Richard West

Laboratoire d'Informatique de Paris-Nord

Papers

Total Citations

H-Index

About

Richard West's research bridges the critical gap between embedded systems, real-time computing, and autonomous control. His most influential work, "Reinforcement Learning for UAV Attitude Control" (2019, 19 citations), pioneers the application of machine learning to autopilot systems, replacing traditional PID controllers with adaptive algorithms that unify inner-loop stability and outer-loop mission planning—a breakthrough for agile unmanned aerial vehicles. West also revolutionized physical computing with "Qduino: A Multithreaded Arduino System for Embedded Computing" (2015, 14 citations), introducing multithreading to the Arduino ecosystem. This work enabled low-cost, real-time multitasking for robotics and IoT devices, democratizing access to complex embedded applications. His latest contribution, "Telomere: Real-Time NAND Flash Storage" (2022), tackles the persistent challenge of garbage collection in solid-state drives, proposing a novel storage architecture that minimizes latency for time-critical systems. With a career spanning control theory, embedded platforms, and storage systems, West's interdisciplinary approach has shaped how engineers design responsive, intelligent hardware. His work on Qduino remains a cornerstone for hobbyists and researchers alike, while his UAV research continues to influence autonomous flight control.