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About

Carlo Sinigaglia is a leading researcher in the field of swarm robotics, specializing in the optimal control of large-scale robotic swarms through mean-field models and PDE-constrained optimization. His major contributions lie in developing computationally efficient, robust density control strategies that enable passive robot particles to be guided by external actuation toward target equilibrium densities. Sinigaglia’s work addresses critical challenges in swarm robotics, including the indirect control of advection-diffusion environmental fields using distributed underwater vehicles. His most-cited paper, “Density Control of Large-Scale Particles Swarm Through PDE-Constrained Optimization” (2022), with 21 citations, introduces an optimal control strategy for shaping swarm densities via boundary global actuation. He has also advanced the field with robust optimal control frameworks that prove stability and efficiency in driving swarms to desired configurations. With a total of over 30 citations across his key publications, Sinigaglia’s research bridges theoretical control theory and practical swarm applications, offering scalable solutions for environmental monitoring, exploration, and collective robotics. His work is foundational for students and researchers seeking to understand the intersection of PDE-constrained optimization, stochastic processes, and multi-agent systems.