Jonathan Bagot

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About

Jonathan Bagot is a roboticist whose research focuses on motion planning and multi-robot coordination for humanoid platforms. His most cited work introduces a full-body motion planning framework for humanoid robots using Rapidly Exploring Random Trees (RRTs), enabling complex, collision-free locomotion in constrained environments. This paper has garnered 8 citations, reflecting its foundational role in advancing humanoid mobility. Bagot also contributed to a notable 2011 demonstration where teams of small humanoid robots—powered by mobile phones for vision, balance, and processing—successfully navigated an obstacle course. In this work, the robots employed particle filters for simultaneous localization and mapping (SLAM) and frontier-based exploration to traverse unknown terrain. This achievement showcased practical, low-cost approaches to multi-agent autonomy and has been cited 4 times for its innovative integration of consumer hardware. Bagot’s research bridges theoretical motion planning with real-world deployment, offering scalable solutions for humanoid robotics. His work continues to inspire new directions in autonomous navigation and collaborative robot teams, making him a key figure in the field of humanoid robotics and embodied AI.