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About

Michael Mangan is a leading researcher in bio-inspired robotics and insect navigation, whose work bridges neuroscience and artificial intelligence to create more efficient autonomous systems. His primary research areas include insect-inspired navigation, neuromorphic computing, and computational models of path integration. Mangan’s major contributions include evolving a neural model of insect path integration (56 citations), which explains how insects use celestial cues for navigation, and developing a computational model of the insect polarised light compass (55 citations) that translates skylight input into behavioral output. His work on route-following without scanning (65 citations) has been particularly influential, demonstrating how insects navigate complex environments with minimal sensory processing. More recently, Mangan has pioneered neuromorphic sequence learning with event cameras (26 citations), enabling low-power robotic navigation through vegetation. His research has achieved over 300 total citations, with notable recognition for his perspective on the virtuous cycle between invertebrate and robotics research. Mangan’s work is distinguished by its practical applications in robotics, including non-destructive fruit estimation for horticulture and place recognition systems, making him a key figure in translating biological principles into technological innovations.