Amalie Heiberg

Norwegian University of Science and Technology

Papers

Total Citations

H-Index

About

Amalie Heiberg is a leading researcher in autonomous maritime systems, with a primary focus on collision avoidance and path following for unmanned surface vehicles (USVs). Her most cited work, "COLREG-Compliant Collision Avoidance for Unmanned Surface Vehicle Using Deep Reinforcement Learning" (2020), has garnered 89 citations, establishing her as a key contributor to the integration of artificial intelligence with maritime navigation. Heiberg’s major contribution lies in developing deep reinforcement learning frameworks that ensure autonomous vessels adhere to the International Regulations for Preventing Collisions at Sea (COLREGs), a critical step toward safe and legal autonomous navigation. By addressing the long-standing challenge of merging path following with collision avoidance, her research bridges classical robotics guidance problems with modern AI solutions. This work has significant implications for the future of maritime transport, offshore operations, and environmental monitoring. Heiberg’s achievements highlight her ability to translate complex regulatory requirements into practical, AI-driven algorithms, making her a notable figure in the growing field of autonomous surface vehicles. Her research continues to inspire advancements in safe, compliant, and efficient maritime autonomy.