@inproceedings{52b47c1c6586473e98943f6d2461c60b,
title = "SSRL: A Safe and Smooth Reinforcement Learning Approach for Collision Avoidance in Navigation",
abstract = "This paper addresses the collision avoidance problem of autonomous robots using reinforcement learning (RL) techniques, with a focus on ensuring both safety and smoothness. To achieve stability guarantees, a data-based Lyapunov function is incorporated into the model-free RL framework for training policies. Furthermore, constraints on action increments and action distributions are introduced, which effectively mitigate the differences between adjacent actions and ensuring smoothness in the learned policies. Subsequently, a safe and smooth reinforcement learning algorithm is proposed for training navigation policies, and its superiority in terms of safety and smoothness are validated by using a ground mobile robot in a simulated environment.",
keywords = "Collision Avoidance, Reinforcement Learning, Safety, Smoothness",
author = "Ruixian Zhang and Jianan Yang and Ye Liang and Shengao Lu and Lixian Zhang",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2nd Conference on Fully Actuated System Theory and Applications, CFASTA 2023 ; Conference date: 14-07-2023 Through 16-07-2023",
year = "2023",
doi = "10.1109/CFASTA57821.2023.10243245",
language = "英语",
series = "Proceedings of the 2nd Conference on Fully Actuated System Theory and Applications, CFASTA 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "681--686",
booktitle = "Proceedings of the 2nd Conference on Fully Actuated System Theory and Applications, CFASTA 2023",
address = "美国",
}