Mario Coppola
Papers
6
Total Citations
221
H-Index
5
About
Mario Coppola is a leading researcher in swarm robotics and multi-agent systems, with a focus on enabling swarms of micro air vehicles (MAVs) to operate in real-world environments. His work addresses fundamental challenges in decentralized coordination, particularly for robots with severely limited sensing, communication, and computational capabilities. Coppola’s major contributions include a provably correct approach to self-organizing pattern formation using anonymous, memoryless, and non-communicating robots—a breakthrough that demonstrates how global behaviors can emerge from minimal local rules. He has also pioneered the use of PageRank centrality to optimize swarm performance through local analysis alone, and developed MAMBPO, a sample-efficient multi-robot reinforcement learning algorithm that leverages learned world models. His highly cited survey on swarming with MAVs (124 citations) provides a comprehensive framework linking local robot capabilities to global mission constraints. Additionally, his work on onboard ranging-based relative localization enables lightweight aerial swarms to achieve real-time positioning and stability without external infrastructure. With over 220 total citations, Coppola’s research is shaping the future of autonomous, scalable, and resource-constrained robotic swarms.
Research Focus
Key Achievements
Top Papers
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