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About

Khaled Belghith is a robotics and artificial intelligence researcher whose work centers on motion planning, telerobotics, and intelligent training systems — with particular focus on space robotics applications. His most significant contribution lies in advancing probabilistic roadmap (PRM) methods for robot path planning, most notably his 2006 paper on anytime dynamic path-planning with flexible probabilistic roadmaps, which has garnered 63 citations and introduced three novel features enabling real-world applicability in complex 3D environments. Belghith has made notable strides in integrating intelligent path-planning algorithms with educational simulators, developing systems designed to train operators of the Space Station Remote Manipulator System (SSRMS) aboard the International Space Station. His 2005 work on autonomous training for space robot manipulators (35 citations) and his Roman Tutor intelligent simulator (22 citations) demonstrate a sustained commitment to bridging advanced robotics with human-centered training technologies. Throughout his career, Belghith has explored automated camera planning, 3D task demonstrations, and workspace desirability modeling, consistently pushing the boundaries of how robots can be programmed, demonstrated, and taught. His cumulative body of work reflects meaningful impact at the intersection of AI, space technology, and robotics education.