Sami Khorbotly
Papers
8
Total Citations
57
H-Index
5
About
Sami Khorbotly is a leading researcher in the field of robotic swarms and multi-agent systems, with a focus on developing cost-effective algorithms for simple, inexpensive robots. His work centers on enabling complex swarm behaviors—such as dispersion, tracking, and search-and-rescue—using limited sensing and computing capabilities. Khorbotly’s major contributions include a gradient descent algorithm for robotic swarm dispersion (11 citations) and an RSS-based triangulation method for robot tracking (10 citations), both of which address the challenge of maximizing functionality while minimizing individual robot cost. He has also adapted nature-inspired optimization algorithms for real-world robotics, notably applying particle swarm optimization to search-and-rescue missions (9 citations) and implementing ant colony optimization on multi-core robots (9 citations). Beyond swarms, Khorbotly has advanced industrial robotics through vision-based programming that automates the teach-repeat paradigm (10 citations). His interdisciplinary work includes a robotic football dance team that bridges engineering and fine arts, demonstrating his commitment to innovative, cross-disciplinary learning experiences. With over 50 citations across his most influential papers, Khorbotly’s research continues to shape practical, scalable solutions in autonomous robotics.
Research Focus
Key Achievements
Top Papers
- 1Robotic Swarm Dispersion Using Gradient Descent Algorithm11 citations · 2019
- 2An RSS-based triangulation method for robot tracking in robotic swarms10 citations · 2017
- 3Teachless teach-repeat: Toward vision-based programming of industrial robots10 citations · 2012
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- 6A camera-based target tracking system for football playing robots3 citations · 2012
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- 8A Vision-Based Feedback and Supervision System for Robotic Swarms2 citations · 2020