DOAJ Open Access 2023

Vision-Based Deep Reinforcement Learning of UAV-UGV Collaborative Landing Policy Using Automatic Curriculum

Chang Wang Jiaqing Wang Changyun Wei Yi Zhu Dong Yin +1 lainnya

Abstrak

Collaborative autonomous landing of a quadrotor Unmanned Aerial Vehicle (UAV) on a moving Unmanned Ground Vehicle (UGV) presents challenges due to the need for accurate real-time tracking of the UGV and the adjustment for the landing policy. To address this challenge, we propose a progressive learning framework for generating an optimal landing policy based on vision without the need of communication between the UAV and the UGV. First, we propose the Landing Vision System (LVS) to offer rapid localization and pose estimation of the UGV. Then, we design an Automatic Curriculum Learning (ACL) approach to learn the landing tasks under different conditions of UGV motions and wind interference. Specifically, we introduce a neural network-based difficulty discriminator to schedule the landing tasks according to their levels of difficulty. Our method achieves a higher landing success rate and accuracy compared with the state-of-the-art TD3 reinforcement learning algorithm.

Topik & Kata Kunci

Motor vehicles. Aeronautics. Astronautics

Penulis (6)

Chang Wang

Jiaqing Wang

Changyun Wei

Yi Zhu

Dong Yin

Jie Li

Format Sitasi

APA MLA BibTeX

Wang, C., Wang, J., Wei, C., Zhu, Y., Yin, D., Li, J. (2023). Vision-Based Deep Reinforcement Learning of UAV-UGV Collaborative Landing Policy Using Automatic Curriculum. https://doi.org/10.3390/drones7110676

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Informasi Jurnal

Tahun Terbit: 2023
Sumber Database: DOAJ
DOI: 10.3390/drones7110676
Akses: Open Access ✓