Hafiz Oyediran
Papers
5
Total Citations
37
H-Index
4
About
Hafiz Oyediran is a pioneering researcher at the intersection of robotics, construction automation, and building information modeling (BIM). His work focuses on enabling robots to operate safely and intelligently in complex indoor environments—from construction sites to power plants. Oyediran’s most influential contribution is the integration of real-time semantic building map updating with Adaptive Monte Carlo Localization (AMCL), a framework that allows mobile robots to maintain robust localization even when faced with non-structural changes in indoor spaces. This work, published in 2023, has already garnered 21 citations for its practical impact on autonomous navigation. He has also advanced construction robotics by linking 4D BIM with robot task planning, enabling action-level simulation for tasks like wall frame installation—a critical step toward fully automated construction workflows. Addressing safety in human-robot collaboration, Oyediran developed a human-aware control and monitoring system for congested construction environments, earning 4 citations. His earlier work assessed robotics applicability for routine operator tasks in power plants, demonstrating his versatility across industrial domains. With a growing citation record and a clear trajectory toward safer, more intelligent automation, Oyediran is shaping the future of robotics in the built environment.
Research Focus
Key Achievements
Top Papers
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- 5Autonomous Building Occupancy Monitoring Using Mobile Robots2 citations · 2022