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About

Evan Shellshear is a researcher whose work bridges the critical intersection of computational geometry, robotics, and industrial safety. His primary research areas include collision detection, automatic path planning, and the emerging field of trust and safety in autonomous systems. Shellshear’s most significant contributions lie in developing algorithms for collision-free path planning in complex environments, particularly for deforming cables and hybrid triangle-and-point models. His highly cited papers, such as "1D sweep-and-prune self-collision detection for deforming cables" (12 citations) and "Automatic collision free path planning in hybrid triangle and point models" (12 and 11 citations), have directly impacted assembly analysis, robot line optimization, and virtual maintenance assessment. These works provide robust, fast proximity queries essential for industrial applications. Notably, his later work on "Trust and Safety" (2021) addresses society’s growing expectations for safety management systems in robotics, reflecting a shift toward human-centered automation. With a career spanning technical algorithm design to ethical safety frameworks, Shellshear’s research continues to shape how robots interact safely and efficiently with their environments.