Alban Grastien

Papers

Total Citations

465

H-Index

About

Alban Grastien is a leading researcher in artificial intelligence, with a primary focus on pathfinding algorithms and the ethical dimensions of human-robot interaction. His most influential work centers on developing efficient, optimal pathfinding techniques for grid-based environments—a core challenge in robotics and video games. His 2011 paper, "Online Graph Pruning for Pathfinding On Grid Maps," has amassed 376 citations and revolutionized the field by introducing a method that dramatically accelerates hierarchical pathfinding without sacrificing optimality. Grastien further advanced the state of the art with his contributions to any-angle pathfinding, notably through his 2013 and 2016 papers, which established new benchmarks for computing truly shortest Euclidean paths on grids, achieving both optimality and practical runtime efficiency. More recently, he has expanded into the socially critical area of robot ethics, exploring how to reduce moral ambiguity in partially observed human-robot interactions. His work bridges rigorous algorithmic theory with real-world deployment concerns, making him a pivotal figure whose research shapes both the technical foundations of autonomous navigation and the responsible design of interactive AI systems.