Matthew P. Castanier
United States Department of the Army, DEVCOM Army Research Laboratory, United States Army
Papers
9
Total Citations
148
H-Index
7
About
Matthew P. Castanier is a robotics and autonomous systems researcher whose work sits at the intersection of multi-robot coordination, energy-aware planning, and decision-making under uncertainty. His research has made significant contributions to how heterogeneous robot teams navigate, plan, and collaborate in complex, real-world environments — particularly off-road settings where terrain variability introduces substantial operational challenges. Castanier's most influential work (43 citations) introduced a stochastic programming framework for simultaneously optimizing task decomposition, assignment, and scheduling across heterogeneous robot teams under capability uncertainty — a practically important advance for applications ranging from pandemic response to disaster robotics. Complementing this, his probabilistic spatial mapping approach to energy cost prediction (37 citations) addresses a critical gap in off-road autonomy, enabling robots to anticipate terrain-dependent power consumption and plan more reliable missions. His broader body of work extends these themes into multi-robot reconnaissance (19 citations), chance-constrained path planning, power sharing across robot fleets, and even human-robot tour guidance systems. Collectively, his research reflects a coherent vision: making autonomous robot teams smarter, more energy-efficient, and more resilient when operating in uncertain, unstructured environments. With over 140 cumulative citations, Castanier's contributions offer valuable foundations for students and researchers working in field robotics, swarm systems, and autonomous mission planning.
Research Focus
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Top Papers
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