Alan Broun

University of the West of England, Bristol Robotics Laboratory

Papers

Total Citations

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About

Alan Broun’s research lies at the intersection of robotics, computer vision, and autonomous systems, with a particular focus on enabling robots to understand and model their own physical structure. His most influential work centers on kinematic self-calibration—the process by which a robot can autonomously build an accurate model of its arm’s geometry using only visual input. In his highly cited 2013 paper, “Bootstrapping a robot’s kinematic model” (9 citations), Broun introduced a novel method that allows a robot to learn its own kinematic parameters from scratch, without prior knowledge or external measurement tools. This foundational approach was further developed in his 2012 study, “Building a Kinematic Model of a Robot’s Arm with a Depth Camera” (5 citations), where he demonstrated practical implementation using affordable depth-sensing technology. Broun’s contributions are significant for advancing robot autonomy, reducing the need for manual calibration, and enabling more flexible and adaptive robotic systems. His work has been recognized as a key step toward truly self-aware robots capable of operating in unstructured environments.