Identifying and mapping potentially adverse discontinuities in underground excavations using thermal and multispectral UAV imagery,” Eng. Geol. 266, 105470 (2020). https://www.sciencedirect.com/science/article/pii/S0013795219314589CrossRefGoogle Scholar
Dang, T., Mascarich, F., Khattak, S., Nguyen, H., Nguyen, H., Hirsh, S., Reinhart, R., Papachristos, C. and Alexis, K., “Autonomous Search for Underground Mine Rescue Using Aerial Robots,” 2020 IEEE Aerospace Conference (IEEE, 2020) pp. 1–8.Google Scholar
Petrlk, M., BÁČa, T., HeŘt, D., Vrba, M., Krajnk, T. and Saska, M., “A robust UAV system for operations in a constrained environment,” IEEE Rob. Autom. Lett. 5(2), 2169–2176 (2020).10.1109/LRA.2020.2970980CrossRefGoogle Scholar
Chataigner, F., Cavestany, P., Soler, M., Rizzo, C., Gonzalez, J., Bosch, C., Gibert, J., Torrente, A., Gomez, R. and Serrano, D., “ARSI: An Aerial Robot for Sewer Inspection,” In: Advances in Robotics Research: From Lab to Market (Springer, 2020) pp. 249–274.CrossRefGoogle Scholar
Shukla, A. and Karki, H., “Application of robotics in onshore oil and gas industry - A review Part I,” Rob. Auto. Syst. 75, 490–507 (2016). https://www.sciencedirect.com/science/article/pii/S0921889015002006CrossRefGoogle Scholar
Mallios, A., Ridao, P., Ribas, D., Carreras, M. and Camilli, R., “Toward autonomous exploration in confined underwater environments,” J. Field Rob. 33(7), 994–1012 (2016).10.1002/rob.21640CrossRefGoogle Scholar
Fairfield, N., Kantor, G. and Wettergreen, D., “Real-time SLAM with octree evidence grids for exploration in underwater tunnels,” J. Field Rob. 24(1–2), 03–21 (2007).10.1002/rob.20165CrossRefGoogle Scholar
Vidal, E., Palomeras, N. and Carreras, M., “Online 3D Underwater Exploration and Coverage,” 2018 IEEE/OES Autonomous Underwater Vehicle Workshop (AUV) (IEEE, 2018) pp. 1–5.Google Scholar
am Ende, B. A., “3D mapping of underwater caves,” IEEE Comput. Graph. Appl. 21(2), 14–20 (2001).10.1109/38.909011CrossRefGoogle Scholar
Martins, A., Almeida, J., Almeida, C. and Silva, E., “UXNEXMIN AUV Perception System Design and Characterization,” 2018 IEEE/OES Autonomous Underwater Vehicle Workshop (AUV) (IEEE, 2018) pp. 1–7.Google Scholar
Vidal, E., Palomeras, N., IsteniČ, K., Gracias, N. and Carreras, M., “Multisensor online 3D view planning for autonomous underwater exploration,” J. Field Rob. 37(6), 1123–1147 (2020).10.1002/rob.21951CrossRefGoogle Scholar
Nocerino, E., Menna, F., Farella, E. and Remondino, F., “3D virtualization of an underground semi-submerged cave system,” Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. ISPRS Arch. 42(2/W15), 857–864 (2019).10.5194/isprs-archives-XLII-2-W15-857-2019CrossRefGoogle Scholar
Jacobi, M., “Autonomous inspection of underwater structures,” Rob. Auto. Syst. 67, 80–86 (2015). https://www.sciencedirect.com/science/article/pii/S0921889014002267CrossRefGoogle Scholar
Weidner, N., Rahman, S., Li, A. Q. and Rekleitis, I., “Underwater Cave Mapping Using Stereo Vision,” 2017 IEEE International Conference on Robotics and Automation (ICRA) (IEEE, 2017) pp. 5709–5715.10.1109/ICRA.2017.7989672CrossRefGoogle Scholar
White, C., Hiranandani, D., Olstad, C. S., Buhagiar, K., Gambin, T. and Clark, C. M., “The Malta cistern mapping project: Underwater robot mapping and localization within ancient tunnel systems,” J. Field Rob. 27(4), 399–411 (2010).CrossRefGoogle Scholar
Gary, M., Fairfield, N., Stone, W. C., Wettergreen, D., Kantor, G. and Sharp, J. M., Jr., “3D Mapping and Characterization of Sistema ZacatÓn from DEPTHX (DEep Phreatic THermal eXplorer),” In: Sinkholes and the Engineering and Environmental Impacts of Karst (2008) pp. 202–212.Google Scholar
Li, Y. and Liu, C., “Efficient and safe motion planning for quadrotors based on unconstrained quadratic programming,” Robotica 39(2), 317–333 (2021).10.1017/S0263574720000387CrossRefGoogle Scholar
Hoy, M. C., 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
Kanellakis, C. and Nikolakopoulos, G., “Evaluation of Visual Localization Systems in Underground Mining,” 2016 24th Mediterranean Conference on Control and Automation (MED) (IEEE, 2016) pp. 539–544.CrossRefGoogle Scholar
Bloesch, M., Omari, S., Hutter, M. and Siegwart, R., “Robust Visual Inertial Odometry Using a Direct EKF-based Approach,” 2015 IEEE/RSJ International Conference on Intelligent Robots and systems (IROS) (IEEE, 2015) pp. 298–304.CrossRefGoogle Scholar
Papachristos, C., Khattak, S., Mascarich, F. and Alexis, K., “Autonomous Navigation and Mapping in Underground Mines Using Aerial Robots,” 2019 IEEE Aerospace Conference (IEEE, 2019) pp. 1–8.Google Scholar
Dang, T., Mascarich, F., Khattak, S., Nguyen, H., Khedekar, N., Papachristos, C. and Alexis, K., “Field-Hardened Robotic Autonomy for Subterranean Exploration,” Conference on Field and Service Robotics, Tokyo, Japan (2019).Google Scholar
Savkin, A. V and Wang, C., “A Method for Collision Free Navigation of Non-Holonomic 3D Robots in Unknown Tunnel like Environments,” 2017 IEEE International Conference on Robotics and Biomimetics (ROBIO) (IEEE, 2017) pp. 936–940.10.1109/ROBIO.2017.8324537CrossRefGoogle Scholar
Matveev, A. S. and Savkin, A. V., Proofs of Technical Results Justifying an Algorithm of Reactive 3D Navigation of a Mobile Robot through an Unknown Tunnel. arXiv preprint arXiv:1803.00803 (2018).Google Scholar
Matveev, A. S., Magerkin, V. and Savkin, A. V., “A method of reactive control for 3D navigation of a nonholonomic robot in tunnel-like environments,” Automatica 114, 108831 (2020). https://www.sciencedirect.com/science/article/pii/S0005109820300297CrossRefGoogle 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).10.1016/j.automatica.2011.01.024CrossRefGoogle 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).10.1017/S026357471700039XCrossRefGoogle Scholar
Elmokadem, T., “A Reactive Navigation Method of Quadrotor UAVs in Unknown Environments with Obstacles based on Differential-Flatness,” Australasian Conference on Robotics and Automation 2019 (ACRA) (2019).CrossRefGoogle Scholar
Hamel, T., Mahony, R., Lozano, R. and Ostrowski, J., “Dynamic modelling and configuration stabilization for an X4-flyer,” IFAC Proc. Vol. 35(1), 217–222 (2002).CrossRefGoogle Scholar
Faessler, M., Franchi, A. and Scaramuzza, D., “Differential flatness of quadrotor dynamics subject to rotor drag for accurate tracking of high-speed trajectories,” IEEE Rob. Autom. Lett. 3(2), 620–626 (2017).10.1109/LRA.2017.2776353CrossRefGoogle Scholar
Garcia, O., Rojo-Rodriguez, E. G., Sanchez, A., Saucedo, D. and Munoz-Vazquez, A., “Robust geometric navigation of a quadrotor UAV on SE (3),” Robotica 38(6), 1019–1040 (2020).10.1017/S0263574719001231CrossRefGoogle Scholar
Mellinger, D. and Kumar, V., “Minimum Snap Trajectory Generation and Control for Quadrotors,” 2011 IEEE International Conference on Robotics and Automation (IEEE, 2011) pp. 2520–2525.CrossRefGoogle Scholar
Mueller, M. W., Hehn, M. and D’Andrea, R., “A computationally efficient motion primitive for quadrocopter trajectory generation,” IEEE Trans. Rob. 31(6), 1294–1310 (2015).10.1109/TRO.2015.2479878CrossRefGoogle Scholar
Sanchez-Rodriguez, J. and Aceves-Lopez, A., “A survey on stereo vision-based autonomous navigation for multi-rotor MUAVs,” Robotica 36(8), 1225–1243 (2018).CrossRefGoogle Scholar
Naudet-Collette, S., Melbouci, K., Gay-Bellile, V., Ait-Aider, O. and Dhome, M., “Constrained RGBD-SLAM,” Robotica 39(2), 277–290 (2021).CrossRefGoogle Scholar
Kang, J. and Doh, N. L., “Full-DOF calibration of a rotating 2-D LIDAR with a simple plane measurement,” IEEE Trans. Rob. 32(5), 1245–1263 (2016).10.1109/TRO.2016.2596769CrossRefGoogle Scholar
Kownacki, C., “A concept of laser scanner designed to realize 3D obstacle avoidance for a fixed-wing UAV,” Robotica 34(2), 243–257 (2016).CrossRefGoogle Scholar