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******************************************************************************************************************* Special issue in Frontiers in Robotics and AI on "Multi-Swarms of Unmanned Autonomous Systems" https://www.frontiersin.org/research-topics/12547/multi-swarms-of-unmanned-autonomous-systems ******************************************************************************************************************* Scope: ====== Swarms of Unmanned Autonomous Systems (UAS), usually inspired by nature, such as bird flocks or fish schools, have been introduced to achieve complex common objectives through collaborative behaviors. Swarms have already been intensively studied as a way to address the limitations of single autonomous systems by augmenting the range of action, increasing the resilience and flexibility of UAS systems. These properties make them very suitable for numerous applications like surveillance, search and rescue or wide-area monitoring. Going one step further, the usage of multiple swarms of different types of autonomous vehicles, e.g. Unmanned Aerial Vehicles (UAV) or Unmanned Ground Vehicles (UGV) recently gained attention. Multi-swarm systems can be composed of heterogeneous vehicles moving in an autonomous and coordinated way, for instance in the air, on the ground, or in the sea. While members of a single swarm work collectively to achieve a mission, interactions between swarms can similarly be cooperative but also competitive (e.g., swarms vs. swarms), opening new research challenges. Multi-swarm systems remain an open research topic because of the intrinsic difficulty in obtaining efficient global behavior while relying on local decisions from distributed and heterogeneous entities evolving in different swarms. Such highly dynamical networked systems not only require efficient mobility behaviors, but also optimized ad hoc communications within and between swarms. The development of UAS swarming solutions is still mainly limited to manual design and optimization, which becomes increasingly tedious and hardly scalable when considering multi-swarm systems. Therefore, novel artificial intelligence (AI) and optimization approaches are thus required to allow the development of efficient multi-swarm behaviors. Topics: ======= • Multi-swarm mobility models • Multi-swarm simulations • Multi-swarm testbeds • Multi-swarm networking models and optimization (e.g., multi-layer networks) • Heuristics - meta-heuristics for multi-swarm optimization • Machine learning for multi-swarm systems • Models for UTM (UAV Traffic Management) • Prey-Predator dynamics in multi-swarms • Multi-swarm collaborative models • Game theoretical models • Multi-swarm applications: surveillance, defense, intruder detection and handling • State-of-the-art analysis (on the topics above) Manuscript deadline: ================= October 14, 2020 Topic Editors: ============ Grégoire Danoy, University of Luxembourg, Luxembourg Bernabé Dorronsoro, University of Cádiz, Spain Matthias R. Brust, University of Luxembourg, Luxembourg Jamal Toutouh, MIT, USA
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