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About

Mohammadali Shahriari is a robotics researcher whose work spans robotic manipulation, multi-robot navigation, and autonomous systems. He is perhaps best known for his highly influential 2016 survey, "State of the Art Robotic Grippers and Applications," which has garnered over 230 citations and remains a foundational reference for researchers exploring modern gripping technologies, including soft and adaptive robotic hands. A significant thread of Shahriari's research addresses the challenges of multi-robot coordination in complex environments. His work on conflict resolution and motion-liveness for multiple mobile robots — using mathematical optimization and metaheuristic algorithms — tackles the critical problem of enabling large robot fleets to navigate cluttered, dynamic spaces without deadlock or collision. Building on this foundation, his later contributions introduced predictive safety criteria and time-to-collision-based methods that incorporate robot dynamics, moving beyond simplistic kinematic models to achieve more robust and realistic collision avoidance. Shahriari has also explored legged robotics, applying fuzzy reward reinforcement learning to develop adaptive gaits for hexapod robots traversing uneven terrain. Across his career, his research consistently bridges theoretical rigor with practical implementation, as evidenced by his lightweight collision avoidance framework designed for resource-constrained platforms. His cumulative body of work reflects a sustained commitment to making autonomous robots safer, smarter, and more deployable in real-world settings.