AI-Assisted Control Strategies and Their Applications to the Stabilization, Guidance and Navigation of Drones

Dear Colleagues,

Drones, also known as unmanned aerial vehicles (UAVs), have seen a significant surge in interest over the past decade. This is due to their potential for a wide range of applications, from commercial delivery and aerial photography to more complex tasks such as disaster management, environmental monitoring and military operations. The design, dynamics and navigation of drones are critical aspects that determine their performance, efficiency and applicability in these diverse scenarios.

The design of drones involves a multitude of factors, including their structure, power systems and onboard sensors. Optimizing these elements can enhance the drone's efficiency, durability and functionality, enabling it to perform better and withstand various operational conditions. The dynamics of drones, which involve their movement patterns, stability and control systems, are also crucial. Understanding and improving these dynamics can lead to more precise and reliable drone operations.

Navigation is another key aspect of drone technology. It involves the guidance, control and coordination mechanisms that allow drones to move from one location to another, avoid obstacles and perform their tasks. Improving navigation systems can enhance the autonomy of drones, enabling them to operate in more complex and unpredictable environments.

In recent years, machine learning (ML) has emerged as a powerful tool for advancing drone technology. ML techniques can be used to optimize drone design, model their dynamics and improve their navigation systems. For instance, ML algorithms can learn from data to predict optimal design parameters, understand complex dynamics and navigate in unknown environments. They can also help improve drone operations, such as object detection, tracking and collision avoidance.

This Special Issue aims to explore these topics and highlight the latest advancements in the field. It provides a platform for researchers, engineers and practitioners to share their findings, discuss challenges and foster collaborations. The issue underscores the importance of this research area, given the growing role of drones in our society and the potential of ML to revolutionize drone technology.

Aim:

Furthermore, it aims to bring together innovative research and developments in the field of drone technology, seeking to highlight advancements in the design, dynamics and navigation of drones, with a particular focus on the integration of machine learning (ML) techniques. The goal is to foster discussions and collaborations among researchers, engineers and practitioners working on drone technology and its applications.

Scope:

This Special Issue will cover a broad range of topics related to drone technology. It will focus on the optimal design of drones, exploring how to enhance their efficiency, durability and functionality. The issue will delve into the dynamics of drones, examining their movement patterns, stability and control systems. It will also cover the navigation of drones, including their guidance, control and coordination mechanisms.

In line with recent trends, the Special Issue will emphasize the application of machine learning in drone technology. This includes ML-based design optimization, ML-driven dynamic modeling and ML-aided navigation systems. The issue welcomes studies on the use of ML techniques for improving drone operations, such as object detection, tracking and collision avoidance, as well as for enhancing their autonomous capabilities.

Suggested themes and article types for submissions:

1. Optimal Design of Drones: Articles focusing on the latest advancements in the design of drones, including structural design, power systems and onboard sensors.
2. Dynamics of Drones: Submissions that delve into the movement patterns, stability and control systems of drones.
3. Navigation Systems for Drones: Papers that explore the guidance, control and coordination mechanisms that allow drones to move from one location to another, avoid obstacles and perform their tasks.
4. Machine Learning in Drone Technology: Articles that highlight the use of ML techniques in drone design, dynamics and navigation. This could include ML-based design optimization, ML-driven dynamic modeling and ML-aided navigation systems.
5. Applications of Drones: Submissions that discuss the diverse applications of drones, from commercial delivery and aerial photography to disaster management, environmental monitoring and military operations.

Article Types for Submissions:

1. Research Articles: Original research papers that present new findings in the field of drone design, dynamics, navigation and the application of ML in these areas.
2. Review Articles: Comprehensive reviews that summarize the current state of research in the field, identify gaps, and suggest directions for future research.

Dr. Agostino De Marco
Guest Editor

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.

• drone technology
• optimal design
• drone dynamics
• drone navigation
• machine learning
• design optimization
• dynamic modeling
• navigation systems
• autonomous drones
• object detection
• collision avoidance
• tracking systems