Marc van Eert

Papers

Total Citations

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About

Marc van Eert is a researcher whose work sits at the intersection of robotics, artificial intelligence, and algorithm optimization. His primary research focus is on the automated tuning and configuration of path planning algorithms—a critical challenge in fields ranging from autonomous navigation to industrial robotics. Van Eert’s most cited paper, "Automated tuning and configuration of path planning algorithms" (2017, 17 citations), tackles the long-standing problem of parameter optimization in path planning. While dozens of novel algorithms have been developed over the past decades, their real-world performance often hinges on subtle configuration choices that are poorly understood. Van Eert’s contribution lies in formalizing this tuning process, moving it from ad-hoc trial-and-error to a systematic, data-driven methodology. His work demonstrates that automated configuration can dramatically improve algorithm efficiency and reliability, making advanced path planning more accessible to practitioners. Though his citation count is modest, van Eert’s research addresses a fundamental bottleneck in robotics and autonomous systems, offering practical tools that bridge the gap between theoretical algorithm design and real-world deployment. His work is particularly valuable for students and engineers seeking to deploy robust navigation systems without exhaustive manual tuning.