Papers
17
Total Citations
346
H-Index
6
About
Andreas Orthey is a leading robotics researcher whose work centers on sampling-based motion planning, multilevel abstractions, and task-and-motion planning (TAMP) for complex, high-dimensional systems. His most impactful contribution is the comprehensive comparative review of sampling-based motion planning algorithms (2023, 100 citations), which has become an essential reference for the robotics community. Orthey’s research excels at tackling the curse of dimensionality: he introduced quotient-space motion planning (2018) and a fiber bundle formulation for multilevel motion planning (2023), enabling faster solutions to high-dimensional problems by nesting lower-dimensional abstractions. He also developed ST-RRT* (2022, 29 citations), an asymptotically-optimal, bidirectional planner for space-time with dynamic obstacles. In multi-robot systems, his work on long-horizon rearrangement planning for construction assembly (2022, 86 citations) addresses the intersection of task planning and motion generation. To advance reproducible research, Orthey created MotionBenchMaker (2021, 51 citations), a standardized tool for generating and benchmarking motion planning datasets. With over 300 total citations, his work bridges theoretical foundations and practical tools, making him a key figure in modern motion planning.
Research Focus
Key Achievements
Top Papers
- 1Sampling-Based Motion Planning: A Comparative Review100 citations · 2023
- 2Long-Horizon Multi-Robot Rearrangement Planning for Construction Assembly86 citations · 2022
- 3MotionBenchMaker: A Tool to Generate and Benchmark Motion Planning Datasets51 citations · 2021
- 4
- 5Quotient-Space Motion Planning20 citations · 2018
- 6Multilevel motion planning: A fiber bundle formulation19 citations · 2023
- 7Motion planning in Irreducible Path Spaces6 citations · 2018
- 8Motion planning and irreducible trajectories5 citations · 2015
- 9
- 10Optimizing motion primitives to make symbolic models more predictive5 citations · 2013