Andrew C. Browning

Papers

Total Citations

149

H-Index

About

Andrew C. Browning is a leading figure in the field of robotic motion planning and safety-critical control. His research primarily focuses on the intersection of obstacle avoidance, real-time navigation, and formal safety guarantees for autonomous systems. Browning’s most impactful work, "Comparative Analysis of Control Barrier Functions and Artificial Potential Fields for Obstacle Avoidance" (2021, 149 citations), provides a definitive benchmark between two foundational paradigms. He systematically evaluates the model-independent, computationally lightweight artificial potential fields (APFs) against the more rigorous, safety-certified control barrier functions (CBFs), offering engineers a clear framework for selecting the appropriate method based on application constraints. This contribution is widely cited for its practical, comparative insights that bridge decades of classical robotics with modern formal methods. Beyond this landmark study, Browning is recognized for advancing the integration of CBFs into real-time systems, ensuring that autonomous vehicles and manipulators can navigate dynamic environments with provable collision avoidance. His work is essential reading for students and researchers seeking to understand the trade-offs between simplicity and safety in autonomous navigation.