QP Chaser: Polynomial Trajectory Generation for Autonomous Aerial Tracking

Maintaining the visibility of the target is one of the major objectives of aerial tracking missions. This paper proposes a target-visible trajectory planning pipeline using quadratic programming (QP). Our approach can handle various tracking settings, including 1) single- and dual-target following and 2) both static and dynamic environments, unlike other works that focus on a single specific setup. In contrast to other studies that fully trust the predicted trajectory of the target and consider only the visibility of the target’s center, our pipeline considers error in target path prediction and the entire body of the target to maintain the target visibility robustly. First, a prediction module uses a sample-check strategy to quickly calculate the reachable areas of moving objects, which represent the areas their bodies can reach, considering obstacles. Subsequently, the planning module formulates a single QP problem, considering path homotopy, to generate a tracking trajectory that maximizes the visibility of the target’s reachable area among obstacles. The performance of the planner is validated in multiple scenarios, through high-fidelity simulations and real-world experiments. Note to Practitioners—This paper proposes an aerial target tracking framework applicable to both single- and dual-target following missions. This paper proposes the prediction of the reachable area of moving objects and the generation of a target- visible trajectory, both of which are computed in real-time. Since the proposed planner considers the possible reach area of moving objects, the generated trajectory of the drone is robust to the prediction inaccuracy in terms of the target visibility. Our system can be utilized in crowded environments with multiple moving objects and extended to multiple-target scenarios. We extensively validate our system through several real-world experiments to show practicality. Received 11 July 2024; revised 6 November 2024, 22 May 2025, and 1 September 2025; accepted 30 October 2025. Date of publication 5 November 2025; date of current version 24 November 2025. This article was recommended for publication by Associate Editor T. Zhang and Editor P. Rocco upon evaluation of the reviewers’ comments. This work was supported by the Unmanned Vehicles Core Technology Research and Development Program through the National Research Foundation of Korea (NRF) and Unmanned Vehicle Advanced Research Center (UVARC) funded by the Ministry of Science and Information and Communication Technology (ICT) under Grant NRF-2020M3C1C1A010864. (Corresponding author: H. Jin Kim.) Yunwoo Lee was with the Artificial Intelligence Institute, Seoul National University, Seoul 08826, South Korea. He is now with the Robotics Institute, Carnegie Mellon University, Pittsburgh, PA 15213 USA (e-mail: yunwool@andrew.cmu.edu). Jungwon Park is with the Department of Mechanical System Design Engineering, Seoul National University of Science and Technology, Seoul 01181, South Korea (e-mail: jungwonpark@seoultech.ac.kr). Seungwoo Jung, Dahyun Oh, and H. Jin Kim are with the Department of Aerospace Engineering and Automation and Systems Research Institute, Seoul National University, Seoul 08826, South Korea (e-mail: tmddn833@snu.ac.kr; qlass33@snu.ac.kr; hjinkim@snu.ac.kr). Boseong Jeon is with the Samsung Research, Samsung Electronics, Seoul 06765, South Korea (e-mail: junbs95@gmail.com). This article has supplementary downloadable material available at https://doi.org/10.1109/TASE.2025.3629046, provided by the authors. Digital Object Identifier 10.1109/TASE.2025.3629046