@inproceedings{1ae25223868f411f8a58bc42355f7e5b,
title = "PPO-NODE: End-to-End Vision-Based Obstacle Avoidance for UAVs in Unknown Complex Environments",
abstract = "To address the challenge of autonomous obstacle avoidance for unmanned aerial vehicles (UAVs) in complex environments, we propose PPO-NODE, an end-to-end deep reinforcement learning method that integrates Proximal Policy Optimization (PPO) with Neural Ordinary Differential Equations (NODE). To predict the motion of dynamic obstacles, NODE is incorporated into the feature extraction process to capture continuous temporal features from sequences of depth images. Experimental evaluations conducted in a PyBullet-based 3D simulation environment demonstrate that the proposed PPO-NODE algorithm achieves a collision rate of 11.3 \% in complex environments, significantly reducing the collision probability for UAVs. This effectively resolves the critical issues of prior map dependency and delayed response to dynamic obstacles inherent in traditional approaches.",
keywords = "collision avoidance, neural ordinary differential equations, reinforcement learning",
author = "Chaolin Fan and Kaixin Xu and Jianan Yang",
note = "Publisher Copyright: {\textcopyright} 2026 IEEE.; 5th Conference on Fully Actuated System Theory and Applications, FASTA 2026 ; Conference date: 22-05-2026 Through 24-05-2026",
year = "2026",
doi = "10.1109/FASTA70174.2026.11549440",
language = "英语",
series = "Proceedings of the 5th Conference on Fully Actuated System Theory and Applications, FASTA 2026",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "555--560",
booktitle = "Proceedings of the 5th Conference on Fully Actuated System Theory and Applications, FASTA 2026",
address = "美国",
}