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About

J. M. Maestre is a versatile robotics and control systems researcher whose work spans smart home integration, multi-robot coordination, and model predictive control for environmental monitoring. Operating at the intersection of robotics, game theory, and control engineering, Maestre has made notable contributions to how autonomous systems collaborate and integrate within intelligent environments. Among his most influential contributions is a cooperative game theory framework for multi-robot task allocation, which leverages the Shapley value to fairly distribute tasks among robot teams — a paper that has garnered 78 citations since 2022. His early work on integrating service robots into smart homes via Universal Plug and Play (UPnP) standards established important foundations for robot interoperability, earning over 50 citations and shaping subsequent research into the Digital Home Compliant protocol. More recently, Maestre has pioneered the fusion of stochastic model predictive control with mobile robot path planning for applications in irrigation canal management and water quality monitoring, demonstrating a strong commitment to sustainability-driven robotics. His career reflects a consistent drive to bridge theoretical frameworks — from cooperative game theory to probabilistic control — with real-world autonomous systems, making his work highly relevant to researchers in robotics, smart infrastructure, and environmental engineering.