Catch Planner: Catching High-Speed Targets in the Flight-Reference-Cited by-同舟云学术

Catch Planner: Catching High-Speed Targets in the Flight

Published:2023-08 Issue:4 Volume:28 Page:2387-2398
ISSN:1083-4435
Container-title:IEEE/ASME Transactions on Mechatronics
language:
Short-container-title:IEEE/ASME Trans. Mechatron.

Author:

Yu Huan¹^ORCID,Wang Pengqin²^ORCID,Wang Jin¹^ORCID,Ji Jialin³,Zheng Zhi¹^ORCID,Tu Jie¹,Lu Guodong¹,Meng Jun⁴,Zhu Meixin⁵,Shen Shaojie²^ORCID,Gao Fei³^ORCID

Affiliation:

1. State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou, China

2. Hong Kong University of Science and Technology (HKUST), Hong Kong, China

3. State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou, China

4. School of Electrical Engineering, Zhejiang University, Hangzhou, China

5. Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China

Funder

Pioneer

Leading Goose

R&D Program of Zhejiang

National Natural Science Foundation of China

Robotics Institute of Zhejiang University

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Control and Systems Engineering

Link

http://xplorestaging.ieee.org/ielx7/3516/10219030/10175023.pdf?arnumber=10175023

Reference37 articles.

1. Learning Minimum-Time Flight in Cluttered Environments

2. Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor;haarnoja;Proc Int Conf Mach Learn,0

3. Learning High-Level Policies for Model Predictive Control

4. Control of Quadrotors Using the Hopf Fibration on SO(3)

5. Grandmaster level in StarCraft II using multi-agent reinforcement learning

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