Hostname: page-component-8448b6f56d-qsmjn Total loading time: 0 Render date: 2024-04-24T17:18:52.278Z Has data issue: false hasContentIssue false
  • English
  • Français

Rotorcraft aerial vehicle’s contact-based landing and vision-based localization research

Published online by Cambridge University Press:  08 November 2022

Xiangdong Meng Open the ORCID record for Xiangdong Meng [Opens in a new window] ,
Haoyang Xi ,
Jinghe Wei ,
Yuqing He ,
Jianda Han  and
Aiguo Song
Xiangdong Meng*
Affiliation:
Microsystem Research Center, the 58th Research Institute of China Electronics Technology Group Corporation, Wuxi, China School of Instrument Science and Engineering, Southeast University, Nanjing, China
Haoyang Xi
Affiliation:
Microsystem Research Center, the 58th Research Institute of China Electronics Technology Group Corporation, Wuxi, China
Jinghe Wei
Affiliation:
Microsystem Research Center, the 58th Research Institute of China Electronics Technology Group Corporation, Wuxi, China
Yuqing He
Affiliation:
Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China
Jianda Han
Affiliation:
College of Artificial Intelligence, Nankai University, Tianjin, China
Aiguo Song
Affiliation:
School of Instrument Science and Engineering, Southeast University, Nanjing, China
*
*Corresponding author. E-mail: xdmeng09@gmail.com
Get access Rights & Permissions [Opens in a new window]

Abstract

A novel concept—the contact-based landing on a mobile platform—is proposed in this paper. An adaptive backstepping controller is designed to deal with the unknown disturbances in the interactive process, and the contact-based landing mission is implemented under the hybrid force/motion control framework. A rotorcraft aerial vehicle system and a ground mobile platform are designed to conduct flight experiments, evaluating the feasibility of the proposed landing scheme and control strategy. To the best of our knowledge, this is the first time a rotorcraft unmanned aerial vehicle has been implemented to conduct a contact-based landing. To improve system autonomy in future applications, vision-based recognition and localization methods are studied, contributing to the detection of a partially occluded cooperative object or at a close range. The proposed recognition algorithms are tested on a ground platform and evaluated in several simulated scenarios, indicating the algorithm’s effectiveness.

Keywords

UAVlandingcontact-basedmobileplatform
Type
Research Article
Information
Robotica , Volume 41 , Issue 4 , April 2023 , pp. 1127 - 1144
Copyright
© The Author(s), 2022. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Eker, R., Aydın, A. and Hubl, J., “Unmanned aerial vehicle (UAV)-based monitoring of a landslide: Gallenzerkogel landslide (Ybbs-Lower Austria) case study,” Environ. Monit. Assess. 190(1), 1–14 (2018).CrossRefGoogle Scholar
Oishi, K. and Jimbo, T., “Autonomous Cooperative Transportation System Involving Multi-Aerial Robots with Variable Attachment Mechanism,” In: 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE, 2021) pp. 6322–6328.CrossRefGoogle Scholar
Lan, Y. and Chen, S., “Current status and trends of plant protection UAV and its spraying technology in China,” Int. J. Precis. Agricultural Aviation 1(1), 1–9 (2018).CrossRefGoogle Scholar
Polvara, R., Sharma, S., Wan, J., Manning, A. and Sutton, R., “Autonomous vehicular landings on the deck of an unmanned surface vehicle using deep reinforcement learning,” Robotica 37(11), 1867–1882 (2019).CrossRefGoogle Scholar
https://www.militaryfactory.com/aircraft/detail.php?aircraft_id=889.Google Scholar
https://www.designation-systems.net/dusrm/app2/q-8.html.Google Scholar
Duranti, S. and Aerosystems, S., “Autonomous Take-Off and Landing of the SHARC Technology Demonstrator,” In: 25th International Congress of the Aeronautical Sciences (2006) pp. 1–10.Google Scholar
https://www.listcompany.org/Uconsystem_Co_Ltd_Info.html.Google Scholar
https://umsskeldar.aero/v-200-skeldar/.Google Scholar
Quenzel, J., Splietker, M., Pavlichenko, D., Schleich, D., Lenz, C., Schwarz, M., Schreiber, M., Beul, M. and Behnke, S., “Autonomous Firefighting with a UAV-UGV Team at MBZIRC 2020,” In: 2021 International Conference on Unmanned Aircraft Systems (ICUAS) (IEEE, 2021) pp. 934–941.CrossRefGoogle Scholar
Baca, T., Stepan, P., Spurny, V., Hert, D., Penicka, R., Saska, M., Thomas, J., Loianno, G. and Kumar, V., “Autonomous landing on a moving vehicle with an unmanned aerial vehicle,” J. Field Robot. 36(5), 874–891 (2019).CrossRefGoogle Scholar
Beul, M., Nieuwenhuisen, M., Quenzel, J., Rosu, R. A., Horn, J., Pavlichenko, D., Houben, S. and Behnke, S., “Team NimbRo at MBZIRC 2017: Fast landing on a moving target and treasure hunting with a team of micro aerial vehicles,” J. Field Robot. 36(1), 204–229 (2019).CrossRefGoogle Scholar
Jin, R., Owais, H. M., Lin, D., Song, T. and Yuan, Y., “Ellipse proposal and convolutional neural network discriminant for autonomous landing marker detection,” J Field Robot. 36(1), 6–16 (2019).CrossRefGoogle Scholar
Li, Z., Meng, C., Zhou, F., Ding, X., Wang, X., Zhang, H., Guo, P. and Meng, X., “Fast vision based autonomous detection of moving cooperative target for unmanned aerial vehicle landing,” J Field Robot. 36(1), 34–48 (2019).CrossRefGoogle Scholar
Kumar, A., Vohra, M., Prakash, R. and Behera, L., “Towards Deep Learning Assisted Autonomous UAVs for Manipulation Tasks in GPS-Denied Environments,” In: 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE, 2020) pp. 1613–1620.CrossRefGoogle Scholar
Paris, A., Lopez, B. T. and How, J. P., “Dynamic Landing of an Autonomous Quadrotor on a Moving Platform in Turbulent Wind Conditions,” In: 2020 IEEE International Conference on Robotics and Automation (ICRA) (IEEE, 2020) pp. 9577–9583.CrossRefGoogle Scholar
Zhang, X., Fang, Y., Zhang, X., Jiang, J. and Chen, X., “A novel geometric hierarchical approach for dynamic visual servoing of quadrotors,” IEEE Trans. Ind. Electron. 67(5), 3840–3849 (2019).CrossRefGoogle Scholar
Rodriguez-Ramos, A., Sampedro, C., Bavle, H., Puente, P. D. L. and Campoy, P., “A deep reinforcement learning strategy for UAV autonomous landing on a moving platform,” J. Intell. Robot. Syst. 93(1), 351–366 (2019).CrossRefGoogle Scholar
Kooi, J. E. and Babuska, R., “Inclined Quadrotor Landing Using Deep Reinforcement Learning,” In: 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE, 2021) pp. 2361–2368.CrossRefGoogle Scholar
Paul, H., Miyazaki, R., Ladig, R. and Shimonomura, K., “Landing of a Multirotor Aerial Vehicle on an Uneven Surface Using Multiple On-Board Manipulators,” In: 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE, 2019) pp. 1926–1933.CrossRefGoogle Scholar
Meng, X., He, Y. and Han, J., “Survey on aerial manipulator: System, modeling, and control,” Robotica 38(7), 1288–1317 (2020).CrossRefGoogle Scholar
Ollero, A., Heredia, G., Franchi, A., Antonelli, G., Kondak, K., Sanfeliu, A., Viguria, A., Martinez-de Dios, J. R., Pierri, F., Cortes, J., Santamaria-Navarro, A., Trujillo, M. A., Balachandran, R., Andrade-Cetto, J. and Rodriguez, A., “The AEROARMS project: Aerial robots with advanced manipulation capabilities for inspection and maintenance,” IEEE Robot. Autom. Mag. 25(4), 12–23 (2018).CrossRefGoogle Scholar
Ryll, M., Muscio, G., Pierri, F., Cataldi, E., Antonelli, G.,Caccavale, F., Bicego, D. and Franchi, A., “6D interaction control with aerial robots: The flying end-effector paradigm,” Int. J. Robot. Res. 38(9), 1045–1062 (2019).CrossRefGoogle Scholar
Brunner, M., Giacomini, L., Siegwart, R. and Tognon, M., “Energy tank-based policies for robust aerial physical interaction with moving objects,” arXiv preprint arXiv: 2202.06755, 2022.Google Scholar
Meng, X., He, Y., Li, Q., Gu, F., Yang, L., Yan, T. and Han, J., “Contact Force Control of an Aerial Manipulator in Pressing an Emergency Switch Process,” In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE, 2018) pp. 2107–2113.CrossRefGoogle Scholar
Meng, X., He, Y. and Han, J., “Design and Implementation of a Contact Aerial Manipulator System for Glass-Wall Inspection Tasks,” In: 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE, 2019) pp. 215–220.CrossRefGoogle Scholar
Faigl, J. and Vana, P., “Surveillance planning with Bezier curves,” IEEE Robot. Autom. Lett. 3(2), 750–757 (2018).CrossRefGoogle Scholar
Zhang, X., Fang, Y., Zhang, X., Jiang, J. and Chen, X., “Dynamic image-based output feedback control for visual servoing of multirotors,” IEEE Trans. Ind. Inform. 16(12), 7624–7636 (2020).CrossRefGoogle Scholar
Borowczyk, A., Nguyen, D.T., Phu-Van Nguyen, A., Nguyen, D. Q., Saussie, D. and Ny, J. L., “Autonomous landing of a multirotor micro air vehicle on a high velocity ground vehicle,” IFAC-PapersOnline 50(1), 10488–10494 (2017).CrossRefGoogle Scholar
Tomasi, C. and Manduchi, R., “Bilateral Filtering for Gray and Color Images,” In: Sixth International Conference on Computer Vision (IEEE Cat. No. 98CH36271) (IEEE, 1998) pp. 839–846.Google Scholar
Suzuki, S., “Topological structural analysis of digitized binary images by border following,” Compu. Vis. Graph. Image Process. 30(1), 32–46 (1985).CrossRefGoogle Scholar