April 17th, 2021 F. Gao, L. Wang, B. Zhou, X. Zhou, J. Pan and S. Shen’s paper “Teach-Repeat-Replan: A Complete and Robust System for Aggressive Flight in Complex Environments” received the IEEE T-RO 2020 King-Sun Fu Memorial Best Paper Award Honorable Mention.
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February 28th, 2021 Seven conference papers are accepted by ICRA 2021: VID-Fusion: Robust Visual-Inertial-Dynamics Odometry for Accurate External Force Estimation, Ziming Ding, Tiankai Yang, Kunyi Zhang, Chao Xu, Fei Gao. EGO-Swarm: A Fully Autonomous and Decentralized Quadrotor Swarm System in Cluttered Environments, Xin Zhou, Jiangchao Zhu, Hongyu Zhou, Chao Xu, Fei Gao. Fast-Tracker: A Robust Aerial System for Tracking Agile Target in Cluttered Environments, Zhichao Han, Ruibin Zhang, Neng Pan, Chao Xu, Fei Gao. Mapless-Planner: A Robust and Fast Planning Framework for Aggressive Autonomous Flight without Map Fusion, Jialin Ji, Zhepei Wang, Yingjian Wang, Chao Xu, Fei Gao. EVA-Planner: Environmental Adaptive Quadrotor Planning, Lun Quan, Zhiwei Zhang, Chao Xu, Fei Gao. Generating Large-Scale Trajectories Efficiently using Double Descriptions of Polynomials, Zhepei Wang, Hongkai Ye, Chao Xu, Fei Gao. Whole-Body Real-Time Motion Planning for Multicopters, Shaohui Yang, Botao He, Zhepei Wang, Chao Xu, Fei, Gao. ICRA 2021 will be held in Xi’an, China from May 30th to June 5th.
Author Zhepei Wang, Xin Zhou, Chao Xu and Fei Gao Source code (to be released) of the proposed framework: https://github.com/ZJU-FAST-Lab/GCOPTER Task-specified Experiments and Simulations: 1. Robust Real-Time SE(3) Planning: youtube or bilibili. (Reported by IEEE Spectrum Website!) 2. Multicopter Swarms Planning: youtube or bilibili. (Also Reported by IEEE Spectrum Website!) 3. Long-Distance Drone Racing Planning: youtube or bilibili. (Published in IEEE RA-L) 4. Gaze Teleoperation Planning: youtube or bilibili. 5. Formation Keeping Planning: youtube or bilibili. 6. A variety of applications powered by GCOPTER or MINCO are not listed here, such as: visibility-aware aerial tracking or planning with nonlinear drag effects.
Introduction Visual odometry has made tremendous progress since Mars Exploration Rovers was exploited, but studies focus less on the platform itself. In order to make the state estimation better applied, we focus on details about the dynamical modeling of quadrotors and propose to make the most of all kinds of sensory data to acquire all states of any quadrotor platform. In the series of work, the identification of quadrotor dynamical system is prime, which is between Newton-Euler dynamics and rotor rpm and motor current. Then, an all-states estimator is designed to fuse different kinds of sensors, which leads to more accurate pose estimation and addictive generalized external force estimation without losing real-time and robustness. Related Research: VID-Fusion: Robust Visual-Inertial-Dynamics Odometry for Accurate External Force Estimation, Ziming Ding, Tiankai Yang, Kunyi Zhang, Chao Xu, Fei Gao, the International Conference on Robotics and Automation (ICRA 2021). [paper]
Introduction Deep learning and reinforcement learning are bringing changes into robotics. In IARC challenge, we designed a hierarchical decision and control for a continuous multi-target problem: policy evaluation with action delay. We further proposed Time-in-Action reinforcement learning, mapping the state-action pair to the time accomplishing the action by its underlying controller. We also used fusing raw fisheye image and attitude data to detect and locate objects in a 3D environment with a deep neural network. Recently we utilized the deep learning method to leverage the performance of robotic navigation. We used the self-supervised learning method to complete obscured parts of obstacles and combine this map prediction during planning to accomplish fast and safe navigation. We are looking forward to learning biased sampling in trajectory planning. We are also interested in model-based reinforcement learning, as well as learning-based low-level controllers and learning-based state estimation. Video
Introduction ZMART is the abbreviation for the Zhejiang University Micro-Aerial Robotics Team, majorly for the International Aerial Robotics Competition in the Asia-Pacific Venue. ZMART won the Best System Design Award (2015 in Beihang University) and the First Prize Award (2016&2017 in Beihang University). On August 27, 2018, assessed by the international Aerial Robot Competition (IARC) committee, ZMART won champion of mission 7 of IARC and won the prize of US $20000. Zhejiang University became the seventh world champion of IARC after Stanford University (1995), Carnegie Mellon University (1997), Berlin University of Technology (2000), Georgia Institute of Technology (2008), Massachusetts Institute of Technology (2009) and Tsinghua University (2013). More information please refer to the link. In 2018, the 11th WRSC and IRSC was held in Sounthampton, UK. As first time participants, our team ZMART won the 3rd place of WRSC 2018 Micro-sailiboat class. In 2019, the 12th WRSC and IRSC was held in Ningbo, China. Our team ZMART won the 1st place of WRSC 2019. In the same year, ZMART won the 2nd place of DJI RoboMaster AI Challenge, with $15,000 prize. Award Research Area Dynamics and Control: Design and Modelling, Disturbance Control, Trajectory Generation, Formation Environment Sensing: Computer Vision, Machine Learning, Detection and Tracking, Visual Odometry, SLAM Artificial Intelligence: Reinforcement Learning, Deep Learning, Human in the Loop, Situation Awareness ZMART featured in media 2016: http://47.243.9.220/zmart-featured-in-media/ ZMART Performance video 2016: 2016 IARC Official Results 2016: 2016 IARC Performance Collection – ZMART 2017: ZMART 2017 Trailer – We need you
December 17th, 2020 Our recent work “EGO-Swarm” is reported by Science. EGO-Swarm is a decentralized and asynchronous systematic solution for multi-robot autonomous navigation in unknown obstacle-rich scenes using merely onboard resources. Author: Xin Zhou, Jiangchao Zhu, Hongyu Zhou, Chao Xu, and Fei Gao from the ZJU Fast Lab. Related Paper: EGO-Swarm: A Fully Autonomous and Decentralized Quadrotor Swarm System in Cluttered Environments (Submitted to ICRA2021). [Preprint] Video Links: YouTube, bilibili (for Mainland China)
The International Aerial Robotics Competition (IARC) Official Website : http://www.aerialroboticscompetition.org/stories/stories7.php (2016 IARC Technology Readiness Level). Quota from the last sentence: The team demonstrating this level of performance in 2016 was Zhejiang University. Other rankings and scores don’t matter. Zhejiang University has currently set the performance level for all IARC teams to beat in 2017. Zhejiang University News（浙江大学新闻办）: http://www.news.zju.edu.cn/news.php?id=43711 Zhejiang Channel of the Xinhuanet（新华网浙江频道）: http://www.zj.xinhuanet.com/zjEdu/20161010/3476997_c.html Zhejiang Online（浙江在线－浙江日报）: http://edu.zjol.com.cn/system/2016/10/10/021325441.shtml