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About

Iyad Kanj is a researcher whose work sits at the dynamic intersection of soft robotics, robotic motion planning, and computational geometry. His research has made significant strides in two complementary domains: the theoretical foundations of path planning and the practical locomotion of bio-inspired soft robotic systems. Kanj's most influential contribution is his development of RRT*-based path planning algorithms for continuum arms—flexible, bio-inspired robotic manipulators that bend continuously rather than through discrete joints. His 2022 paper on this topic has garnered 91 citations, establishing him as a leading voice in this specialized field. Alongside earlier work on near-optimal and smooth path planning for continuum arms, he has systematically advanced how robots navigate complex environments with elegance and efficiency. Beyond manipulation, Kanj has expanded into soft robotic locomotion, exploring quadrupedal gaits, snake-inspired movement—including sidewinding and helical rolling—and novel tumbling locomotion in tetrahedral robots. His 2023 studies on teleoperation and modular soft robots further demonstrate a commitment to real-world applicability, particularly in search-and-rescue scenarios. With over 190 cumulative citations and a diverse but coherent research portfolio, Kanj exemplifies rigorous cross-disciplinary thinking that bridges algorithmic theory with physical robotic systems—making his work essential reading for students in robotics and motion planning alike.