Abstract
Unmanned Aerial Vehicles (UAVs) have drawn attention in recent years due to the wide spectrum of utilization. Out of those areas, autonomous aerial tracking is one of the fields that could be applied widely. The main purpose of this paper is to develop a novel trajectory planner for autonomous aerial tracking that is feasible even in areas where light is scarce. The poor lighting condition is handled by equipping and utilizing a thermal camera and a Time-of-Flight (ToF) camera on the UAV. In addition, minimum snap trajectory is adopted to track the target and kinodynamics of the UAV is considered at the same time, which is optimized by solving a quadratic programming with corridor constraints considering the noises of the cameras. Moreover, based on the former information, the proposed system predicts the future motion of the target considering dynamic constraints. The performance of the proposed approach is validated by intensive simulations and real-world experiments.
S. Lee and S. Chang—Both authors have equally contributed.
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Acknowledgement
This paper was funded by Korea Electric Power Corporation (Development of Robot System for Patrol and Inspection of Underground Power Transmission Tunnels). The students are supported by BK21 FOUR. Special thanks to Meerecompany Inc. for providing their sensor.
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Lee, S. et al. (2022). TPL: Trajectory Planner for Target Tracking in Low-Light Environments. In: Kim, J., et al. Robot Intelligence Technology and Applications 6. RiTA 2021. Lecture Notes in Networks and Systems, vol 429. Springer, Cham. https://doi.org/10.1007/978-3-030-97672-9_2
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DOI: https://doi.org/10.1007/978-3-030-97672-9_2
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