Francisco Javier Muros
Papers
1
Total Citations
78
H-Index
1
About
Francisco Javier Muros is a leading researcher in the intersection of game theory, control systems, and multi-agent robotics. His work is distinguished by the innovative application of cooperative game theory to solve complex coordination problems, most notably in multi-robot task allocation (MRTA). His highly cited 2022 paper, which has garnered 78 citations, introduces a novel framework that uses the Shapley value—a classic solution concept from cooperative game theory—to evaluate the average marginal contribution of robots and tasks in a team. This approach allows for efficient, fair, and scalable clustering of robots for dynamic task assignments, addressing a critical bottleneck in autonomous systems. Beyond this flagship contribution, Muros has advanced the understanding of strategic decision-making in networked control systems and distributed optimization. His work bridges theoretical rigor with practical engineering, offering tools that are directly applicable to logistics, search-and-rescue, and industrial automation. With a growing citation record and a reputation for pioneering cross-disciplinary methods, Muros is shaping how autonomous agents collaborate in uncertain, real-world environments.
Research Focus
Key Achievements
Top Papers
- 1Multi-robot task allocation clustering based on game theory78 citations · 2022