Max Robotics — Global Service Robot Directory + US Certification

About

Khalil Taheri’s research sits at the intersection of robot motion planning, multi-robot coordination, and modular robotics, with a strong emphasis on algorithmic efficiency and practical educational tools. His most cited work introduces a sampling algorithm for probabilistic roadmaps (PRMs) that reduces collision checking—a critical bottleneck for robots navigating narrow passages in high-dimensional configuration spaces. This contribution addresses a fundamental challenge in motion planning, directly impacting how robots compute safe paths in cluttered environments. Taheri also proposed an evolutionary artificial potential field method for stable multi-robot systems operating on domes, demonstrating a novel approach to leader-follower coordination with string-based connectivity. Further, he developed MVGS, a graph signature based on Multiple Views Theory for self-reconfiguration planning in modular robots, and designed the i-puck, an educational mobile robot platform that brings hands-on robotics to undergraduate and graduate curricula. Though his citation counts are modest, each paper targets a distinct, unsolved problem—from sampling efficiency to reconfiguration planning—showcasing a breadth of technical creativity. Taheri’s work is particularly valuable for students and researchers seeking practical, implementable solutions in robotic navigation and modular system design.