Max Robotics — Global Service Robot Directory + US Certification

About

Pantelis Sopasakis is a researcher specializing in nonlinear model predictive control (NMPC), real-time optimization, and autonomous robotics, with a particular focus on developing computationally efficient algorithms for safety-critical applications. His most influential contribution is the application of the Proximal Averaged Newton-type method for Optimal Control (PANOC) to obstacle avoidance problems, introducing a novel modeling framework capable of handling generic nonconvex obstacles, including polytopes and ellipsoids. This work, published in 2018, has garnered 98 citations, reflecting its significant impact on the embedded control and autonomous systems communities. Building on this foundation, Sopasakis has extended his research into collaborative robotics, developing fast NMPC solutions for context-aware robotic arms operating in unstructured, shared human-robot environments where collision avoidance and real-time path planning are essential. His more recent work integrates NMPC with Energy-based Control Barrier Functions (ECBFs) within vision-based human-robot collaboration frameworks, advancing the frontier of safe, perception-driven robot control in smart manufacturing settings. Across his research portfolio, Sopasakis consistently bridges rigorous mathematical optimization with practical embedded implementation, making sophisticated control strategies accessible for real-world deployment.