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About

Behraad Bahreyni is a researcher whose work sits at the intersection of sensor design, human-robot interaction safety, and industrial automation. His most significant contributions center on capacitive proximity sensing systems engineered to protect workers in environments where humans and robots operate in close proximity. His landmark 2018 paper, "Tri-Mode Capacitive Proximity Detection Towards Improved Safety in Industrial Robotics," accumulated 44 citations and introduced a wearable multi-functional sensor capable of maintaining safe separation distances between workers and robotic equipment—a critical advancement as collaborative robotics becomes increasingly prevalent in manufacturing settings. Building on this, his 2016 work on multi-functional capacitive proximity sensing systems (22 citations) expanded the sensing paradigm to include object distance measurement, motion tracking, and surface profile detection, enabling robust collision avoidance in partially modeled or unknown environments. His exploration of alternative sensing modalities is further demonstrated through his novel vector light detector design, which leveraged 3D printing to create a compact three-dimensional proximity sensor. More recently, his research has addressed the challenge of sensing accuracy near conductive objects through target profile estimation. Together, these contributions establish Bahreyni as a thoughtful innovator in safety-critical sensing technology.