无人系统与自主计算实验室

Dr. Fei Gao Received Honorable Mention in the 2020 IEEE-TRO King-Sun Fu Memorial Best Paper Award

Recently, Dr. Fei Gao, the deputy director and Co-PI of the Field Autonomous System & Computing Lab (Fast Lab) of College of Control Science and Engineering, received Honorable Mention in the 2020 IEEE Transactions on Robotics (IEEE-TRO) King-Sun Fu Memorial Best Paper Award for the paper “Teach-Repeat-Replan: A Complete and Robust System for Aggressive Flight in Complex Environments” as the first and corresponding author. This is the first time that the colleges and universities of the Chinese Mainland have won the honor as the leading institution. It is also the first time that IEEE-TRO has awarded this honor to the papers published in IEEE-TRO from the UAV community.

There is a massive market for consumer drones nowadays. However, most of the operators of consumer drones are not professional pilots and would struggle to generate their ideal trajectory for a long time. In some scenarios, such as drone racing or aerial filming, it is impossible for a beginner-level pilot to control the drone to finish the race safely or take an aerial video smoothly without months of training. In a drone racing competition, each quadrotor is controlled by a human pilot to fly through several gates towards the terminal as quickly as possible. In the racing flight, collisions must be avoided to ensure safety, while the flight aggressiveness is expected to be extremely high. However, it is hard for a human pilot to master the skill of balancing speed and safety. As opposed to drone racing, aerial filming/videography does not prefer high speed, but good motion smoothness, because gentle transitions are typically good for generating aesthetical videos.

Based on the above observations, this paper presents a complete solution towards robust aerial autonomy, enabling a drone to accomplish a complicated task with professional performance under merely rough human operations. The human operator may provide an arbitrarily slow or jerky trajectory with an expected topological structure. The system then autonomously converts this poor teaching trajectory to a topologically equivalent and local optimal one. The aggressiveness of the generated repeating motions is tunable, which can meet speed requirements ranging from drone racing to aerial filming. Moreover, during the repeating flight, the system locally observes environmental changes and replans safe trajectories to avoid moving obstacles. The proposed system extends the classical robotics teach-and-repeat framework and is named as Teach-Repeat-Replan.

Before the announcement of this news, the work was ranked among the top 50 hot articles in the IEEE-TRO database (ranked 14th), and was widely concerned and highly marked by eminent scholars at home and abroad. Kevin Lynch, Editor-in-Chief of IEEE Transactions on Robotics, Professor of Northwestern University, IEEE fellow, author of Modern Robotics and other reputable robotics textbooks, when awarding the King-Sun Fu Best Paper Award Honorable Mention, commented, “The T-RO editorial board was impressed by the theoretical and experimental contributions of your work.”

IEEE-TRO is IEEE’s top journal in the field of robotics, including all aspects of research in this sector. IEEE-TRO has very high requirements for the quality of papers, which demands outstanding theoretical and practical contributions to the field of robotics that can promote the development of robotics. The “King-Sun Fu Best Paper Award Honorable Mention” is established to recognize the best work in the papers published by IEEE-TRO every year. Therefore, it is a high honor for all robotics researchers to publish papers in IEEE-TRO and win the award at the same time.

Related Links:

  • Report Link from College of Control Science and Engineering, Zhejiang University

http://www.cse.zju.edu.cn/redir.php?catalog_id=1055601&object_id=1179075

  • About the Paper

https://ieeexplore.ieee.org/document/9102390

  • Open Source of Our Work

https://github.com/HKUST-Aerial-Robotics/Teach-Repeat-Replan

  • Video

https://www.bilibili.com/video/BV1Fx411o78w?from=search&seid=4539658003138370390

  • FAST Lab

http://zju-fast.com