Papers
10
Total Citations
55
H-Index
5
About
Omar Al-Buraiki’s research lies at the intersection of multi-robot task allocation, cooperative formation control, and autonomous perception for unmanned systems. His core contribution is a suite of probabilistic and suitability-based frameworks that match specialized robotic agents to tasks based on their unique capabilities and target characteristics—an approach that addresses the fundamental challenge of deploying heterogeneous swarms in dynamic environments. His most cited work (14 citations) introduces a probabilistic assignment method for specialized multi-agent systems, while subsequent papers extend this to vision-based target recognition and deep learning–driven detection. Beyond allocation, Al-Buraiki has advanced formation and containment control for heterogeneous UAV-AUV teams with actuator delays, and developed robust visual-inertial-wheel odometry with slip compensation and dynamic feature elimination—work that directly improves real-world robot localization. With over 50 total citations across a decade of publications, his research systematically bridges theoretical task assignment with practical perception and control, offering scalable solutions for collaborative autonomous systems in applications ranging from search-and-rescue to environmental monitoring.
Research Focus
Key Achievements
Top Papers
- 1Probabilistic Task Assignment for Specialized Multi-Agent Robotic Systems14 citations · 2019
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