6,576 research outputs found
Survey on model-based manipulation planning of deformable objects
A systematic overview on the subject of model-based manipulation planning of deformable objects is presented. Existing modelling techniques of volumetric, planar and linear deformable objects are described, emphasizing the different types of deformation. Planning strategies are categorized according to the type of manipulation goal: path planning, folding/unfolding, topology modifications and assembly. Most current contributions fit naturally into these categories, and thus the presented algorithms constitute an adequate basis for future developments.Preprin
A Coarse-to-Fine Framework for Dual-Arm Manipulation of Deformable Linear Objects with Whole-Body Obstacle Avoidance
Manipulating deformable linear objects (DLOs) to achieve desired shapes in
constrained environments with obstacles is a meaningful but challenging tasks.
Global planning is necessary for such a highly-constrained task; however,
accurate models of DLOs required by planners are difficult to obtain owing to
their deformable nature, and the inevitable modeling errors significantly
affect the planning results, probably resulting in task failure if the robot
simply executes the planned path in an open-loop manner. In this paper, we
propose a coarse-to-fine framework to combine global planning and local control
for dual-arm manipulation of DLOs, capable of precisely achieving desired
configurations and avoiding potential collisions between the DLO, robot, and
obstacles. Specifically, the global planner refers to a simple yet effective
DLO energy model and computes a coarse path to guarantee the feasibility of the
task; then the local controller follows that path as guidance and further
shapes it with closed-loop feedback to compensate for the planning errors and
guarantee the accuracy of the task. Both simulations and real-world experiments
demonstrate that our framework can robustly achieve desired DLO configurations
in constrained environments with imprecise DLO models. which may not be
reliably achieved by only planning or control
Learning to Navigate Cloth using Haptics
We present a controller that allows an arm-like manipulator to navigate
deformable cloth garments in simulation through the use of haptic information.
The main challenge of such a controller is to avoid getting tangled in, tearing
or punching through the deforming cloth. Our controller aggregates force
information from a number of haptic-sensing spheres all along the manipulator
for guidance. Based on haptic forces, each individual sphere updates its target
location, and the conflicts that arise between this set of desired positions is
resolved by solving an inverse kinematic problem with constraints.
Reinforcement learning is used to train the controller for a single
haptic-sensing sphere, where a training run is terminated (and thus penalized)
when large forces are detected due to contact between the sphere and a
simplified model of the cloth. In simulation, we demonstrate successful
navigation of a robotic arm through a variety of garments, including an
isolated sleeve, a jacket, a shirt, and shorts. Our controller out-performs two
baseline controllers: one without haptics and another that was trained based on
large forces between the sphere and cloth, but without early termination.Comment: Supplementary video available at https://youtu.be/iHqwZPKVd4A.
Related publications http://www.cc.gatech.edu/~karenliu/Robotic_dressing.htm
Real-time Error Control for Surgical Simulation
Objective: To present the first real-time a posteriori error-driven adaptive
finite element approach for real-time simulation and to demonstrate the method
on a needle insertion problem. Methods: We use corotational elasticity and a
frictional needle/tissue interaction model. The problem is solved using finite
elements within SOFA. The refinement strategy relies upon a hexahedron-based
finite element method, combined with a posteriori error estimation driven local
-refinement, for simulating soft tissue deformation. Results: We control the
local and global error level in the mechanical fields (e.g. displacement or
stresses) during the simulation. We show the convergence of the algorithm on
academic examples, and demonstrate its practical usability on a percutaneous
procedure involving needle insertion in a liver. For the latter case, we
compare the force displacement curves obtained from the proposed adaptive
algorithm with that obtained from a uniform refinement approach. Conclusions:
Error control guarantees that a tolerable error level is not exceeded during
the simulations. Local mesh refinement accelerates simulations. Significance:
Our work provides a first step to discriminate between discretization error and
modeling error by providing a robust quantification of discretization error
during simulations.Comment: 12 pages, 16 figures, change of the title, submitted to IEEE TBM
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