Amir Ali Mokhtarzadeh

About

Amir Ali Mokhtarzadeh is a leading researcher at the intersection of autonomous robotics, human-robot interaction (HRI), and intelligent navigation systems. His work is pivotal in advancing the safety and autonomy of self-driving cars and mobile robots, particularly through the integration of dispositif networks and multi-sensor fusion. Mokhtarzadeh’s most cited paper (9 citations) proposes a novel HRI model to reduce accidents in autonomous vehicles, addressing a critical gap in AI safety. He has also pioneered a hybrid robot positioning technique combining GNSS-RTK with visual SLAM, overcoming limitations of single-system navigation in complex environments. His contributions extend to multi-angle facial expression recognition using lightweight deep networks, enhancing robot social perception, and developing novel cartography methods with OAK-D smart cameras for indoor robot localization. More recently, he has tackled the practical challenge of automatic charging for quadruped robots, improving docking success rates through infrared and laser sensor modules. With a growing citation impact and a focus on real-world deployment, Mokhtarzadeh’s research is shaping the next generation of safe, perceptive, and autonomous robotic systems.

Research Focus

Key Achievements

Top Papers

1. 1
   Human-Robot Interaction and Self-Driving Cars Safety Integration of Dispositif Networks

   9 citations · 2018
2. 2
   Robot positioning and navigation technology is based on Integration of the Global Navigation Satellite System and real-time kinematics

   5 citations · 2023
3. 3
   Multi-angle face expression recognition based on integration of lightweight deep network and key point feature positioning

   5 citations · 2023
4. 4
   Novel cartographer using an OAK-D smart camera for indoor robots location and navigation

   2 citations · 2023
5. 5
   Research on automatic charging method based on quadruped robot

   1 citations · 2023

Key Collaborators

Contact & Links