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On screw linear interpolation for point-to-point path planning

  • Stony Brook University

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

27 Scopus citations

Abstract

Robot motion is controlled in the joint space whereas the robots have to perform tasks in their task space. Many tasks like carrying a glass of liquid, pouring liquid, opening a drawer requires constraints on the end-effector during the motion. The forward and inverse kinematic mappings between joint space and task space are highly nonlinear and multi-valued (for IK). Consequently, modeling task space constraints like keeping the orientation of the end-effector fixed while changing its position (which is required for carrying a cup of liquid without dropping it) is quite complex in the joint space. In this paper, we show that the use of screw linear interpolation to plan motions in the task space combined with resolved motion rate control to compute the corresponding joint space path, allows one to satisfy many common task space motion constraints in motion planning, without explicitly modeling them. In particular, any motion constraint that forms a subgroup of the group of rigid body motions can be incorporated in our planning scheme, without explicit modeling. We present simulation and experimental results on Baxter robot for different tasks with task space constraints that demonstrates the usefulness of our approach.

Original languageEnglish
Title of host publication2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages9480-9487
Number of pages8
ISBN (Electronic)9781728162126
DOIs
StatePublished - Oct 24 2020
Event2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020 - Las Vegas, United States
Duration: Oct 24 2020Jan 24 2021

Publication series

NameIEEE International Conference on Intelligent Robots and Systems

Conference

Conference2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020
Country/TerritoryUnited States
CityLas Vegas
Period10/24/2001/24/21

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