Code for AM-Traj is now available on GitHub

AM-Traj is a C++11 header-only library for generating large-scale piecewise polynomial trajectories for aggressive autonomous flights, with highlights on its superior computational efficiency and simultaneous spatial-temporal optimality. Besides, an extremely fast feasibility checker is designed for various kinds of constraints. All components in this framework leverage the algebraic convenience of the polynomial trajectory optimization problem, thus our method is capable of computing a spatial-temporal optimal trajectory with 60 pieces within 5ms, i.e., 150Hz at least. You just need to include “am_traj.hpp” and “root_finder.hpp” in your code. Please use the up-to-date master branch which may have a better performance than the one in our paper.

Author: Zhepei Wang and Fei Gao from the ZJU Fast Lab.

Related Papers:

Alternating Minimization Based Trajectory Generation for Quadrotor Aggressive Flight, Zhepei Wang, Xin Zhou, Chao Xu, Jian Chu, and Fei Gao, submitted to RA-L/IROS 2020.

Detailed Proofs of Alternating Minimization Based Trajectory Generation for Quadrotor Aggressive Flight, Zhepei Wang, Xin Zhou, Chao Xu, and Fei Gao, the supplementary material.

Video Linksyoutube or bilibili


Postdoc Opportunities at the FAST Lab, Zhejiang University

ZJU was founded in 1897, which is one of the oldest and most prestigious institutions of higher education in China. It is considered a top university in the Chinese mainland, which is ranked #6 in Asia and #54 worldwide according to the QS University Rankings for 2020.

The FAST Lab is the recipients of the Champion of the International Aerial Robotics Competition (2018), the First Prize of the DJI RoboMaster AI Global Challenges (2019), as well as the Champion of the World Robotic Sailing Competition (2019). The FACT Lab has a close collaborative relationship with industrial companies such as the Supcon and the DJI, etc. For more information about the FAST Lab, please visit

The FAST Lab is calling for applications for several postdoc positions in the areas of unmanned systems (e.g., mechatronic control, autonomous navigation & planning) and industrial intelligence (e.g., industrial vision systems, machine learning and control systems).

Successful applicants should hold a Ph.D. in the areas of engineering or science disciplines, such as control science & engineering, robotics, applied math, computing, electrical engineering, electronics, mechanical & aerospace engineering, but not limited to these.

Application packages should send a CV and sample publications to entitled by “FAST Postdoc Application”. Positions remain open until filled. If you have any questions regarding the postdoc positions, please do not hesitate to contact (Chao Xu) and (Fei Gao).

The Chinese version is available on this page.




        无人系统与自主计算实验室(Field Autonomous Systems & compuTing)主要方向:1)智能无人系统;2)工业智能技术。现承担国家重点研发计划项目(科技部)、工业互联网创新发展工程项目(工信部)、基金项目(国家自然科学基金委)、国家电网项目、大疆(DJI)联合研发项目等;实验室与产业界合作密切、与国外同类顶尖实验室保持紧密合作关系;曾获国际空中机器人大赛冠军(2014年 – 2018年第七代任务)、DJI机甲大师全球人工智能挑战赛一等奖(2019年)、世界机器人帆船大赛总冠军(2019年)等荣誉。更多信息请访问。目前招聘博士后若干名,研究方向为

  • 无人系统实时导航与控制(运动控制、视觉导航、轨迹规划等)
  • 工业智能系统与信息处理(工业视觉、机器学习、控制系统等)



  • 欢迎控制、应用数学、计算机、电子信息、电气、机械、航空航天等(但不限于以上)跨学科优秀博士毕业生联系申请。
  • 具有良好师德师风,有较好的学术发展潜力和合作精神。
  • 申请者一般应为毕业3年内的优秀博士毕业生,身体健康,年龄原则上不超过35周岁。



  • 博士后年薪一般为15 – 30万,学院提供一定的科研启动经费。
  • 博士后在站时间由学院、合作导师和博士后本人根据研究项目和内容需要在2 – 6年内灵活确定,在站期间可申请租住学校教师公寓,人事关系进入学校后从事博士后研究工作3年及以上的博士后,可按学校相关规定申报学校高级专业技术职务。
  • 学校和学院鼓励博士后出站后积极应聘校内外专业技术岗位,并将博士后作为学校教学、科研、成果转化等岗位选聘的重要来源。
  • 鼓励和支持博士后研究人员申报博士后国际交流计划、博士后科学基金以及其他国家与地方的科技项目和博士后资助项目。


材料(含简历、代表作)请寄,并注明“FAST-Lab Postdoc Application”,招满为止;咨询请联系许超)、高飞)