3,898 research outputs found
A Single-Query Manipulation Planner
In manipulation tasks, a robot interacts with movable object(s). The
configuration space in manipulation planning is thus the Cartesian product of
the configuration space of the robot with those of the movable objects. It is
the complex structure of such a "Composite Configuration Space" that makes
manipulation planning particularly challenging. Previous works approximate the
connectivity of the Composite Configuration Space by means of discretization or
by creating random roadmaps. Such approaches involve an extensive
pre-processing phase, which furthermore has to be re-done each time the
environment changes. In this paper, we propose a high-level Grasp-Placement
Table similar to that proposed by Tournassoud et al. (1987), but which does not
require any discretization or heavy pre-processing. The table captures the
potential connectivity of the Composite Configuration Space while being
specific only to the movable object: in particular, it does not require to be
re-computed when the environment changes. During the query phase, the table is
used to guide a tree-based planner that explores the space systematically. Our
simulations and experiments show that the proposed method enables improvements
in both running time and trajectory quality as compared to existing approaches.Comment: 8 pages, 7 figures, 1 tabl
Error Analysis and Correction for Weighted A*'s Suboptimality (Extended Version)
Weighted A* (wA*) is a widely used algorithm for rapidly, but suboptimally,
solving planning and search problems. The cost of the solution it produces is
guaranteed to be at most W times the optimal solution cost, where W is the
weight wA* uses in prioritizing open nodes. W is therefore a suboptimality
bound for the solution produced by wA*. There is broad consensus that this
bound is not very accurate, that the actual suboptimality of wA*'s solution is
often much less than W times optimal. However, there is very little published
evidence supporting that view, and no existing explanation of why W is a poor
bound. This paper fills in these gaps in the literature. We begin with a
large-scale experiment demonstrating that, across a wide variety of domains and
heuristics for those domains, W is indeed very often far from the true
suboptimality of wA*'s solution. We then analytically identify the potential
sources of error. Finally, we present a practical method for correcting for two
of these sources of error and experimentally show that the correction
frequently eliminates much of the error.Comment: Published as a short paper in the 12th Annual Symposium on
Combinatorial Search, SoCS 201
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