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About

Riad Menasri is a robotics researcher specializing in trajectory and path planning for redundant manipulators, with a focus on bilevel optimization and metaheuristic algorithms. His most cited work, "A trajectory planning of redundant manipulators based on bilevel optimization" (2014, 50 citations), introduces a novel framework that optimizes both end-effector positioning and joint configuration simultaneously, addressing the inherent redundancy in robotic arms. This approach enables smoother, more efficient motion in complex environments. Menasri further advanced the field by integrating metaheuristic methods, such as genetic algorithms and particle swarm optimization, to solve bilevel optimization problems for path planning under obstacle constraints, as seen in his 2013 paper (9 citations) and 2014 work (8 citations). His contributions emphasize maximizing manipulability and trajectory smoothness, critical for industrial and service robotics. With over 77 total citations across his key papers, Menasri’s research bridges theoretical optimization and practical robotic control, offering scalable solutions for real-world manipulation tasks. His work is particularly notable for its application of bilevel optimization—a sophisticated technique that coordinates high-level path decisions with low-level joint movements—making him a notable figure in autonomous robotic systems.