Dear Colleagues,

Micro aerial vehicles (MAVs) are gaining significant attention in research groups and industries all over the world, due to the blooming number of military and civilian applications, including industrial inspection, search and rescue, commodity transportation, resource exploration, civilian surveillance, and precision agriculture. Over the past decade, multitudes of research contributions have been made to deploy MAVs in dynamic, confined, and unstructured environments. MAVs can be deployed in those remote or areas inaccessible by humans, such as disaster sites and forests. MAVs can significantly reduce the risks to human life, increase the system’s efficiency, and reduce the execution time as compared to conventional techniques. The profound capabilities of MAVs range from fixed wing surveillance MAVs to advanced MAVs with the capability of hovering and navigation along with carrying multiple sensors. MAVs are small enough to fit on palm of a hand, operate over several kilometers, and can transmit data to a portable base station. The agility and simplicity of MAVs put them in the forefront of robotic developments. Apart from these appealing benefits, challenges with respect to the embedded system (e.g., memory, processing power, energy, etc.) are usually present. MAVs operate with several inter-linked constraints, such as processing, flight time, battery endurance, weight, size, and communication bandwidth. The use of MAVs is also restricted by limited operational endurance and lack of flexible cooperation between multiple MAVs. Another limitation of MAVs arises from the onboard power supply for computation, sensing, or actuation, which can be insubstantial due to restrictions on energy with existing of-the-shelf energy resources. The selection of mechanical design also determines the permissible energy payload, which ultimately dictates the onboard computation, sensing, or actuation. In addition, MAVs control and perception has a strong relation. Therefore, researchers should take sensor and processor availability and design constraint along with optimized algorithms into account to ensure perception on MAVs. Similarly, the flight control and navigation of MAVs require careful modeling of vehicle’s dynamics and operation characteristics, e.g., sensor saturation and actuation. Thus, researchers have to design efficient algorithms for detection, tracking, navigation, trajectory planning, adaptive and robust feedback control strategies, swarm collaboration, mapping, and exploration.

This Special Issue focuses on the technological advancements of MAVs. It welcomes high-quality scientific contributions based on theory and experiments in the domains of novel designs for MAVs, innovative strategies for aerial manipulation, localization, control mechanisms for guidance and navigation of single and multi-MAVs, multi-MAVs collaboration, propulsion mechanisms of MAVs,  trajectory planning and tracking, stability characteristics, aerodynamic aspects, and charging methods. Our objective is to gather high-quality and original contributions which address the breakthrough novelties of MAVs.  Surveys and reviews are equally welcome. Topics of interest include, but are not limited to, the following subject areas:

• Integration of MAVs with other key enabling technologies;
• Dexterous aerial manipulation;
• Propulsion mechanisms of MAVs;
• Mapping, localization, and autonomous navigation;
• Trajectory planning and coverage strategies;
• Obstacles avoidance mechanisms;
• Object detection and tracking, 3D vision methods;
• Reinforcement learning to perform autonomous task;
• Flight test, control, guidance, and stability;
• Robust and adaptive control of MAVs;
• Machine learning for MAVs;
• Structural analysis of MAVs;
• Charging techniques for MAVs;
• Aerodynamic optimization;
• Sensitivity and performance analysis; 
• Applications, challenges, and security issues of MAVs.

Dr. Syed Agha Hassnain Mohsan
Dr. Mumtaz Karatas
Dr. Muhammad Asghar Khan
Guest Editors

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• micro aerial vehicles
• flight control
• trajectory planning
• aerial manipulation
• design optimization
• robust control
• adaptive control
• reinforcement learning
• machine learning for MAVs
• cooperative control of MAVs
• mapping and localization methods