1,013 research outputs found
Direct visual servoing with respect to rigid objects
Existing visual servoing techniques which do not need metric information require, on the other hand, prior knowledge about the object's shape and/or the camera's motion. In this paper, we propose a new visual servoing technique which does not require any of them. The method is direct in the sense that: the intensity value of all pixels is used (i.e. we avoid the feature extraction step which introduces errors); and that the proposed control error as well as the control law are fully based on image data (i.e. metric measures are neither required nor estimated). Besides not relying on prior information, the scheme is robust to large errors in the camera's internal parameters. We provide the theoretical proofs that the proposed task function is locally isomorphic to the camera pose, that the approach is motion- and shape-independent, and also that the derived control law ensures local asymptotic stability. Furthermore, the proposed control error allows for simple, smooth, physically valid, singularity-free path planning, which leads to a large domain of convergence for the servoing. The approach is validated through various results using objects of different shapes, large initial displacements as well as large errors in the camera's internal parameters
Image based visual servoing using algebraic curves applied to shape alignment
Visual servoing schemes generally employ various image features (points, lines, moments etc.) in their control formulation. This paper presents a novel method for using boundary information in visual servoing. Object boundaries are
modeled by algebraic equations and decomposed as a unique sum of product of lines. We propose that these lines can be used to extract useful features for visual servoing purposes. In this paper, intersection of these lines are used as point features in visual servoing. Simulations are performed with a 6 DOF Puma
560 robot using Matlab Robotics Toolbox for the alignment of a free-form object. Also, experiments are realized with a 2 DOF SCARA direct drive robot. Both simulation and experimental results are quite promising and show potential of our new method
Manipulating Highly Deformable Materials Using a Visual Feedback Dictionary
The complex physical properties of highly deformable materials such as
clothes pose significant challenges fanipulation systems. We present a novel
visual feedback dictionary-based method for manipulating defoor autonomous
robotic mrmable objects towards a desired configuration. Our approach is based
on visual servoing and we use an efficient technique to extract key features
from the RGB sensor stream in the form of a histogram of deformable model
features. These histogram features serve as high-level representations of the
state of the deformable material. Next, we collect manipulation data and use a
visual feedback dictionary that maps the velocity in the high-dimensional
feature space to the velocity of the robotic end-effectors for manipulation. We
have evaluated our approach on a set of complex manipulation tasks and
human-robot manipulation tasks on different cloth pieces with varying material
characteristics.Comment: The video is available at goo.gl/mDSC4
Aerial-Ground collaborative sensing: Third-Person view for teleoperation
Rapid deployment and operation are key requirements in time critical
application, such as Search and Rescue (SaR). Efficiently teleoperated ground
robots can support first-responders in such situations. However, first-person
view teleoperation is sub-optimal in difficult terrains, while a third-person
perspective can drastically increase teleoperation performance. Here, we
propose a Micro Aerial Vehicle (MAV)-based system that can autonomously provide
third-person perspective to ground robots. While our approach is based on local
visual servoing, it further leverages the global localization of several ground
robots to seamlessly transfer between these ground robots in GPS-denied
environments. Therewith one MAV can support multiple ground robots on a demand
basis. Furthermore, our system enables different visual detection regimes, and
enhanced operability, and return-home functionality. We evaluate our system in
real-world SaR scenarios.Comment: Accepted for publication in 2018 IEEE International Symposium on
Safety, Security and Rescue Robotics (SSRR
Hybrid visual servoing with hierarchical task composition for aerial manipulation
© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.In this paper a hybrid visual servoing with a hierarchical task-composition control framework is described for aerial manipulation, i.e. for the control of an aerial vehicle endowed with a robot arm. The proposed approach suitably combines into a unique hybrid-control framework the main benefits of both image-based and position-based control schemes. Moreover, the underactuation of the aerial vehicle has been explicitly taken into account in a general formulation, together with a dynamic smooth activation mechanism. Both simulation case studies and experiments are presented to demonstrate the performance of the proposed technique.Peer ReviewedPostprint (author's final draft
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