Max Robotics — Global Service Robot Directory + US Certification

About

John Galeotti’s research career bridges two fascinating domains: evolutionary robotics and robotic ultrasound systems. His early work laid the foundation for autonomous robot colonies, most notably through the design and construction of EvBot mobile robots, which enabled groundbreaking studies in maze exploration and evolutionary behavior. His 2004 paper on maze exploration behaviors remains his most cited work, with 51 citations, demonstrating its lasting influence on the field of evolutionary robotics. More recently, Galeotti has made significant contributions to medical robotics, particularly in autonomous ultrasound imaging. His 2022 paper on autonomous ultrasound scanning using Bayesian optimization and hybrid force control (27 citations) addresses the critical shortage of skilled sonographers by developing systems that can perform ultrasound procedures without a human-in-the-loop. He has also advanced needle tracking in ultrasound-guided interventions, developing novel algorithms that account for needle bending in heterogeneous tissue—a realistic challenge often overlooked in prior research. His work on unsupervised deformable ultrasound image registration (U-RAFT) and vessel segmentation showcases his ability to integrate deep learning with real-time clinical applications. Galeotti’s research is characterized by a rare combination of foundational robotics principles and cutting-edge medical applications, making him a notable figure in both evolutionary and medical robotics.