Soroush Sadeghnejad
Amirkabir University of Technology, Sharif University of Technology
Papers
26
Total Citations
370
H-Index
12
About
Soroush Sadeghnejad is a leading researcher in humanoid robotics, multi-robot systems, and medical simulation technologies. His work spans from advancing autonomous soccer-playing robots to developing haptic feedback systems for surgical training. He has made significant contributions to the RoboCup Humanoid League and the HuroCup competition, with his seminal paper on the league’s evolution toward the 2050 goal of robots versus humans accumulating 71 citations. His research on multi-robot task allocation using clustering methods (44 citations) and humanoid robot detection through deep learning (23 citations) demonstrates his impact on autonomous coordination and perception. In medical robotics, Sadeghnejad has pioneered phenomenological tissue fracture modeling and haptic simulation for endoscopic sinus and skull base surgery training systems, with multiple papers receiving 13–20 citations. His work on adaptive control for robot-assisted telesurgery and nonlinear impedance control of virtual tool-tissue interaction has advanced realistic surgical simulators. Through these contributions, Sadeghnejad has established himself as a key figure bridging humanoid robotics and medical simulation, with his research cited over 270 times across top venues in robotics and surgical technology.
Research Focus
Key Achievements
Top Papers
- 1Humanoid Robots in Soccer: Robots Versus Humans in RoboCup 205071 citations · 2015
- 2Multi-robot Task Allocation Using Clustering Method44 citations · 2016
- 3HuroCup: competition for multi-event humanoid robot athletes35 citations · 2016
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- 5Humanoid Robot Detection Using Deep Learning: A Speed-Accuracy Tradeoff23 citations · 2018
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