Robot Swarm
Robot swarms are systems of multiple robots coordinating to achieve a common goal without central control, focusing on robustness, scalability, and efficiency. Current research emphasizes decentralized control algorithms, often inspired by natural swarms, including those based on social forces, random walks (e.g., Lévy walks), and graph neural networks, as well as novel approaches leveraging blockchain for secure communication and hierarchical structures for improved scalability. This field is significant for its potential applications in diverse areas such as search and rescue, environmental monitoring, and manufacturing, driving advancements in distributed control, collective intelligence, and multi-agent systems.
Papers
Collective Decision-Making on Task Allocation Feasibility
Samratul Fuady, Danesh Tarapore, Shoaib Ehsan, Mohammad D. Soorati
Highly Efficient Observation Process based on FFT Filtering for Robot Swarm Collaborative Navigation in Unknown Environments
Chenxi Li, Weining Lu, Zhihao Ma, Litong Meng, Bin Liang
Leveraging swarm capabilities to assist other systems
Miquel Kegeleirs, David Garzón Ramos, Guillermo Legarda Herranz, Ilyes Gharbi, Jeanne Szpirer, Ken Hasselmann, Lorenzo Garattoni, Gianpiero Francesca, Mauro Birattari
Certified Policy Verification and Synthesis for MDPs under Distributional Reach-avoidance Properties
S. Akshay, Krishnendu Chatterjee, Tobias Meggendorfer, Đorđe Žikelić