Paul Buzaud
Papers
2
Total Citations
19
H-Index
2
About
Paul Buzaud is a researcher whose work bridges the critical intersection of autonomous systems and sensor integration. His primary research areas include decentralized multi-agent coordination, combinatorial optimization, and sensor calibration for robotic perception. Buzaud’s most notable contribution is his work on the decentralized weapon–target assignment problem, where he addresses the challenge of coordinating multiple agents under asynchronous communication constraints. This work, published in 2022 and garnering 13 citations, proposes a novel framework that moves beyond traditional centralized planners, enabling robust task allocation in distributed, real-world environments. Additionally, Buzaud has made significant strides in sensor fusion, developing an extrinsic calibration method for camera and motion capture systems (2021, 6 citations). This technique uses fiducial markers and motion capture to precisely locate camera coordinate frames, a fundamental capability for research in robotics and computer vision. Through these contributions, Buzaud is advancing the practical deployment of autonomous systems, ensuring they can operate effectively in complex, decentralized, and perceptually rich environments.
Research Focus
Key Achievements
Top Papers
- 1Decentralized Weapon–Target Assignment Under Asynchronous Communications13 citations · 2022
- 2Extrinsic Calibration of Camera and Motion Capture Systems6 citations · 2021