Matthew N. Goodell
Papers
1
Total Citations
41
H-Index
1
About
Matthew N. Goodell’s research lies at the intersection of multi-agent systems, information theory, and autonomous sensing, with a primary focus on decentralized control for environmental monitoring and target localization. His most influential work, the 2020 paper on a decentralized multi-agent information-theoretic (DeMAIT) control algorithm, has garnered 41 citations and represents a significant advance in the field. This algorithm enables mobile sensor agents to collaboratively and efficiently locate targets—such as gas leaks—by integrating Bayesian estimation with information-theoretic motion planning. Goodell’s contribution is notable for addressing the challenge of coordinating multiple autonomous agents without centralized control, a critical requirement for real-world deployments in hazardous or inaccessible environments. By optimizing sensor trajectories to maximize information gain, his approach improves both the speed and accuracy of target estimation and localization. This work has implications for environmental safety, disaster response, and robotics, and it stands as a key reference for researchers developing decentralized, intelligent sensing systems. Goodell’s research continues to push the boundaries of autonomous multi-agent coordination, making him a notable figure in the field.
Research Focus
Key Achievements
Top Papers
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