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 in Communication-Constrained Environments
Thomas G. Kelly, Mohammad Divband Soorati, Klaus-Peter Zauner, Sarvapali D. Ramchurn, Danesh Tarapore
Industry Led Use-Case Development for Human-Swarm Operations
Jediah R. Clark, Mohammad Naiseh, Joel Fischer, Marise Galvez Trigo, Katie Parnell, Mario Brito, Adrian Bodenmann, Sarvapali D. Ramchurn, Mohammad Divband Soorati