Lun Li - IEEE Xplore Author Profile
Subscribe
ADVANCED SEARCH

Author details

< Back

Lun Li

Publications
4
Citations
2
Publications by Year
20232024
Co-Authors:
Show All Co-Authors (10)
Image of author Lun Li

Lun Li

Affiliation

Cyberspace Institute of Advanced Technology
Guangzhou University
Guangzhou, China

Publication Topics

Biography

Lun Li received the M.S. degree in electric engineering from Harbin Engineering University, Harbin, China, in 2020. He is currently working toward the Ph.D. degree in artificial intelligence with the Institute of Robotics and Automatic Information System, Nankai University, Tianjin, China. His research interests include reinforcement learning and motion planning for unmanned fixed-wing plane.(Based on document published on 16 February 2024).
Publications
4
Citations
2
Publications by Year
20232024
Co-Authors:
Show All Co-Authors (10)
Author's Published Works

Showing 1-4 of 4 results

  • Sort

Filter Results

Show

Sort By

Results

LRAE: Large-Region-Aware Safe and Fast Autonomous Exploration of Ground Robots for Uneven Terrains

Qingchen Bi;Xuebo Zhang;Shiyong Zhang;Runhua Wang;Lun Li;Jing Yuan

IEEE Robotics and Automation Letters
Year: 2024 | Volume: 9, Issue: 12 | Journal Article |
Ground robot autonomous exploration for uneven terrains is still challenging since the rugged terrain structures not only degrade the exploration performance but also threaten the navigation safety of the robot. In this letter, a novel exploration planner is proposed for safe and fast exploration in uneven terrains. To obtain high exploration efficiency, we propose a large-region-aware exploration...Show More

LRAE: Large-Region-Aware Safe and Fast Autonomous Exploration of Ground Robots for Uneven Terrains

Qingchen Bi;Xuebo Zhang;Shiyong Zhang;Runhua Wang;Lun Li;Jing Yuan

Year: 2024 | Volume: 9, Issue: 12 | Journal Article |

Autopilot Controller of Fixed-Wing Planes Based on Curriculum Reinforcement Learning Scheduled by Adaptive Learning Curve

Lun Li;Xuebo Zhang;Chenxu Qian;Runhua Wang;Minghui Zhao

IEEE Transactions on Emerging Topics in Computational Intelligence
Year: 2024 | Volume: 8, Issue: 3 | Journal Article |
In this paper, we present a novel curriculum reinforcement learning method that can automatically generate a high-performance autopilot controller for a 6-degree-of-freedom (6-DOF) aircraft with an unknown dynamic model, which is difficult to be handled using traditional control methods. In this method, a sigmoid-like learning curve is elegantly introduced to generate goals (the desired heading, a...Show More

Autopilot Controller of Fixed-Wing Planes Based on Curriculum Reinforcement Learning Scheduled by Adaptive Learning Curve

Lun Li;Xuebo Zhang;Chenxu Qian;Runhua Wang;Minghui Zhao

Year: 2024 | Volume: 8, Issue: 3 | Journal Article |

Reinforcement Learning Methods for Fixed-Wing Aircraft Control

Lun Li;Xuebo Zhang;Yang Wang;Chenxu Qian;Minghui Zhao

2023 9th International Conference on Systems and Informatics (ICSAI)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
In recent years, reinforcement learning (RL) has been widely researched for fixed-wing aircraft control, offering transformative potential for more adaptive, efficient, and autonomous flight operations. This paper presents a comprehensive analysis of the current state, advantages, challenges, and future prospects of employing RL in fixed-wing aircraft control. The advantages of RL include model-fr...Show More

Reinforcement Learning Methods for Fixed-Wing Aircraft Control

Lun Li;Xuebo Zhang;Yang Wang;Chenxu Qian;Minghui Zhao

Year: 2023 | Conference Paper |

FT-TF: A 4D Long-Term Flight Trajectory Prediction Method Based on Transformer

Shijin Luo;Minghui Zhao;Zhenjie Zhao;Lun Li;Shiyong Zhang;Xuebo Zhang

2023 42nd Chinese Control Conference (CCC)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
The accurate prediction of target aircraft trajectory in the process of air combat can significantly improve the ability of aircraft to gain air superiority. Most of the trajectory prediction methods currently applied in air combat are based on traditional time-series prediction algorithms such as LSTM with short prediction steps, which can not realize the demand for long-term prediction in air co...Show More

FT-TF: A 4D Long-Term Flight Trajectory Prediction Method Based on Transformer

Shijin Luo;Minghui Zhao;Zhenjie Zhao;Lun Li;Shiyong Zhang;Xuebo Zhang

Year: 2023 | Conference Paper |

IEEE Account

Purchase Details

Profile Information

Need Help?

A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.
© Copyright 2025 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.