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Hello! I am Boyu Zhou, currently an assistant professor at Sun Yat-sen University, School of Artificial Intelligence. I lead the STAR Group (SmarT Aerial Robotics). Before that I obtained my Ph.D. degree in the Aerial Robotics Group, Robotics Institute, Hong Kong University of Science and Technology in 2022. I got my B.Eng. degree from Shanghai Jiao Tong University in 2018.
My research focuses on mobile robot planning and perception that enable robust and agile autonomous operation in unknown complex environments. Detailed research information can be found in the following highlights and my publication lists.
Please contact me if you are interested in joining my research team.
Recently, we further develop a fully decentralized approach for exploration tasks using a fleet of quadrotors. The quadrotor team operates with asynchronous and limited communication, and does not require any central control. The coverage paths and workload allocations of the team are optimized and balanced in order to fully realize the system's potential. The associated paper has been published at IEEE T-RO. [Paper] [Video] [Code]
Our recent work toward fully automated and highly efficient aerial reconstruction, published at ICRA 2023, by Chen Feng: [Paper] [Code] [Video]
Autonomous exploration is a fundamental problem for various applications of unmanned aerial vehicles(UAVs). Recently I propose FUEL, a hierarchical framework that can support Fast UAV ExpLoration in complex unknown environments. Our method is demonstrated to complete challenging exploration tasks 3-8 times faster than state-of-the-art approaches. Our work was shown on IEEE Spectrum Video Friday. It has been accepted by RA-L. [Paper] [Video] [Code]
Videos of papers Robust and Efficient Quadrotor Trajectory Generation for Fast Autonomous Flight and Robust Real-time UAV Replanning Using Guided Gradient-based Optimization and Topological Paths were featured on IEEE Spectrum Video Friday! Thanks for Contributor Fan Shi, and Editor Evan and Erico! [Link2] [Link2] [Link3] (Search for HKUST in the pages).
I present RAPTOR, a Robust And Perception-aware TrajectOry Replanning framework to enable fast and safe flight in complex unknown environments. Its main features are: (a) finding feasible and high-quality trajectories in very limited computation time, and (b) introducing a perception-aware strategy to actively observe and avoid unknown obstacles. The associated paper is accepted by T-RO. [Paper] [Video]
Paper Robust Real-time UAV Replanning Using Guided Gradient-based Optimization and Topological Paths has been accepted by ICRA 2020. It presents a UAV replanning method that can support aggressive autonomous flight in unknown cluttered environments. It features searching for multiple trajectories in distinctive topological classes, exploring the solution space more thoroughly and yielding better solutions. [Paper] [Video] [Code] [IEEE Spectrum]
Paper Robust and Efficient Quadrotor Trajectory Generation for Fast Autonomous Flight has been accepted by RA-L. It presents kinodynamic path searching and B-spline-based optimization to generate high-quality trajectories within a few milliseconds. Fully autonomous flights and aggressive human chasing are demonstrated in our video. [Paper] [Video] [Code] [IEEE Spectrum]