The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/a21231b6-f867-40df-962d-27f9dc25f57a

Yu, Hang; Christophe de Wagter; Guido De Croon (2024): Data underlying the publication: MAVRL: Learn to Fly in Cluttered Environments with Varying Speed. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/a21231b6-f867-40df-962d-27f9dc25f57a.v1

Dataset

• Artificial Intelligence and Image Processing
• Aerospace Engineering
• Engineering
• Information and Computing Sciences

aerial robotics, autonomous drone, Latent space, Obstacle avoidance, Reinforcement learning, unmanned aerial vehicle (UAV), Varying speed

CC BY 4.0

RefWorks, BibTeX, Reference Manager, Endnote, DataCite, NLM, DC, CFF

by Hang Yu , Christophe de Wagter , Guido De Croon

Datasets for paper of 'MAVRL: Learn to Fly in Cluttered Environments with Varying Speed'. This research is about how to fly in a cluttered environment based on reinforcement learning. There are 5 .zip files in this dataset: vae.zip is the training result of Variational AutoEncoder; lstm_dataset.zip is the collected data of many rollouts by an initial policy; RecurrentPPO_1.zip contains a initial policy and retrained policy; LSTM_110_9_0.zip is the training result of LSTM; AvoidBench.zip is the unity standalone file.

• 2024-02-20 first online, published, posted

4TU.ResearchData

MAVRL: Learn to Fly in Cluttered Environments with Varying Speed

TU Delft, Faculty of Aerospace Engineering, Micro Air Vehicles Lab (MAVLab)