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Adaptive Control for Pivoting with Visual and Tactile Feedback
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.ORCID iD: 0000-0002-3653-4691
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.ORCID iD: 0000-0003-2078-8854
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.ORCID iD: 0000-0003-2965-2953
2016 (English)Conference paper, Published paper (Refereed)
Abstract [en]

In this work we present an adaptive control approach for pivoting, which is an in-hand manipulation maneuver that consists of rotating a grasped object to a desired orientation relative to the robot’s hand. We perform pivoting by means of gravity, allowing the object to rotate between the fingers of a one degree of freedom gripper and controlling the gripping force to ensure that the object follows a reference trajectory and arrives at the desired angular position. We use a visual pose estimation system to track the pose of the object and force measurements from tactile sensors to control the gripping force. The adaptive controller employs an update law that accommodates for errors in the friction coefficient,which is one of the most common sources of uncertainty in manipulation. Our experiments confirm that the proposed adaptive controller successfully pivots a grasped object in the presence of uncertainty in the object’s friction parameters.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2016.
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:kth:diva-187483ISI: 000389516200050Scopus ID: 2-s2.0-84977472497OAI: oai:DiVA.org:kth-187483DiVA: diva2:930537
Conference
IEEE International Conference on Robotics and Automation,Stockholm, Sweden 16-21 May 2016
Note

QC 20160524

Available from: 2016-05-24 Created: 2016-05-24 Last updated: 2017-01-19Bibliographically approved
In thesis
1. Robotic Manipulation under Uncertainty and Limited Dexterity
Open this publication in new window or tab >>Robotic Manipulation under Uncertainty and Limited Dexterity
2016 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Robotic manipulators today are mostly constrained to perform fixed, repetitive tasks. Engineers design the robot’s workcell specifically tailoredto the task, minimizing all possible uncertainties such as the location of tools and parts that the robot manipulates. However, autonomous robots must be capable of manipulating novel objects with unknown physical properties such as their inertial parameters, friction and shape. In this thesis we address the problem of uncertainty connected to kinematic constraints and friction forces in several robotic manipulation tasks. We design adaptive controllers for opening one degree of freedom mechanisms, such as doors and drawers, under the presence of uncertainty in the kinematic parameters of the system. Furthermore, we formulate adaptive estimators for determining the location of the contact point between a tool grasped by the robot and the environment in manipulation tasks where the robot needs to exert forces with the tool on another object, as in the case of screwing or drilling. We also propose a learning framework based on Gaussian Process regression and dual arm manipulation to estimate the static friction properties of objects. The second problem we address in this thesis is related to the mechanical simplicity of most robotic grippers available in the market. Their lower cost and higher robustness compared to more mechanically advanced hands make them attractive for industrial and research robots. However, the simple mechanical design restrictsthem from performing in-hand manipulation, i.e. repositioning of objects in the robot’s hand, by using the fingers to push, slide and roll the object. Researchers have proposed thus to use extrinsic dexterity instead, i.e. to exploit resources and features of the environment, such as gravity or inertial forces,  that can help the robot to perform regrasps. Given that the robot must then interact with the environment, the problem of uncertainty becomes highly relevant. We propose controllers for performing pivoting, i.e. reorienting the grasped object in the robot’s hand, using gravity and controlling the friction exerted by the fingertips by varying the grasping force.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2016. 43 p.
Series
TRITA-CSC-A, ISSN 1653-5723 ; 2016:15
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computer Science
Identifiers
urn:nbn:se:kth:diva-187484 (URN)978-91-7729-022-3 (ISBN)
Public defence
2016-06-13, F3, Lindstedtsvägen 26, KTH Campus Valhallavägen, Stockholm, 10:00 (English)
Opponent
Supervisors
Note

QC 20160524

Available from: 2016-05-24 Created: 2016-05-24 Last updated: 2016-05-30Bibliographically approved

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Viña Barrientos, FranciscoSmith, ChristianKragic, Danica

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