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About

Matthew Daigle is a researcher whose work sits at the intersection of fault diagnosis, prognosis, and autonomous systems, with particular emphasis on mobile robotics and aerospace applications. He has made foundational contributions to distributed fault diagnosis in multi-robot systems, most notably through his highly cited 2007 paper on distributed diagnosis in mobile robot formations (124 citations), which addressed the critical challenge of maintaining safe operation in complex, decentralized robotic environments without the computational burden of centralized approaches. His 2008 work on qualitative event-based fault diagnosis of hybrid systems (42 citations) further demonstrated his ability to tackle the nuanced behavior of systems that blend continuous physical dynamics with discrete computational processes. Beyond diagnosis, Daigle has pioneered the field of Prognostics-enabled Decision Making (PDM), developing hardware testbeds and validation methodologies that bridge theoretical algorithms and real-world deployment on mobile robot platforms. His 2020 testbed development paper (37 citations) reflects ongoing impact in enabling autonomous systems to act intelligently on health-state information. Spanning nearly two decades of research, Daigle's body of work has meaningfully advanced how autonomous and aerospace systems detect faults, predict failures, and make safer, smarter operational decisions—a contribution of growing importance as unmanned systems become increasingly prevalent.