Peter Won

Papers

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About

Peter Won is a leading researcher in modular mobile self-reconfigurable robotics, with a focus on autonomous docking systems and sensor integration. His most cited work, "Development of an Effective Docking System for Modular Mobile Self-Reconfigurable Robots Using Extended Kalman Filter and Particle Filter" (2015, 14 citations), introduces a groundbreaking autonomous docking algorithm that enables modular robots equipped with low-cost sensors to determine initial distance and orientation between modules. By establishing robust sensor models and integrating Extended Kalman Filters with Particle Filters, Won's system dramatically improves docking reliability in real-world conditions—a critical capability for self-reconfiguring robotic swarms. This work directly addresses the challenge of enabling inexpensive, scalable robotic modules to autonomously connect and reconfigure, advancing the field of modular robotics toward practical applications in search-and-rescue, space exploration, and adaptive manufacturing. Won's contributions demonstrate how sophisticated algorithmic approaches can overcome hardware limitations, making self-reconfigurable systems more accessible and deployable. His research continues to influence the development of autonomous robotic systems that can dynamically adapt their morphology to complex environments.