Resilient Swarm

Resilient swarm research focuses on designing and controlling groups of robots or agents that can achieve complex tasks collaboratively, even with limitations in individual capabilities, communication, or environmental uncertainties. Current research emphasizes developing decentralized control algorithms, often inspired by biological swarms, utilizing models like deep generative models for trajectory planning, neural networks for emergent behavior control, and Kuramoto oscillators for geometry-informed learning. This field is significant for advancing robotics, particularly in applications requiring robust, scalable, and adaptable systems for tasks such as exploration, surveillance, and environmental monitoring, as well as providing insights into collective behavior in biological systems.