Papers
14
Total Citations
295
H-Index
8
About
Iraj Mantegh is a leading researcher in intelligent robotics and autonomous systems, with a focus on robot programming by demonstration (PbD), path planning, and automated manufacturing. His seminal work, "Trajectory Learning for Robot Programming by Demonstration Using Hidden Markov Model and Dynamic Time Warping" (158 citations), pioneered efficient methods for encoding and generalizing complex trajectories, enabling robots to learn from human demonstrations with high accuracy. Mantegh further advanced PbD through conditional random fields and image-based trajectory planning, significantly enhancing robotic skill transfer in manipulation tasks. In manufacturing, he developed intelligent thermal control for resistance welding of fiberglass laminates (17 citations), automating large-scale joining processes for thermoplastic composites. His contributions extend to coverage path planning for eddy current inspection on complex aeronautical parts, improving non-destructive testing in aerospace. Mantegh also innovated in autonomous navigation, applying boundary integral equation methods for mobile robot path planning and, more recently, integrating deep reinforcement learning with fuzzy logic for UAV multi-dynamic target interception (2024). With over 280 total citations across his most-cited works, Mantegh’s research bridges theoretical advances in learning and planning with practical applications in aerospace, manufacturing, and field robotics, making him a pivotal figure in modern autonomous systems.
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