17/8/2019 – I passed my Ph.D. thesis defense entitled ‘Motion Planning for Autonomous Flights: Algorithms, Systems, and Applications‘.
29/6/2019 – We release the source code of the Teach-Repeat-Replan framework, which is a complete and robust autonomous drone system for aggressive flight in complex environments. This project supports fully Autonomous Drone Race.
If you feel this repo useful, please star it or cite our related publications:
- Teach-Repeat-Replan: A Complete and Robust System for Aggressive Flight in Complex Environments, Fei Gao, Luqi Wang, Boyu Zhou, Luxin Han, Jie Pan, shaojie Shen, IEEE Transactions on Robotics (T-RO), conditionally accepted.
- Optimal Trajectory Generation for Quadrotor Teach-and-Repeat, Fei Gao, Luqi Wang, Kaixuan Wang, William Wu, Boyu Zhou, Luxin Han, Shaojie Shen, IEEE Robotics and Automation Letters (RA-L), 2019.
This project contains local online mapping, global mapping, local online planning, global spatial-temporal planning, controller, visual-inertial localization, global pose graph optimization, human-robot interface, and a complete simulator.
Our system can be applied to situations where the user has a preferable rough route but isn’t able to pilot the drone ideally. For example, for drone racing or aerial filming, a beginner-level pilot is impossible to control the drone to finish the race safely or take an aerial video smoothly unless months of training. With our system, the human pilot can virtually control the drone with his/her navie operations, then our system automatically generates a very efficient repeating trajectory and autonomously execute it.
Our system can also be used for normal autonomous navigations, like our previous works in video1 and video2. For these applications, the drone can autonomously fly in complex environments using only onboard sensing and planning.
23/6/2019 – I have three papers accepted by IROS 2019 (two as the corresponding author), check the publication list for these fresh new works!