Masri Diar

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About

Masri Diar is a robotics researcher whose work focuses on enabling autonomous locomotion and navigation for legged robots in complex, semi-structured environments. His primary research areas include motion planning, foothold placement, and vision-based navigation for hexapod walking robots. Diar’s most impactful contribution is his work on foothold placement planning for hexapod crawling robots, which has garnered 23 citations. In this study, he proposed a pipelined approach using an RGB-D camera to perceive terrain in 2.5D, allowing precise foot-tip positioning—a critical capability for robots operating on uneven or cluttered surfaces. He also advanced multi-goal trajectory planning by integrating motion primitives with unsupervised learning to solve the traveling salesman problem for efficient path generation. Additionally, Diar addressed latency challenges in vision-based navigation systems, developing a low-latency, FPGA-centric image processing method to enable real-time control from visual data. His work bridges perception, planning, and control, contributing practical solutions for deploying walking robots in real-world scenarios. With a citation count exceeding 30 across his key papers, Diar’s research continues to influence the fields of field robotics and autonomous navigation.