Abstract
Because unmanned aerial vehicles (UAVs) have relatively free movement compared to unmanned ground vehicles (UGVs), they can be adopted for various tasks. However, low payload and short flight time are the major limitations in operating UAVs. Consequently, lightening UAVs and multi-UAV systems are being researched. With the aim of utilizing the multi-UAV system efficiently, the relative position of each UAV is needed. In this study, we propose a low-cost relative position estimation method for the lightened multi-UAV system that uses only the range data between the UAVs on the cyber-physical system. In addition, our method uses the filtered ultra-wideband (UWB) data with reduced outliers by utilizing the sliding window and a low pass filter on the UWB raw data.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Lee, H., et al.: CAROS-Q: climbing aerial robot system adopting rotor offset with a quasi-decoupling controller. IEEE Robot. Autom. Lett. 6(4), 8490–8497 (2021)
Lee, H., et al.: Low-level pose control of tilting multirotor for wall perching tasks using reinforcement learning. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 9669–9676. IEEE (2021)
Qin, T., Li, P., Shen, S.: VINS-Mono: a robust and versatile monocular visual-inertial state estimator. IEEE Trans. Robot. 34(4), 1004–1020 (2018)
Bloesch, M., Omari, S., Hutter, M., Siegwart, R.: Robust visual inertial odometry using a direct EKF-based approach. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 298–304 (2015)
Lim, H., Jeon, J., Myung, H.: UV-SLAM: unconstrained line-based SLAM using vanishing points for structural mapping. IEEE Robot. Autom. Lett. 7(2), 1518–1525 (2022)
Choi, J., Myung, H.: BRM localization: UAV localization in GNSS-denied environments based on matching of numerical map and UAV images. In: Proceedings of the 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4537–4544. IEEE (2020)
Xu, W., Zhang, F.: Fast-LIO: a fast, robust LiDAR-inertial odometry package by tightly-coupled iterated Kalman filter. IEEE Robot. Autom. Lett. 6(2), 3317–3324 (2021)
Shan, T., Englot, B.: LEGO-LOAM: lightweight and ground-optimized LiDAR odometry and mapping on variable terrain. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4758–4765 (2018)
Wa̧sik, A., Ventura, R., Pereira, J.N., Lima, P.U., Martinoli, A.: Lidar-based relative position estimation and tracking for multi-robot systems. In: Robot 2015: Second Iberian Robotics Conference. AISC, vol. 417, pp. 3–16. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-27146-0_1
Yun, S., Sung, S., Lee, Y.J., Kang, T., Chun, S.: INS/vision sensor integrated system for precise relative position estimation using landmark. In: Proceedings of the IEEE National Aerospace and Electronics Conference, pp. 367–372. IEEE (2008)
Leutenegger, S., Furgale, P., Rabaud, V., Chli, M., Konolige, K., Siegwart, R.: Keyframe-based visual-inertial SLAM using nonlinear optimization. In: Proceedings of the Robotics Science and Systems (RSS) (2013)
Shin, S., Lee, E., Choi, J., Myung, H.: MIR-VIO: mutual information residual-based visual inertial odometry with UWB fusion for robust localization. In: Proceedings of the 21st International Conference on Control, Automation and Systems (ICCAS), pp. 91–96. IEEE (2021)
Jung, K.Y., Shin, S.J., Myung, H.: U-VIO: tightly coupled UWB visual inertial odometry for robust localization. In: Kim, J., et al. (eds.) RiTA 2021. LNNS, vol. 429, pp. 272–283. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-97672-9_24
Cossette, C.C., Shalaby, M., Saussié, D., Forbes, J.R., Le Ny, J.: Relative position estimation between two UWB devices with IMUs. IEEE Robot. Autom. Lett. 6(3), 4313–4320 (2021)
Shalaby, M., Cossette, C.C., Forbes, J.R., Le Ny, J.: Relative position estimation in multi-agent systems using attitude-coupled range measurements. IEEE Robot. Autom. Lett. 6(3), 4955–4961 (2021)
Choi, J., Lee, E., Shin, S., Myung, H.: Solving geometric constraints for relative position estimation using UWB sensors in multi-robot system. In: Proceedings of the World Congress on Advances in Nano, Bio, Robotics and Energy (ANBRE21). Techno-press (2021)
Agarwal, S., Mierle, K., Team, T.C.S.: Ceres Solver, no. 3 (2022). https://github.com/ceres-solver/ceres-solver
Cao, Y., Beltrame, G.: VIR-SLAM: visual, inertial, and ranging SLAM for single and multi-robot systems. Auton. Robot. 45(6), 905–917 (2021)
Acknowledgement
This work has been supported by the Unmanned Swarm CPS Research Laboratory Program of Defense Acquisition Program Administration and Agency for Defense Development (UD220005VD).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Jeong, M., Choi, J., Myung, H. (2023). Low-Cost UWB-Based Relative Position Estimation for Cyber Physical Multi-robot System. In: Jo, J., et al. Robot Intelligence Technology and Applications 7. RiTA 2022. Lecture Notes in Networks and Systems, vol 642. Springer, Cham. https://doi.org/10.1007/978-3-031-26889-2_10
Download citation
DOI: https://doi.org/10.1007/978-3-031-26889-2_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-26888-5
Online ISBN: 978-3-031-26889-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)