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September 7th, 2021 Zhichao Han, Zhepei Wang, Neng Pan, Yi Lin, Chao Xu, Fei Gao’ s work on “Fast-Racing: An Open-source Strong Baseline for SE(3) Planning in Autonomous Drone Racing” is accepted by IEEE Robotics and Automation Letters (RA-L) for publication.    

July 1st, 2021 Four conference papers are accepted by IROS 2021: FAST-Dynamic-Vision: Detection and Tracking Dynamic Objects with Event and Depth Sensing, Botao He, Haojia Li, Siyuan Wu, Dong Wang, Zhiwei Zhang, Qianli Dong, Chao Xu, Fei Gao. Autonomous Flights in Dynamic Environments with Onboard Vision, Yingjian Wang, Jialin Ji, Qianhao Wang, Chao Xu, Fei Gao. Visibility-aware Trajectory Optimization with Application to Aerial Tracking, Qianhao Wang, Yuman Gao, Jialin Ji, Chao Xu, Fei Gao. Learning-based 3D Occupancy Prediction for Autonomous Navigation in Occluded Environments, Lizi Wang, Hongkai Ye, Qianhao Wang, Yuman Gao, Chao Xu, Fei Gao.

Fast-Tracker is a systematic solution that uses an unmanned aerial vehicle (UAV) to aggressively and safely track an agile target. The open-source project releases the tracking algorithm with a simulation of a car-tracking scenario as a demo. Author: Zhichao Han*, Ruibin Zhang*, Neng Pan*, Chao Xu and Fei Gao from the ZJU Fast Lab Related Paper: Fast-Tracker: A Robust Aerial System for Tracking AgileTarget in Cluttered Environments, Zhichao Han*, Ruibin Zhang*, Neng Pan*, Chao Xu, Fei Gao, International Conference on Robotics and Automation (ICRA 2021) Video Links: youtube or bilibili Code Link:  https://github.com/ZJU-FAST-Lab/Fast-tracker External Forces Resilient Planner is a systematic motion planning framework that can resiliently generate safe trajectories under volatile external forces. This work integrates our previous work, VID-Fusion as the external force estimator. Author: Yuwei WU and Fei GAO from the ZJU Fast Lab Related Paper: External Forces Resilient Safe Motion Planning for Quadrotor, Yuwei Wu, Ziming Ding, Chao Xu, Fei Gao, submitted to IEEE Robotics and Automation Letter (RA-L). Video Links: youtube or bilibili Code Link: https://github.com/ZJU-FAST-Lab/forces_resilient_planner VID-Fusion is a work to estimate odometry and external force simultaneously by a tightly coupled Visual-Inertial-Dynamics state estimator. The open source project releases the code of VID-Fusion, along with an experimental dataset in the real word. Author: Ziming Ding, Tiankai Yang, Kunyi Zhang, Chao Xu, and Fei Gao from the ZJU FAST Lab. Related Paper: VID-Fusion: Robust Visual-Inertial-Dynamics Odometry for Accurate External Force Estimation, Ziming Ding, Tiankai Yang, Kunyi Zhang, Chao Xu, and Fei Gao, International Conference on Robotics and Automation (ICRA 2021) Video Links: Youtube or bilibili Code Link: https://github.com/ZJU-FAST-Lab/VID-Fusion

Dr. Fei Gao, the co-director of ZJU Field Autonomous System and Computing Laboratory (ZJU FAST Lab), received Honorable Mention in the 2020 IEEE Transactions on Robotics 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 a college or university of mainland China has won this honor as the first completion unit, and it is also the first time that IEEE-TRO has awarded this honor to a paper published in the field of unmanned aerial vehicles on IEEE-TRO. 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. By the time the news was released, this work had ranked among the top 50 popular articles in the IEEE-TRO database (ranked 14th) and received extensive attention and high praise from well-known scholars in the field […]