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\u27s 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\u27s
motions and contributes to human-robot safety
