About

Othman Lakhal is a robotics and automation researcher whose work sits at the intersection of continuum robotics, adaptive control, and additive manufacturing. He is best known for his extensive contributions to modeling and controlling biologically inspired soft and continuum manipulators — robots that mimic the fluid motion of trunks, tentacles, and snakes. His 2015 paper on adaptive neural network control of the Compact Bionic Handling Arm (CBHA), which has garnered over 112 citations, established him as a leading voice in intelligent control strategies for this notoriously complex class of robots. Alongside this, his hybrid kinematic modeling approach (65 citations) demonstrated innovative methods for real-time inverse kinematics, blending quantitative and learning-based techniques. Lakhal has further advanced the field through metaheuristic and finite element-based approaches to kinematic modeling, consistently pushing the boundaries of accuracy and computational efficiency. More recently, he has extended his expertise into robotized additive manufacturing for architectural construction, exploring how hyper-redundant robots can enable complex geometric fabrication. With a body of work spanning adaptive algorithms, path planning, and obstacle avoidance for mobile soft manipulators, Lakhal's research has meaningfully shaped both theoretical foundations and practical applications of next-generation robotic systems.

Research Focus

Key Achievements

11
H-Index
27
Papers
448
Total Citations
17
Avg Citations/Paper
🏆 Most Cited Paper
Adaptive Neural Network Control of a Compact Bionic Handling Arm
112 citations · 2015
📈 Most Prolific Year: 2017 (6 Papers)
🤝 Key Collaborators: 52
🏛 Institutions: École Polytechnique, Centre National de la Recherche Scientifique, École Centrale de Lille, Polytechnic School of Algiers, Institut national de recherche en sciences et technologies du numérique, Université de Lille

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 14 days ago