Karl Klingebiel

Papers

Total Citations

149

H-Index

About

Karl Klingebiel is a leading researcher in the field of autonomous robotics, with a primary focus on safe motion planning, collision avoidance, and real-time control systems. His most influential work, "Comparative Analysis of Control Barrier Functions and Artificial Potential Fields for Obstacle Avoidance" (2021, 149 citations), provides a rigorous, side-by-side evaluation of two foundational approaches to robotic navigation. This paper has become a key reference for engineers and academics, offering critical insights into the trade-offs between model-independent, easy-to-implement methods like Artificial Potential Fields and more formal, safety-guaranteeing frameworks like Control Barrier Functions. Klingebiel’s contribution lies not only in clarifying the practical strengths and limitations of each technique but also in guiding the development of more robust, hybrid solutions for real-time obstacle avoidance in complex environments. His work is widely cited for its clarity and practical relevance, making it essential reading for anyone designing autonomous systems for mobile robots or manipulators. Through this analysis, Klingebiel has helped shape the conversation on how to balance computational efficiency with provable safety in modern robotics.