Chao Qin is a Ph.D. Candidate with the Flight Systems and Control lab. His research focuses on the visual servo based control of quadrotors for the purposes of enabling autonomous drone racing.
Research Interests
Aggressive quadrotor flight, visual servo based control, autonomous drone racing
Education
Ph.D. Student, University of Toronto, Aerospace Science and Engineering (Sept. 2020 – Present)
M.Sc. – Shanghai Jiao Tong University – Aerospace Engineering (2019)
B.Eng. – Xi’dian University – Electrical Engineering (2016)
Publications
2024
Qin*, Chao; Michet*, Maxime S. J.; Chen*, Jingxiang; Liu, Hugh H. -T.
Time-Optimal Gate-Traversing Planner for Autonomous Drone Racing Inproceedings
In: 2024 IEEE International Conference on Robotics and Automation (ICRA), IEEE, Best Paper Award Finalist!, 2024.
@inproceedings{ICRA2024CQ,
title = {Time-Optimal Gate-Traversing Planner for Autonomous Drone Racing},
author = {Chao Qin* and Maxime S.J. Michet* and Jingxiang Chen* and Hugh H. -T. Liu},
year = {2024},
date = {2024-01-31},
urldate = {2024-01-31},
booktitle = {2024 IEEE International Conference on Robotics and Automation (ICRA)},
publisher = {IEEE, Best Paper Award Finalist!},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
2023
Qin*, Chao; Yu*, Qiuyu; Go*, Shing Hei Helson; Liu, Hugh H. -T.
Perception-Aware Image-Based Visual Servoing of Aggressive Quadrotor UAVs Journal Article
In: IEEE/ASME Transactions on Mechatronics, 2023.
@article{TMech2023CQ,
title = {Perception-Aware Image-Based Visual Servoing of Aggressive Quadrotor UAVs},
author = {Chao Qin* and Qiuyu Yu* and Shing Hei Helson Go* and Hugh H. -T. Liu},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
journal = {IEEE/ASME Transactions on Mechatronics},
abstract = {The maintenance of visual features within the sensor field of view (FOV) poses a significant challenge for underactuated aerial vehicles like quadrotors, especially during aggressive maneuvers. However, existing image-based visual servo control (IBVS) methods rely on strict target visibility assumptions or impose excessive constraints on the quadrotor's agility to meet this requirement. Furthermore, the effectiveness of the visibility constraint defined in prior works remains unverified in aggressive flight tests. To address these issues, we present a robust IBVS scheme for quadrotors to perform aggressive maneuvers while ensuring target visibility. Based on the nonlinear model predictive control (NMPC) framework, we propose a novel underactuation compensation scheme to eliminate the need for a virtual camera frame, which enables us to formulate the true target visibility constraint. We then introduce a quaternion-based representation of spherical visual features to handle the nonsmooth vector field problem on the 2-sphere and derive its corresponding image kinematics. We validate our method through three challenging visual servo tasks where agile maneuvers are desired: fast landing, aggressive long-distance flight, and dynamic object tracking. Extensive simulation and experiment show that our method consistently achieves a target-visible rate of 100% in all image frames, even under a maximum pitch of 21.04$^circ$. The results validate the effectiveness of our visibility constraint under large robot rotations and underscore its importance in enabling robust and aggressive flights.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The maintenance of visual features within the sensor field of view (FOV) poses a significant challenge for underactuated aerial vehicles like quadrotors, especially during aggressive maneuvers. However, existing image-based visual servo control (IBVS) methods rely on strict target visibility assumptions or impose excessive constraints on the quadrotor's agility to meet this requirement. Furthermore, the effectiveness of the visibility constraint defined in prior works remains unverified in aggressive flight tests. To address these issues, we present a robust IBVS scheme for quadrotors to perform aggressive maneuvers while ensuring target visibility. Based on the nonlinear model predictive control (NMPC) framework, we propose a novel underactuation compensation scheme to eliminate the need for a virtual camera frame, which enables us to formulate the true target visibility constraint. We then introduce a quaternion-based representation of spherical visual features to handle the nonsmooth vector field problem on the 2-sphere and derive its corresponding image kinematics. We validate our method through three challenging visual servo tasks where agile maneuvers are desired: fast landing, aggressive long-distance flight, and dynamic object tracking. Extensive simulation and experiment show that our method consistently achieves a target-visible rate of 100% in all image frames, even under a maximum pitch of 21.04$^circ$. The results validate the effectiveness of our visibility constraint under large robot rotations and underscore its importance in enabling robust and aggressive flights.
Qin*, Chao; Yu*, Qiuyu; Go*, Shing Hei Helson; Liu, Hugh H. -T.
Perception-Aware Image-Based Visual Servoing of Aggressive Quadrotor UAVs Inproceedings
In: Advanced Intelligent Mechatronics (AIM), IEEE, 2023.
@inproceedings{AIM2023CQ,
title = {Perception-Aware Image-Based Visual Servoing of Aggressive Quadrotor UAVs},
author = {Chao Qin* and Qiuyu Yu* and Shing Hei Helson Go* and Hugh H. -T. Liu},
url = {https://www.youtube.com/watch?v=X2-SMGD99oA},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
booktitle = {Advanced Intelligent Mechatronics (AIM)},
journal = {IEEE/ASME International Conference on Advanced Intelligent Mechatronics},
publisher = {IEEE},
abstract = {The maintenance of visual features within the sensor field of view (FOV) poses a significant challenge for underactuated aerial vehicles like quadrotors, especially during aggressive maneuvers. However, existing image-based visual servo control (IBVS) methods rely on strict target visibility assumptions or impose excessive constraints on the quadrotor's agility to meet this requirement. Furthermore, the effectiveness of the visibility constraint defined in prior works remains unverified in aggressive flight tests. To address these issues, we present a robust IBVS scheme for quadrotors to perform aggressive maneuvers while ensuring target visibility. Based on the nonlinear model predictive control (NMPC) framework, we propose a novel underactuation compensation scheme to eliminate the need for a virtual camera frame, which enables us to formulate the true target visibility constraint. We then introduce a quaternion-based representation of spherical visual features to handle the nonsmooth vector field problem on the 2-sphere and derive its corresponding image kinematics. We validate our method through three challenging visual servo tasks where agile maneuvers are desired: fast landing, aggressive long-distance flight, and dynamic object tracking. Extensive simulation and experiment show that our method consistently achieves a target-visible rate of 100% in all image frames, even under a maximum pitch of 21.04$^circ$. The results validate the effectiveness of our visibility constraint under large robot rotations and underscore its importance in enabling robust and aggressive flights.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
The maintenance of visual features within the sensor field of view (FOV) poses a significant challenge for underactuated aerial vehicles like quadrotors, especially during aggressive maneuvers. However, existing image-based visual servo control (IBVS) methods rely on strict target visibility assumptions or impose excessive constraints on the quadrotor's agility to meet this requirement. Furthermore, the effectiveness of the visibility constraint defined in prior works remains unverified in aggressive flight tests. To address these issues, we present a robust IBVS scheme for quadrotors to perform aggressive maneuvers while ensuring target visibility. Based on the nonlinear model predictive control (NMPC) framework, we propose a novel underactuation compensation scheme to eliminate the need for a virtual camera frame, which enables us to formulate the true target visibility constraint. We then introduce a quaternion-based representation of spherical visual features to handle the nonsmooth vector field problem on the 2-sphere and derive its corresponding image kinematics. We validate our method through three challenging visual servo tasks where agile maneuvers are desired: fast landing, aggressive long-distance flight, and dynamic object tracking. Extensive simulation and experiment show that our method consistently achieves a target-visible rate of 100% in all image frames, even under a maximum pitch of 21.04$^circ$. The results validate the effectiveness of our visibility constraint under large robot rotations and underscore its importance in enabling robust and aggressive flights.
2022
Yu*, Qiuyu; Qin*, Chao; Luo, Lingkun; Liu, Hugh H-T; Hu, Shiqiang
CPA-Planner: Motion Planner with Complete Perception Awareness for Sensing-Limited Quadrotors Journal Article
In: IEEE Robotics and Automation Letters, 2022.
@article{yu2022cpa,
title = {CPA-Planner: Motion Planner with Complete Perception Awareness for Sensing-Limited Quadrotors},
author = {Qiuyu Yu* and Chao Qin* and Lingkun Luo and Hugh H-T Liu and Shiqiang Hu},
url = {https://www.flight.utias.utoronto.ca/fsc/wp-content/uploads/2023/05/RAL2022JY_accept.pdf},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {IEEE Robotics and Automation Letters},
publisher = {IEEE},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
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