Marvin Dares

Papers

Total Citations

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About

Marvin Dares is a researcher advancing the intersection of robotics, fault detection, and augmented reality (AR) for industrial maintenance. His work focuses on developing intelligent systems that enhance the reliability and maintainability of automated machinery, particularly in the context of Industry 4.0. Dares’s most notable contribution is the creation of an Automated Guided Vehicle (AGV) test bed, detailed in his 2020 paper (6 citations), which simulates fault conditions and generates sensor data to improve fault detection algorithms—a critical step toward predictive maintenance. He also co-authored a comprehensive review on AR tracking methods for robot maintenance (5 citations), synthesizing approaches for applying AR to both large-scale assets and smaller robotic systems. More recently, his 2022 work on AGV localization using sensor fusion (2 citations) demonstrates ongoing innovation in robot navigation and robustness. While his citation counts are modest, Dares’s research provides foundational tools and frameworks for integrating AR and sensor-based diagnostics into real-world industrial settings, making him a practical contributor to the future of autonomous, self-diagnosing machinery.