6,635 research outputs found
Better parameter-free anytime search by minimizing time between solutions
This paper presents a new anytime search algorithm, any- time explicit estimation search (AEES). AEES is an anytime search algorithm which attempts to minimize the time between improvements to its incumbent solution by taking advantage of the differences between solution cost and length. We provide an argument that minimizing the time between solutions is the right thing to do for an anytime search algorithm and show that when actions have differing costs, many state-of-the-art search algorithms, including the search strategy of LAMA11 and anytime nonparametric A*, do not minimize the time between solutions. An empirical evaluation on seven domains shows that AEES often has both the shortest time between incumbent solutions and the best solution in hand for a wide variety of cutoffs
Batch Informed Trees (BIT*): Sampling-based Optimal Planning via the Heuristically Guided Search of Implicit Random Geometric Graphs
In this paper, we present Batch Informed Trees (BIT*), a planning algorithm
based on unifying graph- and sampling-based planning techniques. By recognizing
that a set of samples describes an implicit random geometric graph (RGG), we
are able to combine the efficient ordered nature of graph-based techniques,
such as A*, with the anytime scalability of sampling-based algorithms, such as
Rapidly-exploring Random Trees (RRT).
BIT* uses a heuristic to efficiently search a series of increasingly dense
implicit RGGs while reusing previous information. It can be viewed as an
extension of incremental graph-search techniques, such as Lifelong Planning A*
(LPA*), to continuous problem domains as well as a generalization of existing
sampling-based optimal planners. It is shown that it is probabilistically
complete and asymptotically optimal.
We demonstrate the utility of BIT* on simulated random worlds in
and and manipulation problems on CMU's HERB, a
14-DOF two-armed robot. On these problems, BIT* finds better solutions faster
than RRT, RRT*, Informed RRT*, and Fast Marching Trees (FMT*) with faster
anytime convergence towards the optimum, especially in high dimensions.Comment: 8 Pages. 6 Figures. Video available at
http://www.youtube.com/watch?v=TQIoCC48gp
Variable Annealing Length and Parallelism in Simulated Annealing
In this paper, we propose: (a) a restart schedule for an adaptive simulated
annealer, and (b) parallel simulated annealing, with an adaptive and
parameter-free annealing schedule. The foundation of our approach is the
Modified Lam annealing schedule, which adaptively controls the temperature
parameter to track a theoretically ideal rate of acceptance of neighboring
states. A sequential implementation of Modified Lam simulated annealing is
almost parameter-free. However, it requires prior knowledge of the annealing
length. We eliminate this parameter using restarts, with an exponentially
increasing schedule of annealing lengths. We then extend this restart schedule
to parallel implementation, executing several Modified Lam simulated annealers
in parallel, with varying initial annealing lengths, and our proposed parallel
annealing length schedule. To validate our approach, we conduct experiments on
an NP-Hard scheduling problem with sequence-dependent setup constraints. We
compare our approach to fixed length restarts, both sequentially and in
parallel. Our results show that our approach can achieve substantial
performance gains, throughout the course of the run, demonstrating our approach
to be an effective anytime algorithm.Comment: Tenth International Symposium on Combinatorial Search, pages 2-10.
June 201
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
Figure
Rescheduling with iterative repair
This paper presents a new approach to rescheduling called constraint-based iterative repair. This approach gives our system the ability to satisfy domain constraints, address optimization concerns, minimize perturbation to the original schedule, produce modified schedules, quickly, and exhibits 'anytime' behavior. The system begins with an initial, flawed schedule and then iteratively repairs constraint violations until a conflict-free schedule is produced. In an empirical demonstration, we vary the importance of minimizing perturbation and report how fast the system is able to resolve conflicts in a given time bound. We also show the anytime characteristics of the system. These experiments were performed within the domain of Space Shuttle ground processing
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