9,480 research outputs found
Motion Planning for Manipulation With Heuristic Search
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
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
Contingent task and motion planning under uncertainty for human–robot interactions
Manipulation planning under incomplete information is a highly challenging task for mobile manipulators. Uncertainty can be resolved by robot perception modules or using human knowledge in the execution process. Human operators can also collaborate with robots for the execution of some difficult actions or as helpers in sharing the task knowledge. In this scope, a contingent-based task and motion planning is proposed taking into account robot uncertainty and human–robot interactions, resulting a tree-shaped set of geometrically feasible plans. Different sorts of geometric reasoning processes are embedded inside the planner to cope with task constraints like detecting occluding objects when a robot needs to grasp an object. The proposal has been evaluated with different challenging scenarios in simulation and a real environment.Postprint (published version
Coordination of several robots based on temporal synchronization
© 2016. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/This paper proposes an approach to deal with the problem of coordinating multi-robot systems, in which each robot executes individually planned tasks in a shared workspace. The approach is a decoupled method that can coordinate the participating robots in on-line mode. The coordination is achieved through the adjustment of the time evolution of each robot along its original planned geometric path according to the movements of the other robots to assure a collision-free execution of their respective tasks. To assess the proposed approach different tests were performed in graphical simulations and real experiments.Postprint (published version
A Certified-Complete Bimanual Manipulation Planner
Planning motions for two robot arms to move an object collaboratively is a
difficult problem, mainly because of the closed-chain constraint, which arises
whenever two robot hands simultaneously grasp a single rigid object. In this
paper, we propose a manipulation planning algorithm to bring an object from an
initial stable placement (position and orientation of the object on the support
surface) towards a goal stable placement. The key specificity of our algorithm
is that it is certified-complete: for a given object and a given environment,
we provide a certificate that the algorithm will find a solution to any
bimanual manipulation query in that environment whenever one exists. Moreover,
the certificate is constructive: at run-time, it can be used to quickly find a
solution to a given query. The algorithm is tested in software and hardware on
a number of large pieces of furniture.Comment: 12 pages, 7 figures, 1 tabl
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