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About

Stephen Hailes is a versatile researcher whose work spans autonomous systems, surgical robotics, and sensor networks. He is perhaps best known for his influential 2014 study on the "shepherding problem," which modeled how a single agent can herd multiple autonomous individuals — a deceptively simple biological phenomenon with profound implications for crowd control, environmental management, and swarm robotics. This paper has garnered over 213 citations, reflecting its broad interdisciplinary appeal. A significant portion of Hailes' research addresses hand-eye calibration in robotic-assisted minimally invasive surgery, where precise alignment between robotic and camera coordinate frames is critical for patient safety and surgical accuracy. His series of papers on this topic — including novel approaches using adjoint transformations, screw constraints, and remote centre-of-motion configurations — have collectively accumulated over 125 citations, establishing him as a key contributor to surgical robotics methodology. Beyond these flagship areas, Hailes has explored multi-robot search and rescue planning, prosthetic feedback systems using electromyography, and sensor network deployment for emergency tunnel response. This breadth reflects a researcher committed to translating computational and robotic intelligence into real-world safety and healthcare applications, making his work valuable reading for students across robotics, biomedical engineering, and autonomous systems.