无人系统与自主计算实验室

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/