M. Mahdi Ghazaei Ardakani
Papers
11
Total Citations
229
H-Index
8
About
M. Mahdi Ghazaei Ardakani is a robotics researcher whose work spans trajectory generation, human-robot interaction, and wearable robotics. His most significant contributions lie in applying Model Predictive Control (MPC) to real-time trajectory planning for robotic manipulators — a body of work that has garnered nearly 90 citations across two landmark publications from 2015 and 2018, establishing him as a key voice in intelligent motion planning for industrial robots. Equally influential is his research into sensorless kinesthetic teaching, where he developed observer-based force control methods that allow industrial robots to be reprogrammed through physical guidance without costly force-torque sensors. These contributions, cited over 65 times collectively, address a critical need in flexible manufacturing environments. His work on friction compensation further strengthens this line of research by making lead-through programming more practical and accessible. Beyond manipulation, Ghazaei Ardakani has made notable strides in wearable robotics, proposing optimal actuator selection frameworks for back-support exoskeletons, and has explored non-prehensile object manipulation and reinforcement learning for dexterous grippers. His diverse yet cohesive research portfolio reflects a consistent commitment to making robots more adaptable, intuitive, and physically capable in real-world environments.
Research Focus
Key Achievements
Top Papers
- 1Real-time trajectory generation using model predictive control51 citations · 2015
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- 3Model Predictive Control for Real-Time Point-to-Point Trajectory Generation36 citations · 2018
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- 6Quasi-static analysis of planar sliding using friction patches16 citations · 2020
- 7Trajectory Generation for Assembly Tasks via Bilateral Teleoperation11 citations · 2014
- 8Reinforcement Learning for 4-Finger-Gripper Manipulation8 citations · 2018
- 9Online Minimum-Jerk Trajectory Generation6 citations · 2015
- 10