93 citations · 156 across the 4 of their papers we have counts for
5 papers
Manipulating Deformable Objects by Interleaving Prediction, Planning, and Control
Dale McConachie, Andrew Dobson, Mengyao Ruan +1
We present a framework for deformable object manipulation that interleaves planning and control, enabling complex manipulation tasks without relying on high-fidelity modeling or si…
dRRT*: Scalable and Informed Asymptotically-Optimal Multi-Robot Motion Planning
Rahul Shome, Kiril Solovey, Andrew Dobson +2
Many exciting robotic applications require multiple robots with many degrees of freedom, such as manipulators, to coordinate their motion in a shared workspace. Discovering high-qu…
Scalable Asymptotically-Optimal Multi-Robot Motion Planning
Andrew Dobson, Kiril Solovey, Rahul Shome +2
Finding asymptotically-optimal paths in multi-robot motion planning problems could be achieved, in principle, using sampling-based planners in the composite configuration space of…
Similar Part Rearrangement With Pebble Graphs
Athanasios Krontiris, Rahul Shome, Andrew Dobson +3
This work proposes a method for effectively computing manipulation paths to rearrange similar objects in a cluttered space. The solution can be used to place similar products in a…
Sampling-based Roadmap Planners are Probably Near-Optimal after Finite Computation
Andrew Dobson, George V. Moustakides, Kostas E. Bekris
Sampling-based motion planners have proven to be efficient solutions to a variety of high-dimensional, geometrically complex motion planning problems with applications in several d…