Date of Award


Degree Type


Degree Name

Doctor of Philosophy (PhD)

Graduate Group

Computer and Information Science

First Advisor

Maxim Likhachev


Heuristic searches such as A* search are a popular means of finding least-cost

plans due to their generality, strong theoretical guarantees on completeness

and optimality, simplicity in implementation, and consistent behavior. In

planning for robotic manipulation, however, these techniques are commonly

thought of as impractical due to the high-dimensionality of the planning

problem. As part of this thesis work, we have developed a heuristic

search-based approach to motion planning for manipulation that does deal

effectively with the high-dimensionality of the problem. In this thesis,

I will present the approach together with its theoretical properties and show

how to apply it to single-arm and dual-arm motion planning with upright

constraints on a PR2 robot operating in non-trivial cluttered spaces. Then

I will explain how we extended our approach to manipulation planning for

n-arms with regrasping. In this work, the planner itself makes all of the

discrete decisions, including which arm to use for the pickup and putdown, whether

handoffs are necessary and how the object should be grasped at each step along

the way.

An extensive experimental analysis in both simulation and on a physical PR2

shows that, in terms of runtime, our approach is on par with some of the most

common sampling-based approaches. This includes benchmarking our planning

framework on two domains that we constructed that are common to manufacturing:

pick-and-place of fast moving objects and the autonomous assembly of small

objects. Between these applications, the planner exhibited fast planning times

and the ability to robustly plan paths into and out of tight working

environments that are common to assembly. The closing work of this thesis

includes an exhaustive study of the natural tradeoff that occurs between

planning efficiency versus solution quality for different values of the

heuristic inflation factor. A comparison of the solution quality of our planner

to paths computed by an asymptotically optimal approach given a great deal of

time for path optimization is included as well. Finally, a set of experimental

results are included that show that due to our approach's deterministic

cost-minimization, similar input tends to lead to similarity in the output. This

kind of local consistency is important to the predictability of the robot's

motions and contributes to human-robot safety.

Available for download on Wednesday, July 25, 2018

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