Papers
22
Total Citations
787
H-Index
10
About
Tarek Taha is a multidisciplinary researcher whose work spans flexible electronics, autonomous robotics, and intelligent human-robot interaction. His research has made significant contributions across several interconnected domains, earning widespread recognition in both engineering and computer science communities. Taha's early work in wearable technology produced a landmark 2016 paper on tissue paper-derived carbon-PDMS strain sensors, which has accumulated 239 citations and demonstrated an elegantly simple fabrication method for highly flexible resistive sensors with strong commercial potential. Simultaneously, his robotics research has shaped how autonomous systems inspect and map complex environments. His influential surveys on multi-robot coverage path planning (172 citations) and robotic structural inspection (108 citations) have become foundational references for researchers entering these fields. A recurring theme in Taha's work is intelligent decision-making under uncertainty. His POMDP-based frameworks for wheelchair navigation and assistive human-robot interaction pioneered probabilistic approaches to shared autonomy, helping robots better anticipate and respond to human intent. More recently, his contributions to Visual SLAM methodologies and semantic hazard mapping for search-and-rescue robots demonstrate a sustained commitment to pushing autonomous systems into real-world, safety-critical applications. Across his career, Taha has consistently bridged hardware innovation and algorithmic intelligence to advance human-centred autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1Highly Flexible Strain Sensor from Tissue Paper for Wearable Electronics239 citations · 2016
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- 3A survey on inspecting structures using robotic systems108 citations · 2016
- 4POMDP-based long-term user intention prediction for wheelchair navigation64 citations · 2008
- 5A POMDP framework for modelling human interaction with assistive robots39 citations · 2011
- 6A Survey of Visual SLAM Methods36 citations · 2023
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