A paper is conditionally accpeted by IEEE T-RO


F. Gao, L. Wang, B. Zhou, L. Han, J. Pan and S. Shen’s work on ‘Teach-Repeat-Replan: A Complete and Robust System for Aggressive Flight in Complex Environments’ is conditionally accepted by IEEE Transactions on Robotics (T-RO).

Introduction Teach-Repeat-Replan is a complete and robust system enables Autonomous Drone Race. It contains all components for UAV aggressive flight in complex environments. It is built upon the classical robotics teach-and-repeat framework, which is widely adopted in infrastructure inspection, aerial transportation, and search-and-rescue. Our system can capture users’ intention of a flight mission, convert an arbitrarily jerky teaching trajectory to a guaranteed smooth and safe repeating trajectory, and generate safe local re-plans to avoid unmapped or moving obstacles on the flight.

Code: https://github.com/USTfgaoaa/Teach-Repeat-Replan/


Jiaming Liang is going to give an oral presentation for HIFICOMA 2019


Jiaming Liang’s article entitled “Filtering enhanced tomographic PIV reconstruction based on deep neural networks” has been accepted for oral presentation by the committee of the International Symposium on High-Fidelity Computational Methods & Applications 2019, which will be held in Shanghai during December 14-16, 2019. The symposium is to enhance deployment and applications of the high-fidelity methods in complex industrial fluid flows. (For more information, please refer to https://www.ishfcma.org/ ).