Prof. Shaojie SHEN and His Academic Descendants Received Honorable Mention for the 2020 IEEE Transactions on Robotics King-Sun Fu Memorial Best Paper Award
Prof. Shaojie SHEN and his academic descendants received Honorable Mention in the 2020 IEEE Transactions on Robotics King-Sun Fu Memorial Best Paper Award for their paper “Teach-Repeat-Replan: A Complete and Robust System for Aggressive Flight in Complex Environments”.
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.
“Teach-Repeat-Replan: A Complete and Robust System for Aggressive Flight in Complex Environments”
by Fei Gao; Luqi Wang; Boyu Zhou; Xin Zhou; Jie Pan; Shaojie Shen
Volume 36, Issue 5, pages 1526-1545, October 2020
The first author, Fei GAO, was PhD student supevisored by Prof. SHEN during 2016-2019 and is currently an Assistant Professor at Zhejiang University. Luqi WANG and Boyu ZHOU are current PhD students. Xin ZHOU is a current Zhejiang University MPhil student studying under Fei GAO. Jie PAN completed MPhil degree at HKUST in 2019.
This is the second time Prof. SHEN's research group received the same award. In 2019, Prof. SHEN and his postgraduate students Peiliang LI and Tong QIN received Honorable Mention in IEEE Transactions on Robotics King-Sun Fu Memorial Best Paper Award for their paper on “VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator” (See related news).