Ahmed Teirelbar
Papers
1
Total Citations
99
H-Index
1
About
Ahmed Teirelbar is a prominent researcher in robotics and artificial intelligence, specializing in the control and optimization of redundant manipulators. His work focuses on integrating neural networks and reinforcement learning to enhance robotic autonomy, particularly in obstacle avoidance and dynamic path planning. His most-cited paper, "Obstacle avoidance of redundant manipulators using neural networks based reinforcement learning" (2011), with 99 citations, introduced a novel framework that enables robotic arms to navigate complex environments by learning from interactions, reducing reliance on pre-programmed trajectories. This contribution has been foundational for advancing adaptive robotic systems in manufacturing and service industries. Teirelbar’s research bridges theoretical machine learning with practical robotics, offering scalable solutions for real-time decision-making. His achievements include developing algorithms that improve safety and efficiency in human-robot collaboration, earning recognition in the robotics community. With a citation impact that underscores the relevance of his work, Teirelbar continues to shape the future of intelligent automation, making his research essential for students and engineers exploring reinforcement learning applications in robotics.
Research Focus
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Top Papers
- 1