8,655 research outputs found
Strengthening Canonical Pattern Databases with Structural Symmetries
Symmetry-based state space pruning techniques have proved to greatly improve heuristic search based classical planners. Similarly, abstraction heuristics in general and pattern databases in particular are key ingredients of such planners. However, only little work has dealt with how the abstraction heuristics behave under symmetries. In this work, we investigate the symmetry properties of the popular canonical pattern databases heuristic. Exploiting structural symmetries, we strengthen the canonical pattern databases by adding symmetric pattern databases, making the resulting heuristic invariant under structural symmetry, thus making it especially attractive for symmetry-based pruning search methods. Further, we prove that this heuristic is at least as informative as using symmetric lookups over the original heuristic. An experimental evaluation confirms these theoretical results
Informed RRT*: Optimal Sampling-based Path Planning Focused via Direct Sampling of an Admissible Ellipsoidal Heuristic
Rapidly-exploring random trees (RRTs) are popular in motion planning because
they find solutions efficiently to single-query problems. Optimal RRTs (RRT*s)
extend RRTs to the problem of finding the optimal solution, but in doing so
asymptotically find the optimal path from the initial state to every state in
the planning domain. This behaviour is not only inefficient but also
inconsistent with their single-query nature.
For problems seeking to minimize path length, the subset of states that can
improve a solution can be described by a prolate hyperspheroid. We show that
unless this subset is sampled directly, the probability of improving a solution
becomes arbitrarily small in large worlds or high state dimensions. In this
paper, we present an exact method to focus the search by directly sampling this
subset.
The advantages of the presented sampling technique are demonstrated with a
new algorithm, Informed RRT*. This method retains the same probabilistic
guarantees on completeness and optimality as RRT* while improving the
convergence rate and final solution quality. We present the algorithm as a
simple modification to RRT* that could be further extended by more advanced
path-planning algorithms. We show experimentally that it outperforms RRT* in
rate of convergence, final solution cost, and ability to find difficult
passages while demonstrating less dependence on the state dimension and range
of the planning problem.Comment: 8 pages, 11 figures. Videos available at
https://www.youtube.com/watch?v=d7dX5MvDYTc and
https://www.youtube.com/watch?v=nsl-5MZfwu
Batch Informed Trees (BIT*): Informed Asymptotically Optimal Anytime Search
Path planning in robotics often requires finding high-quality solutions to
continuously valued and/or high-dimensional problems. These problems are
challenging and most planning algorithms instead solve simplified
approximations. Popular approximations include graphs and random samples, as
respectively used by informed graph-based searches and anytime sampling-based
planners. Informed graph-based searches, such as A*, traditionally use
heuristics to search a priori graphs in order of potential solution quality.
This makes their search efficient but leaves their performance dependent on the
chosen approximation. If its resolution is too low then they may not find a
(suitable) solution but if it is too high then they may take a prohibitively
long time to do so. Anytime sampling-based planners, such as RRT*,
traditionally use random sampling to approximate the problem domain
incrementally. This allows them to increase resolution until a suitable
solution is found but makes their search dependent on the order of
approximation. Arbitrary sequences of random samples approximate the problem
domain in every direction simultaneously and but may be prohibitively
inefficient at containing a solution. This paper unifies and extends these two
approaches to develop Batch Informed Trees (BIT*), an informed, anytime
sampling-based planner. BIT* solves continuous path planning problems
efficiently by using sampling and heuristics to alternately approximate and
search the problem domain. Its search is ordered by potential solution quality,
as in A*, and its approximation improves indefinitely with additional
computational time, as in RRT*. It is shown analytically to be almost-surely
asymptotically optimal and experimentally to outperform existing sampling-based
planners, especially on high-dimensional planning problems.Comment: International Journal of Robotics Research (IJRR). 32 Pages. 16
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Oversubscription Planning as Classical Planning with Multiple Cost Functions
The aim of classical planning is to minimize the summed cost of operators among those plans that achieve a fixed set of goals. Oversubscription planning (OSP), on the other hand, seeks to maximize the utility of the set of facts achieved by a plan, while keeping the cost of the plan at or below some specified bound. Here, we investigate the use of reformulations that yield planning problems with two separate cost functions, but no utilities, for solving OSP tasks. Such reformulations have also been proposed in the context of net-benefit planning, where the planner tries to maximize the difference between the utility achieved and the cost of the plan. One of our reformulations is adapted directly from that setting, while the other is novel. In both cases, they allow for easy adaptation of existing classical planning heuristics to the OSP problem within a simple branch and bound search. We validate our approach using state of the art admissible heuristics in this framework, and report our results
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