Deep Learning for Drones and Its Applications

Dear Colleagues,

We are pleased to invite you to submit your papers to the MDPI Drones Special Issue on Deep Learning for Drones and Its Applications.

Drones, especially vertical takeoff and landing platforms (VTOL), are extremely popular and useful for many tasks. The variety of commercially-available VTOL platforms today indicates that they have been leaving the research lab and are being utilized for real-world aerial work, such as vertical structure inspection, construction site survey, and precision agriculture. These platforms offer high-level autonomous functionalities, minimizing user interventions, and can carry the useful payloads required for an application.

In addition, we have witnessed rapid growth in the area of machine learning, especially deep learning. This has demonstrated that state-of-the-art deep learning techniques can already outperform human capabilities in many sophisticated tasks, such as autonomous driving, playing games such GO or Dota2 (reinforcement learning), and even in medical image analysis (object detection and instance segmentation).

Based on the two cutting-edge technologies mentioned above, there exists a growing interest in utilizing deep-learning techniques for aerial robots, in order to improve their capabilities and level of autonomy. This step change will play a pivotal role in both drone technologies and the field of aerial robotics.

Within this context, we thus invite papers focusing on current advances in the area of deep learning for field aerial robots in this Special Issue.

Papers are solicited on all areas directly related to these topics, including but not limited to the following:

• Large-scale aerial datasets and standardized benchmarks for the training, testing, and evaluation of deep-learning solutions
• Deep neural networks (DNN) for field aerial robot perception (e.g., object detection, or semantic classification for navigation)
• Recurrent networks for state estimation and dynamic identification of aerial vehicles
• Deep-reinforcement learning for aerial robots (discrete-, or continuous-control) in dynamic environments
• Learning-based aerial manipulation in cluttered environments
• Decision making or task planning using machine learning for field aerial robots
• Data analytics and real-time decision making with aerial robots-in-the-loop
• Aerial robots in agriculture using deep learning
• Aerial robots in inspection using deep learning
• Imitation learning for aerial robots (e.g., teach and repeat)
• Multi aerial-agent coordination using deep learning

Dr. Inkyu Sa
Dr. Alexander Millane
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Drones is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.