A Novel Learning-Based Trajectory Generation Strategy for a Quadrotor

Author:

Hua Hean1ORCID,Fang Yongchun2ORCID

Affiliation:

1. College of Electrical and Information Engineering, and the National Engineering Research Center of Robot Visual Perception and Control Technology, Hunan University, Changsha, China

2. Institute of Robotics and Automatic Information System, College of Artificial Intelligence, and the Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin, China

Funder

National Natural Science Foundation of China

Haihe Lab of ITAI

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Artificial Intelligence,Computer Networks and Communications,Computer Science Applications,Software

Cited by 5 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. An improved switched prescribed finite time attitude control for quadrotors;International Journal of Robust and Nonlinear Control;2024-07-18

2. High-Speed Trajectory Tracking Control for Quadrotors via Deep Reinforcement Learning;2024 39th Youth Academic Annual Conference of Chinese Association of Automation (YAC);2024-06-07

3. Collision Avoidance and Navigation for a Quadrotor Swarm Using End-to-end Deep Reinforcement Learning;2024 IEEE International Conference on Robotics and Automation (ICRA);2024-05-13

4. Pontryagin’s Minimum Principle-Guided RL for Minimum-Time Exploration of Spatiotemporal Fields;IEEE Transactions on Neural Networks and Learning Systems;2024

5. A Novel Guided Deep Reinforcement Learning Tracking Control Strategy for Multirotors;IEEE Transactions on Automation Science and Engineering;2024

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