24,385 research outputs found
Faster Motion on Cartesian Paths Exploiting Robot Redundancy at the Acceleration Level
The problem of minimizing the transfer time along a given Cartesian path for redundant robots can be approached in two steps, by separating the generation of a joint path associated to the Cartesian path from the exact minimization of motion time under kinematic/dynamic bounds along the obtained parameterized joint path. In this framework, multiple suboptimal solutions can be found, depending on how redundancy is locally resolved in the joint space within the first step. We propose a solution method that works at the acceleration level, by using weighted pseudoinversion, optimizing an inertia-related criterion, and including null-space damping. Several numerical results obtained on different robot systems demonstrate consistently good behaviors and definitely faster motion times in comparison with related methods proposed in the literature. The motion time obtained with our method is reasonably close to the global time-optimal solution along same Cartesian path. Experimental results on a KUKA LWR IV are also reported, showing the tracking control performance on the executed motions
Compliance error compensation technique for parallel robots composed of non-perfect serial chains
The paper presents the compliance errors compensation technique for
over-constrained parallel manipulators under external and internal loadings.
This technique is based on the non-linear stiffness modeling which is able to
take into account the influence of non-perfect geometry of serial chains caused
by manufacturing errors. Within the developed technique, the deviation
compensation reduces to an adjustment of a target trajectory that is modified
in the off-line mode. The advantages and practical significance of the proposed
technique are illustrated by an example that deals with groove milling by the
Orthoglide manipulator that considers different locations of the workpiece. It
is also demonstrated that the impact of the compliance errors and the errors
caused by inaccuracy in serial chains cannot be taken into account using the
superposition principle.Comment: arXiv admin note: text overlap with arXiv:1204.175
A Depth Space Approach for Evaluating Distance to Objects -- with Application to Human-Robot Collision Avoidance
We present a novel approach to estimate the distance between a generic point in the Cartesian space and objects detected with a depth sensor. This information is crucial in many robotic applications, e.g., for collision avoidance, contact point identification, and augmented reality. The key idea is to perform all distance evaluations directly in the depth space. This allows distance estimation by considering also the frustum generated by the pixel on the depth image, which takes into account both the pixel size and the occluded points. Different techniques to aggregate distance data coming from multiple object points are proposed. We compare the Depth space approach with the commonly used Cartesian space or Configuration space approaches, showing that the presented method provides better results and faster execution times. An application to human-robot collision avoidance using a KUKA LWR IV robot and a Microsoft Kinect sensor illustrates the effectiveness of the approach
Stiffness modeling of non-perfect parallel manipulators
The paper focuses on the stiffness modeling of parallel manipulators composed
of non-perfect serial chains, whose geometrical parameters differ from the
nominal ones. In these manipulators, there usually exist essential internal
forces/torques that considerably affect the stiffness properties and also
change the end-effector location. These internal load-ings are caused by
elastic deformations of the manipulator ele-ments during assembling, while the
geometrical errors in the chains are compensated for by applying appropriate
forces. For this type of manipulators, a non-linear stiffness modeling
tech-nique is proposed that allows us to take into account inaccuracy in the
chains and to aggregate their stiffness models for the case of both small and
large deflections. Advantages of the developed technique and its ability to
compute and compensate for the compliance errors caused by different factors
are illustrated by an example that deals with parallel manipulators of the
Or-thoglide famil
Automated sequence and motion planning for robotic spatial extrusion of 3D trusses
While robotic spatial extrusion has demonstrated a new and efficient means to
fabricate 3D truss structures in architectural scale, a major challenge remains
in automatically planning extrusion sequence and robotic motion for trusses
with unconstrained topologies. This paper presents the first attempt in the
field to rigorously formulate the extrusion sequence and motion planning (SAMP)
problem, using a CSP encoding. Furthermore, this research proposes a new
hierarchical planning framework to solve the extrusion SAMP problems that
usually have a long planning horizon and 3D configuration complexity. By
decoupling sequence and motion planning, the planning framework is able to
efficiently solve the extrusion sequence, end-effector poses, joint
configurations, and transition trajectories for spatial trusses with
nonstandard topologies. This paper also presents the first detailed computation
data to reveal the runtime bottleneck on solving SAMP problems, which provides
insight and comparing baseline for future algorithmic development. Together
with the algorithmic results, this paper also presents an open-source and
modularized software implementation called Choreo that is machine-agnostic. To
demonstrate the power of this algorithmic framework, three case studies,
including real fabrication and simulation results, are presented.Comment: 24 pages, 16 figure
Markerless visual servoing on unknown objects for humanoid robot platforms
To precisely reach for an object with a humanoid robot, it is of central
importance to have good knowledge of both end-effector, object pose and shape.
In this work we propose a framework for markerless visual servoing on unknown
objects, which is divided in four main parts: I) a least-squares minimization
problem is formulated to find the volume of the object graspable by the robot's
hand using its stereo vision; II) a recursive Bayesian filtering technique,
based on Sequential Monte Carlo (SMC) filtering, estimates the 6D pose
(position and orientation) of the robot's end-effector without the use of
markers; III) a nonlinear constrained optimization problem is formulated to
compute the desired graspable pose about the object; IV) an image-based visual
servo control commands the robot's end-effector toward the desired pose. We
demonstrate effectiveness and robustness of our approach with extensive
experiments on the iCub humanoid robot platform, achieving real-time
computation, smooth trajectories and sub-pixel precisions
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