Mahdi Hassan
Papers
13
Total Citations
247
H-Index
10
About
Mahdi Hassan’s research lies at the intersection of autonomous robotics, multi-robot coordination, and adaptive coverage path planning, with a particular focus on industrial and underwater applications. His most influential work, the predator-prey-based PPCPP algorithm (72 citations), revolutionized adaptive coverage path planning by enabling robots to dynamically respond to unexpected obstacles and environmental changes—a critical capability for real-world deployment. Hassan further advanced multi-robot collaboration through simultaneous area partitioning and allocation strategies, ensuring complete coverage in complex, unstructured environments. His practical impact is exemplified by the SPIR (Submersible Pylon Inspection Robot, 21 citations), an autonomous underwater vehicle designed for bridge pile cleaning and condition assessment, directly addressing safety risks in hazardous underwater maintenance. Across his top-cited papers, Hassan has accumulated over 200 citations, demonstrating significant influence in both theoretical and applied robotics. His work on deformable spiral-based algorithms for marine growth removal and decentralized multi-robot approaches to moving obstacle avoidance showcases his ability to tackle challenging real-world problems. Through these contributions, Hassan has established himself as a leading voice in making autonomous industrial robots more adaptive, collaborative, and practically deployable.
Research Focus
Key Achievements
Top Papers
- 1PPCPP: A Predator–Prey-Based Approach to Adaptive Coverage Path Planning72 citations · 2019
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