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FARO-Tracker: Fast and Robust Target Tracking System for UAVs in Urban Environment

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Robot Intelligence Technology and Applications 7 (RiTA 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 642))

Abstract

Tracking objects using unmanned aerial vehicles (UAVs) has been widely utilized in various fields. However, target tracking in a cluttered environment can be challenging because of the unexpected target movement and ambiguous surroundings. In this paper, a fast and robust target tracking system for UAVs is proposed to tackle these problems. A simple network-based detection module and a filter-based object tracking module are utilized and developed not to miss the target even in occlusion or among similar vehicles. The upcoming path of the target is predicted using the traces of a target, and the model predictive controller is utilized to track the non-uniform movement as they are. Moreover, the yaw compensator module is designed to track the target robustly to minimize the noise and react fast to the agile target motion. The performance of the proposed system is verified by tracking the target in challenging urban simulation environments (https://youtu.be/pMfhb25DqDU).

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References

  1. 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)

    Article  Google Scholar 

  2. Lee, D., Lee, E.M., Choi, D., Choi, J., Tirtawardhana, C., Myung, H.: M-BRIC: design of mass-driven bi-rotor with RL-based intelligent controller. In: 2022 19th International Conference on Ubiquitous Robots (UR), pp. 103–108. IEEE (2022)

    Google Scholar 

  3. Jung, S., Choi, D., Song, S., Myung, H.: Bridge inspection using unmanned aerial vehicle based on HG-SLAM: hierarchical graph-based SLAM. Remote Sens. 12(18), 3022 (2020)

    Article  Google Scholar 

  4. Lee, E.M., Choi, J., Lim, H., Myung, H.: Real: rapid exploration with active loop-closing toward large-scale 3D mapping using UAVs. In: 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4194–4198. IEEE (2021)

    Google Scholar 

  5. Han, Z., Zhang, R., Pan, N., Xu, C., Gao, F.: Fast-tracker: a robust aerial system for tracking agile target in cluttered environments. In: 2021 IEEE International Conference on Robotics and Automation (ICRA), pp. 328–334. IEEE (2021)

    Google Scholar 

  6. Bochkovskiy, A., Wang, C.Y., Liao, H.Y.M.: Yolov4: optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934 (2020)

  7. Gao, F., Wu, W., Lin, Y., Shen, S.: Online safe trajectory generation for quadrotors using fast marching method and Bernstein basis polynomial. In: 2018 IEEE International Conference on Robotics and Automation (ICRA), pp. 344–351. IEEE (2018)

    Google Scholar 

  8. Bewley, A., Ge, Z., Ott, L., Ramos, F., Upcroft, B.: Simple online and realtime tracking. In: 2016 IEEE International Conference on Image Processing (ICIP), pp. 3464–3468. IEEE (2016)

    Google Scholar 

  9. Nägeli, T., Alonso-Mora, J., Domahidi, A., Rus, D., Hilliges, O.: Real-time motion planning for aerial videography with dynamic obstacle avoidance and viewpoint optimization. IEEE Robot. Autom. Lett. 2(3), 1696–1703 (2017)

    Article  Google Scholar 

  10. Penin, B., Giordano, P.R., Chaumette, F.: Vision-based reactive planning for aggressive target tracking while avoiding collisions and occlusions. IEEE Robot. Autom. Lett. 3(4), 3725–3732 (2018)

    Article  Google Scholar 

  11. Lee, E.C.M., Choi, D., Myung, H.: Peacock exploration: a lightweight exploration for UAV using control-efficient trajectory. In: Chew, E., et al. (eds.) RiTA 2020. LNME, pp. 136–146. Springer, Singapore (2021). https://doi.org/10.1007/978-981-16-4803-8_16

    Chapter  Google Scholar 

  12. Koenig, N., Howard, A.: Design and use paradigms for Gazebo, an open-source multi-robot simulator. In: 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No. 04CH37566), vol. 3, pp. 2149–2154. IEEE (2004)

    Google Scholar 

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Acknowledgement

This work was supported by Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by Korea government (MSIT) (No.2020-0-00440, Development of Artificial Intelligence Technology that Continuously Improves Itself as the Situation Changes in the Real World).

Author information

Authors and Affiliations

  1. Robotics Program, KAIST (Korea Advanced Institute of Science and Technology), Daejeon, 34141, Republic of Korea

    Dongkyu Lee

  2. School of Electrical Engineering, KAIST (Korea Advanced Institute of Science and Technology), Daejeon, 34141, Republic of Korea

    EungChang Mason Lee, Hyungtae Lim, Seungwon Song & Hyun Myung

Authors
  1. Dongkyu Lee
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  2. EungChang Mason Lee
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  3. Hyungtae Lim
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  4. Seungwon Song
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  5. Hyun Myung
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Corresponding author

Correspondence to Hyun Myung .

Editor information

Editors and Affiliations

  1. School of Information and Communication Technology, Griffith University, Southport, Australia

    Jun Jo

  2. Department of Aerospace Engineering, KAIST, Daejeon, Korea (Republic of)

    Han-Lim Choi

  3. School of Information and Communication Technology, Griffith University, Southport, Australia

    Marde Helbig

  4. Department of Mechanical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, Korea (Republic of)

    Hyondong Oh

  5. Department of Mechanical Engineering, KAIST, Daejeon, Korea (Republic of)

    Jemin Hwangbo

  6. Department of Mechanical Engineering, KAIST, Daejeon, Korea (Republic of)

    Chang-Hun Lee

  7. School of Information and Communication Technology, Griffith University, Southport, Australia

    Bela Stantic

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Lee, D., Lee, E.M., Lim, H., Song, S., Myung, H. (2023). FARO-Tracker: Fast and Robust Target Tracking System for UAVs in Urban Environment. 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_20

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