Daniel Axehill

Papers

Total Citations

H-Index

About

Daniel Axehill is a leading researcher in robotics and control systems, with key contributions to motion planning, optimization, and autonomous decision-making under uncertainty. His work bridges theoretical foundations and practical applications, particularly in the development of algorithms for robotic systems operating in complex, adversarial environments. Notably, his 2019 paper "Informative Path Planning in the Presence of Adversarial Observers" addresses the critical challenge of gathering information using mobile robots while contending with hostile agents, formulating the problem to balance information maximization with risk minimization. Though this specific work has garnered 4 citations, Axehill’s broader impact is reflected in his extensive body of research on mixed-integer programming for trajectory planning and real-time control, which has influenced fields from autonomous driving to aerial robotics. His achievements include advancing the use of optimization techniques for safe and efficient robot navigation, earning recognition for his ability to translate complex mathematical models into deployable solutions. For students and researchers, Axehill’s work exemplifies how rigorous theoretical insights can drive innovation in autonomous systems.