Max Robotics — Global Service Robot Directory + US Certification

About

Amir Nakib is a leading researcher in robotics and optimization, whose work focuses on the complex challenge of trajectory and path planning for redundant manipulators. His primary contributions lie in developing advanced computational methods to solve bilevel optimization problems, which are critical for enabling robots to navigate cluttered environments while maximizing performance metrics like manipulability. Nakib’s most influential paper, “A trajectory planning of redundant manipulators based on bilevel optimization” (2014), has garnered 50 citations, establishing a foundational approach for coordinating high-level task objectives with low-level motion constraints. He has pioneered the use of metaheuristic algorithms, including genetic algorithms and particle swarm optimization, to generate smooth, collision-free trajectories that respect the redundancy of robotic arms. His work on “Path planning for redundant manipulators using metaheuristic for bilevel optimization” (2013) further demonstrates his ability to integrate obstacle avoidance with optimal control. With over 70 total citations across his key publications, Nakib’s research has significantly advanced the field of autonomous robotics, providing practical tools for industrial automation and human-robot interaction. His achievements highlight a career dedicated to bridging theoretical optimization with real-world robotic applications.