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Autonomous Robots

, Volume 43, Issue 2, pp 469–483 | Cite as

Relaxed-rigidity constraints: kinematic trajectory optimization and collision avoidance for in-grasp manipulation

  • Balakumar SundaralingamEmail author
  • Tucker Hermans
Article
  • 288 Downloads
Part of the following topical collections:
  1. Special Issue on Robotics: Science and Systems

Abstract

This paper proposes a novel approach to performing in-grasp manipulation: the problem of moving an object with reference to the palm from an initial pose to a goal pose without breaking or making contacts. Our method to perform in-grasp manipulation uses kinematic trajectory optimization which requires no knowledge of dynamic properties of the object. We implement our approach on an Allegro robot hand and perform thorough experiments on ten objects from the YCB dataset. The proposed method is general enough to generate motions for most objects the robot can grasp. Experimental results support the feasibillty of its application across a variety of object shapes. We explore the adaptability of our approach to additional task requirements by including collision avoidance and joint space smoothness costs. The grasped object avoids collisions with the environment by the use of a signed distance cost function. We reduce the effects of unmodeled object dynamics by requiring smooth joint trajectories. We additionally compensate for errors encountered during trajectory execution by formulating an object pose feedback controller.

Keywords

Dexterous manipulation Trajectory optimization Motion planning 

Notes

Funding

This study was funded partly by National Science Foundation (NSF) (Grant Number 1657596).

Compliance with ethical standards

Conflict of interest

Author Balakumar Sundaralingam has no conflicts of interest. Author Tucker Hermans has no conflicts of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.University of Utah Robotics Center and the School of ComputingUniversity of UtahSalt Lake CityUSA

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