Mohamed S Marzouqi
Engineering Systems (United States), Australian Research Council, Monash University
Papers
5
Total Citations
89
H-Index
4
About
Mohamed S. Marzouqi is a robotics researcher whose work has made meaningful contributions to the field of autonomous robot navigation, with a particular focus on covert path planning and visibility-based motion strategies. His research addresses one of the more tactically nuanced challenges in mobile robotics: enabling robots to navigate environments while minimizing detection by hostile sensors or sentries. Marzouqi's most influential contribution, "Covert Path Planning for Autonomous Robot Navigation in Known Environments" (2003, 28 citations), laid the foundation for what he termed *Covert Robotics* — a framework for stealth-driven navigation in cluttered spaces. He subsequently extended this work to unknown environments (2004, 25 citations), demonstrating how robots could adaptively plan hidden paths even without prior knowledge of adversarial positions. His 2006 paper on visibility-based path planning (27 citations) refined these approaches through novel visibility sensitivity modeling, while his 2005 work addressed computational efficiency in evaluating robot exposure. Additional contributions include pursuit strategies using convex region segmentation. Collectively, Marzouqi's body of work, accumulating nearly 90 citations, has helped establish covert robotics as a distinct and practically relevant subfield, with applications in surveillance, search-and-rescue, and defense-oriented autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1Covert path planning for autonomous robot navigation in known environments28 citations · 2003
- 2New visibility-based path-planning approach for covert robotic navigation27 citations · 2006
- 3Covert robotics: covert path planning in unknown environments25 citations · 2004
- 4Fast visibility evaluation for covert robotics path planning6 citations · 2005
- 5Efficient robotic pursuit of a moving target in a known environment using a novel convex region segmentation3 citations · 2004