Qiuyu (Joey) Yu was a visiting scholar from Shanghai Jiao Tong University, where he pursued his Ph.D. degree with the School of Aeronautics and Astronautics. He visited the FSC lab in 2022 to work on visuo-servo control for aggressive quadrotor control with Chao Qin.
Research Interests
Motion planning of UAVs, SLAM, combinatorial optimization, reinforcement learning
Education
Ph.D. Candidate – Shanghai Jiao Tong University (current)
B. S. – Central South University – Aeronautics and Astronautics (2017)
Publications
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.},
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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}
}