Manuel Quero
Papers
1
Total Citations
1
H-Index
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About
Manuel Quero is a robotics researcher whose work focuses on advancing autonomous navigation in complex, real-world environments. His primary research areas include hierarchical risk-aware planning, context-aware robot control, and intralogistics automation. Quero’s major contribution is the development of DARKO-Nav, a novel navigation framework that integrates risk assessment and contextual understanding to enable safer and more efficient robot movement in crowded, dynamic industrial settings. This work addresses critical challenges in warehouse and factory automation, where robots must interact with human workers, unpredictable obstacles, and narrow pathways. While his most-cited paper, “DARKO-Nav: Hierarchical Risk and Context-Aware Robot Navigation in Complex Intralogistic Environments” (2025), is still early in its citation life, it represents a significant step toward practical, deployable robotic systems. Quero’s research bridges the gap between theoretical planning algorithms and real-world constraints, offering a scalable solution for next-generation logistics. His contributions are particularly relevant for students and engineers interested in safe human-robot collaboration, autonomous mobile robots, and the integration of perception with decision-making.
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Top Papers
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