Matthew D. Houghton

Papers

Total Citations

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About

Matthew D. Houghton is a researcher whose work sits at the intersection of optimal control, trajectory optimization, and autonomous systems, with a particular focus on advanced aerial mobility. His major contributions center on adapting and applying sophisticated algorithms—specifically Differential Dynamic Programming (DDP) and Model Predictive Path Integral (MPPI) control—to solve complex, real-time path planning problems. His most-cited paper, "Parameterized Differential Dynamic Programming" (2022, 17 citations), extends the classic DDP framework to efficiently handle time-invariant system parameters, a critical step for practical deployment. In a companion work (2022, 10 citations), Houghton directly demonstrates the viability of these methods for Vertical Takeoff and Landing (VTOL) aircraft, a cornerstone of Urban Air Mobility. By bridging the gap between theoretical robotics algorithms and the stringent demands of next-generation aviation, Houghton’s research provides a clear, computationally tractable pathway for enabling autonomous flight in congested urban environments, marking him as a key contributor to the future of autonomous aerial systems.