Swarm Perception and Control of UAVs
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".
Deadline for manuscript submissions: closed (31 December 2020) | Viewed by 5769
Special Issue Editors
Interests: UAVs swarm; robotic vision; machine learning; MBSE
Interests: robotic vision; path planning of UAVs; pattern recognition; machine learning; face recognition; wavelets
Special Issues, Collections and Topics in MDPI journals
Interests: Swarm robotics; applied control; computer vision; wireless sensor network; cognitive radio network; hetereomorphism robotics and IoT/IoRT
Interests: unmanned systems; information fusion; distributed control; navigation
Special Issues, Collections and Topics in MDPI journals
Interests: UAV swarm; perception-aware control; sensor fusion; robotics vision
Special Issue Information
Dear Colleagues,
Aerial robotics has become an area of intense research within the robotics and control community, and autonomous aerial robots can capitalize on the three-dimensional (3D) airspace with aplomb.
In recent years, swarms of such aerial robots or autonomous unmanned aerial vehicles (UAVs) are emerging as a disruptive technology to enable highly reconfigurable, on-demand, distributed intelligent autonomous systems with high impact in many areas of science, technology, and society. Applications of UAV swarm systems span a broad spectrum of areas, including human-unreachable environments and challenging domains. Various specific tasks are addressed, such as foraging and coverage of a given area, UAV swarm observation, object pushing and transportation, exploration, and flocking. In any application, autonomous aerial swarms are expected to be more capable than a single large vehicle, offering significantly enhanced flexibility (adaptability, scalability, and maintainability) and robustness (reliability, survivability, and fault tolerance).
Swarming aerial robots must operate autonomously in a complex 3D world including urban canyons and an airspace that is getting increasingly crowded with drones and commercial airplanes. A cooperative UAV swarm can share information or tasks to accomplish a common, though perhaps not singular, objective. In order to achieve the goals of the swarming aerial robots, swarm task planning and decision-making, swarm game and cooperation, swarm evolution and configuration control, as well as swarm perception and reasoning have received increasing attention.
On the other hand, the success of aerial swarms flying in a 3D world is predicated on the distributed and synergistic capabilities of controlling individual and collective motions of aerial robots with limited resources for on-board computation, power, communication, sensing, and actuation. Achieving large-scale group autonomy in complex environments requires computationally efficient and scalable algorithms. UAVs obtain spatial information from their onboard sensor system and vision system, and the onboard perception algorithm must be run on an appropriately small time scale to enable the UAV to avoid collisions with dynamic, unexpected obstacles. Therefore, how to deal with airborne perceptual information and achieve end-to-end “perception control” is also a very important issue.
To date, the efforts in the study of airborne visual perception and collaborative controller design of UAV swarm systems have been continuously increasing, but many problems remains to be explored, discovered, and solved. The primary purpose of this Special Issue is to explore and display the latest achievements of UAV swarm theory and technology in modeling, control, planning, sensing, design, and implementation. The areas of interests include, but are not limited to:
- Overview of UAVs swarm control;
- Swarm control theories and technologies;
- Swarm modeling of multiple-vehicle cooperation;
- Swarm game, cooperation, and optimization;
- Swarm cooperative path planning and re-planning;
- Swarm dynamics network synchronization;
- Swarm evolution and configuration control;
- Swarm cooperative formation control;
- Bio-inspired cooperative swarm behavior simulation;
- Swarm distributed consensus;
- Fault-tolerance and robustness in UAVs swarm systems;
- Artificial intelligence in swarm cooperative control;
- Heterogeneous teams (combining different type of vehicles or manned/unmanned systems, end-effectors, and sensors);
- Deep learning for resource-constrained embedded vision sensor applications;
- End-to-end UAV airborne perception and control algorithm;
- Defect prediction and location based on swarm intelligence.
Dr. Jin Xiao
Dr. Baochang Zhang
Dr. Xiaohai Li
Dr. Jinwen Hu
Dr. Yang Lyu
Guest Editors
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