High speed autonomous navigation with micro aerial vehicles (MAVs) operating in unknown dynamic environments requires the vehicles to be capable of quick replans to avoid potential obstacles. The high operating speed, the short sensing range and the unknown dynamic environments all leave strictly limited reacion time to make motion plans of high qualities. Developing reasonable navigation frameworks and fast motion planning algorithms is an essential part of it, especially for those lightweight multi-rotors with limited onboard sensing and computation capabilities.
The projects under this topic focus on developing effective yet computation-friendly motion planning methods to enable safe and high speed navigation of MAVs, considering constraints and sensing and control uncertainties.
- TGK-Planner: An Efficient Topology Guided Kinodynamic Planner for Autonomous Quadrotors, Hongkai Ye, Xin Zhou, Chao Xu, Jian Chu, Fei Gao, IEEE Robotics and Automation Letter (RA-L). [paper] [code]
- EGO-Planner: An ESDF-free Gradient-based Local Planner for Quadrotors, Xin Zhou, Zhepei Wang, Chao Xu, Fei Gao, IEEE Robotics and Automation Letter (RA-L with ICRA 2021 option). [paper] [code]
- 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, submitted to the International Conference on Robotics and Automation (ICRA 2021). [preprint]
- EVA-Planner: Environmental Adaptive Quadrotor Planning, Lun Quan, Zhiwei Zhang, Chao Xu, Fei Gao, submitted to the International Conference on Robotics and Automation (ICRA 2021). [preprint] [code]