Mission Planning, Perception and Control for Drones in Wide-Area Operations

Dear Colleagues,

Drones are playing an increasingly important role in both military and civilian fields, especially in wide-area operations. The closed loop, consisting of mission planning, perception, and control, is the key technology to achieve high-level intelligent autonomous wide-area operating drones. Establishing a sophisticated architecture, coordinating the components’ relationships, and achieving information interconnection are essential for reliability and performance of this loop, which brings tremendous emphasis on swarming intelligent, formation planning, control safety, etc.

This Special Issue, entitled Mission Planning, Perception, and Control for Drones in Wide-area Operations, aims to provide a scientific platform regarding the new trends in technologies for drones in wide-area operations, including mission planning technology, artificial intelligence, autonomous intelligent unmanned systems, and the combination of learning and control. We would like to collect innovative research including but not limited to mission planning, reliable perception, decision making, machine learning, and control.

Review and Original Research articles are welcome. Topics of interests include, but are not limited to, the following:

1) Motion control and path planning for wide-area operating drones;

2) Mission planning for wide-area operating drones;

3) Task decomposition and allocation for wide-area operating drones;

4) Simultaneous localization and map** in a complex environment;

5) Vision recognition and detection;

6) Intelligent perception techniques;

7) Model-based predictive control for intelligent systems;

8) Robust control, sliding mode control, and adaptive control;

9) Deep learning and reinforcement learning;

10) Architecture for learning and control integration;

11) Advanced decision and control methods for unmanned systems;

12) Intelligent swarm and formation.

We look forward to receiving your contributions.

Dr. Weiran Yao
Dr. **angyu Shao
Dr. Yuehua Liu
Prof. Dr. Liming **n
Dr. Jiatao Ding
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at mdpi.longhoe.net 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.

• motion control and path planning for wide-area operating drones
• mission planning for wide-area operating drones
• task decomposition and allocation for wide-area operating drones
• simultaneous localization and map** in a complex environment
• vision recognition and detection
• intelligent perception techniques
• model-based predictive control for intelligent systems
• robust control, sliding mode control, and adaptive control
• deep learning and reinforcement learning
• architecture for learning and control integration
• advanced decision and control methods for unmanned systems
• intelligent swarm and formation