2 research outputs found
Analysis of Motion Planning by Sampling in Subspaces of Progressively Increasing Dimension
Despite the performance advantages of modern sampling-based motion planners,
solving high dimensional planning problems in near real-time remains a
challenge. Applications include hyper-redundant manipulators, snake-like and
humanoid robots. Based on the intuition that many of these problem instances do
not require the robots to exercise every degree of freedom independently, we
introduce an enhancement to popular sampling-based planning algorithms aimed at
circumventing the exponential dependence on dimensionality. We propose
beginning the search in a lower dimensional subspace of the configuration space
in the hopes that a simple solution will be found quickly. After a certain
number of samples are generated, if no solution is found, we increase the
dimension of the search subspace by one and continue sampling in the higher
dimensional subspace. In the worst case, the search subspace expands to include
the full configuration space - making the completeness properties identical to
the underlying sampling-based planer. Our experiments comparing the enhanced
and traditional version of RRT, RRT-Connect, and BidirectionalT-RRT on both a
planar hyper-redundant manipulator and the Baxter humanoid robot indicate that
a solution is typically found much faster using this approach and the run time
appears to be less sensitive to the dimension of the full configuration space.
We explore important implementation issues in the sampling process and discuss
its limitations.Comment: 8 pages, 11 figures. arXiv admin note: substantial text overlap with
arXiv:1612.0733
Dynamically Efficient Kinematics for Hyper-Redundant Manipulators
A hyper-redundant robotic arm is a manipulator with many degrees of freedom,
capable of executing tasks in cluttered environments where robotic arms with
fewer degrees of freedom are unable to operate. This paper introduces a new
method for modeling those manipulators in a completely dynamic way. The
proposed method enables online changes of the kinematic structure with the use
of a special function; termed "meta-controlling function". This function can be
used to develop policies to reduce drastically the computational cost for a
single task, and to robustly control the robotic arm, even in the event of
partial damage. The direct and inverse kinematics are solved for a generic
three-dimensional articulated hyper-redundant arm, that can be used as a proof
of concept for more specific structures. To demonstrate the robustness of our
method, experimental simulation results, for a basic "meta-controlling"
function, are presented.Comment: Published in the 24th Mediterranean Conference on Control and
Automation (MED-2016