666 research outputs found
Randomized parallel motion planning for robot manipulators
We present a novel approach to parallel motion planning for
robot manipulators in 3D workspaces. The approach is based on a
randomized parallel search algorithm and focuses on solving the
path planning problem for industrial robot arms working in a
reasonably cluttered workspace.The path planning system works in the
discretized configuration space, which needs not to be represented
explicitly. The parallel search is conducted by a number of
rule-based sequential search processes, which work to find a path
connecting the initial configuration to the goal via a number of
randomly generated subgoal configurations. Since the planning
performs only on-line collision tests with proper proximity
information without using pre-computed information, the approach
is suitable for planning problems with multirobot or dynamic
environments.
The implementation has been carried out on the parallel virtual
machine (PVM) of a cluster of SUN4 workstations and SGI machines.
The experimental results have shown that the approach works well
for a 6-dof robot arm in a reasonably cluttered environment, and
that parallel computation increases the efficiency of motion planning significantly
Open World Assistive Grasping Using Laser Selection
Many people with motor disabilities are unable to complete activities of
daily living (ADLs) without assistance. This paper describes a complete robotic
system developed to provide mobile grasping assistance for ADLs. The system is
comprised of a robot arm from a Rethink Robotics Baxter robot mounted to an
assistive mobility device, a control system for that arm, and a user interface
with a variety of access methods for selecting desired objects. The system uses
grasp detection to allow previously unseen objects to be picked up by the
system. The grasp detection algorithms also allow for objects to be grasped in
cluttered environments. We evaluate our system in a number of experiments on a
large variety of objects. Overall, we achieve an object selection success rate
of 88% and a grasp detection success rate of 90% in a non-mobile scenario, and
success rates of 89% and 72% in a mobile scenario
Near-Optimal Motion Planning Algorithms Via A Topological and Geometric Perspective
Motion planning is a fundamental problem in robotics, which involves finding a path for an autonomous system, such as a robot, from a given source to a destination while avoiding collisions with obstacles. The properties of the planning space heavily influence the performance of existing motion planning algorithms, which can pose significant challenges in handling complex regions, such as narrow passages or cluttered environments, even for simple objects. The problem of motion planning becomes deterministic if the details of the space are fully known, which is often difficult to achieve in constantly changing environments. Sampling-based algorithms are widely used among motion planning paradigms because they capture the topology of space into a roadmap. These planners have successfully solved high-dimensional planning problems with a probabilistic-complete guarantee, i.e., it guarantees to find a path if one exists as the number of vertices goes to infinity. Despite their progress, these methods have failed to optimize the sub-region information of the environment for reuse by other planners. This results in re-planning overhead at each execution, affecting the performance complexity for computation time and memory space usage.
In this research, we address the problem by focusing on the theoretical foundation of the algorithmic approach that leverages the strengths of sampling-based motion planners and the Topological Data Analysis methods to extract intricate properties of the environment. The work contributes a novel algorithm to overcome the performance shortcomings of existing motion planners by capturing and preserving the essential topological and geometric features to generate a homotopy-equivalent roadmap of the environment. This roadmap provides a mathematically rich representation of the environment, including an approximate measure of the collision-free space. In addition, the roadmap graph vertices sampled close to the obstacles exhibit advantages when navigating through narrow passages and cluttered environments, making obstacle-avoidance path planning significantly more efficient.
The application of the proposed algorithms solves motion planning problems, such as sub-optimal planning, diverse path planning, and fault-tolerant planning, by demonstrating the improvement in computational performance and path quality. Furthermore, we explore the potential of these algorithms in solving computational biology problems, particularly in finding optimal binding positions for protein-ligand or protein-protein interactions.
Overall, our work contributes a new way to classify routes in higher dimensional space and shows promising results for high-dimensional robots, such as articulated linkage robots. The findings of this research provide a comprehensive solution to motion planning problems and offer a new perspective on solving computational biology problems
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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