Papers
10
Total Citations
276
H-Index
9
About
Andrew Dobson is a leading researcher in robot motion planning and manipulation, with a focus on developing algorithms that are both theoretically sound and practically deployable. His core contributions lie in asymptotically optimal motion planning, where his work on sparse roadmap spanners (122 citations) provides a memory-efficient alternative to PRM* that still guarantees near-optimal paths. This breakthrough is critical for resource-constrained robots and real-time applications. Dobson has also made significant advances in rearrangement planning, using pebble graphs to efficiently compute manipulation paths for cluttered environments—a problem with combinatorial complexity that is central to warehouse automation. His research on cloud automation (34 citations) further bridges theory and practice by precomputing roadmaps for flexible industrial manipulators. Beyond planning, he has contributed to multi-agent coherence under velocity obstacles and empirically evaluated end-effector modalities for warehouse picking. Dobson is also the architect of PRACSYS, an extensible software framework for composing motion controllers and planners that has supported multiple follow-on studies. With over 275 total citations, his work continues to shape scalable, asymptotically optimal solutions for single- and multi-robot systems.
Research Focus
Key Achievements
Top Papers
- 1Sparse roadmap spanners for asymptotically near-optimal motion planning122 citations · 2014
- 2Rearranging similar objects with a manipulator using pebble graphs38 citations · 2014
- 3Cloud Automation: Precomputing Roadmaps for Flexible Manipulation34 citations · 2015
- 4Maintaining team coherence under the velocity obstacle framework17 citations · 2012
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- 7An Extensible Software Architecture for Composing Motion and Task Planners11 citations · 2014
- 8
- 9Scalable asymptotically-optimal multi-robot motion planning10 citations · 2017
- 10Similar Part Rearrangement With Pebble Graphs3 citations · 2014