Ouahib Guenounou
Papers
2
Total Citations
9
H-Index
2
About
Ouahib Guenounou is a researcher advancing the frontier of multi-robot coordination, with a primary focus on task allocation and path planning in heterogeneous multi-robot systems (MRS). His work addresses the critical challenge of enabling robots with diverse sensing capabilities to efficiently inspect spatially distributed industrial sites. Guenounou’s key contribution lies in developing enhanced genetic algorithms that optimize both task assignment and travel routes, significantly reducing the overall distance covered by robot teams. His most cited paper (2025, 7 citations) proposes an improved genetic algorithm for this purpose, while a closely related study (2024, 2 citations) further refines the optimization framework. Though early in its citation trajectory, this work is gaining traction for its practical relevance to industrial automation and inspection logistics. Guenounou’s research bridges the gap between theoretical optimization and real-world deployment, offering scalable solutions that could transform how factories and infrastructure facilities manage multi-robot fleets. His algorithmic innovations promise to reduce operational costs and increase inspection efficiency, marking him as an emerging voice in robotics and operations research.
Research Focus
Key Achievements
Top Papers
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- 2