About

Dan Halperin is a prominent computational geometry and robotics researcher whose work has fundamentally shaped our understanding of motion planning, particularly in multi-robot systems. Based at Tel Aviv University, Halperin has built a distinguished career bridging theoretical algorithmic foundations with practical robotics applications. His most influential contributions center on multi-robot motion planning, where he and his collaborators developed the discrete-RRT (dRRT) framework — a sampling-based approach for navigating implicit roadmaps in complex multi-robot environments — accumulating over 200 citations across related publications. Halperin has made significant theoretical advances in understanding the complexity of robot motion, including foundational work on free-space complexity amidst fat obstacles and the hardness of unlabeled multi-robot planning, where robots are interchangeable and need not follow assigned targets. His visibility-Voronoi complex work (109 citations) opened new geometric tools for robot navigation, while his research on optimality-guaranteed path planning for unlabeled disc robots demonstrates his commitment to both rigor and practicality. His early involvement in the Algorithmic Foundations of Robotics workshop (1995) reflects his lasting influence on shaping the research community itself. Collectively, Halperin's portfolio represents essential reading for anyone studying robot motion planning or computational geometry.

Research Focus

Key Achievements

22
H-Index
73
Papers
1,555
Total Citations
21
Avg Citations/Paper
🏆 Most Cited Paper
Finding a Needle in an Exponential Haystack: Discrete RRT for Exploration of Implicit Roadmaps in Multi-robot Motion Planning
118 citations · 2015
📈 Most Prolific Year: 2015 (9 Papers)
🤝 Key Collaborators: 80
🏛 Institutions: Tel Aviv University, Stanford University, Robotics Research (United States), Massachusetts Institute of Technology, Technical University of Munich

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 34 days ago