Path Quality Improvement of Sampling-Based Planners: An Efficient Optimization-Based Approach Using Analytical Gradients

Jonas Wittmann, Corina Klein, Daniel Rixen

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

3 Scopus citations

Abstract

Modern collaborative robotic applications require feasible robot motions that are predictable for human coworkers. We propose two path post-processing modules based on nonlinear optimization to improve path quality: the Path Length Post-Processor (PLPP) minimizes the path length; the Path Smoothness Post-Processor (PSPP) improves the path smoothness. Both keep a clearance to obstacles. We derive the analytical gradients of the optimization problem for computational efficiency and we validate our approaches in simulation.

Original languageEnglish
Title of host publicationCISM International Centre for Mechanical Sciences, Courses and Lectures
PublisherSpringer Science and Business Media Deutschland GmbH
Pages182-190
Number of pages9
DOIs
StatePublished - 2022

Publication series

NameCISM International Centre for Mechanical Sciences, Courses and Lectures
Volume606
ISSN (Print)0254-1971
ISSN (Electronic)2309-3706

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