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About

Andrew Singletary is a robotics and control systems researcher whose work centers on safety-critical control, obstacle avoidance, and the theoretical foundations of autonomous systems. He is best known for his pioneering contributions to **Control Barrier Functions (CBFs)**, a mathematical framework that provides formal safety guarantees for robotic and autonomous systems operating in complex, dynamic environments. Singletary's most cited work, "Guaranteed Obstacle Avoidance for Multi-Robot Operations With Limited Actuation" (164 citations), established a decentralized CBF-based supervisory controller for multi-robot collision avoidance — a foundational contribution to safe multi-agent systems. His comparative study of CBFs versus Artificial Potential Fields (149 citations) became an essential reference for researchers evaluating modern safety control methods against classical approaches. Beyond theory, Singletary has demonstrated a strong commitment to real-world applicability, extending CBF frameworks to handle stochastic uncertainty, imperfect state measurements, and practical cyber-physical system constraints. His work spans robotic manipulators, human-robot collaboration, food preparation automation, and nonlinear model predictive control integrated with stability guarantees. With over 600 cumulative citations and contributions appearing across robotics, control theory, and autonomous systems, Singletary represents a new generation of researchers bridging rigorous mathematical safety guarantees with deployable robotic solutions.