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About

Manuele Rusci is a leading researcher in the field of autonomous nano-drones, focusing on enabling intelligent, swarm-based operations within extreme hardware constraints. His work centers on three key areas: ultra-wideband (UWB) relative localization for scalable swarms, multi-sensory anti-collision systems, and on-device self-supervised learning for visual perception. Rusci’s major contributions include the development of the "Land & Localize" framework, which provides infrastructure-free, scalable swarm localization for 10 cm-scale nano-drones, and a multi-sensory anti-collision design that combines laser ranging with vision-based detection for robust exploration. His most cited work (11 citations) addresses a critical bottleneck in robotic swarms, while his latest research (2024) pioneers on-device self-supervised learning, allowing sub-50g nano-drones to adapt perception models to unknown environments without retraining—a breakthrough for real-world deployment. With a total of 21 citations across his top papers, Rusci’s innovations are shaping the future of autonomous micro-robotics, pushing the boundaries of what is possible on sub-100mW processors. His work is essential reading for anyone interested in edge AI, swarm robotics, or resource-constrained autonomous systems.