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An Efficient Motion Planning Method with a Lazy Demonstration Graph for Repetitive Pick-and-Place
Robotic systems frequently need to plan consecutive similar manipulation in some scenarios (e.g., pick-and-place tasks), leading to similar motion plans. Moreover, the workspace of a robot changes with the difference in operation actions, which affects subsequent tasks. Therefore, it is significant...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9703960/ https://www.ncbi.nlm.nih.gov/pubmed/36412738 http://dx.doi.org/10.3390/biomimetics7040210 |
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author | Zuo, Guoyu Li, Mi Yu, Jianjun Wu, Chun Huang, Gao |
author_facet | Zuo, Guoyu Li, Mi Yu, Jianjun Wu, Chun Huang, Gao |
author_sort | Zuo, Guoyu |
collection | PubMed |
description | Robotic systems frequently need to plan consecutive similar manipulation in some scenarios (e.g., pick-and-place tasks), leading to similar motion plans. Moreover, the workspace of a robot changes with the difference in operation actions, which affects subsequent tasks. Therefore, it is significant to reuse information from previous solutions for new motion planning instances to adapt to workplace changes. This paper proposes the Lazy Demonstration Graph (LDG) planner, a novel motion planner that exploits successful and high-quality planning cases as prior knowledge. In addition, a Gaussian Mixture Model (GMM) is established by learning the distribution of samples in the planning cases. Through the trained GMM, more samples are placed in a promising location related to the planning tasks for achieving the purpose of adaptive sampling. This adaptive sampling strategy is combined with the Lazy Probabilistic Roadmap (LazyPRM) algorithm; in the subsequent planning tasks, this paper uses the multi-query property of a road map to solve motion planning problems without planning from scratch. The lazy collision detection of the LazyPRM algorithm helps overcome changes in the workplace by searching candidate paths. Our method also improves the quality and success rate of the path planning of LazyPRM. Compared with other state-of-the-art motion planning algorithms, our method achieved better performance in the planning time and path quality. In the repetitive motion planning experiment of the manipulator for pick-and-place tasks, we designed two different experimental scenarios in the simulation environment. The physical experiments are also carried out in AUBO−i5 robot arm. Accordingly, the experimental results verified our method’s validity and robustness. |
format | Online Article Text |
id | pubmed-9703960 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-97039602022-11-29 An Efficient Motion Planning Method with a Lazy Demonstration Graph for Repetitive Pick-and-Place Zuo, Guoyu Li, Mi Yu, Jianjun Wu, Chun Huang, Gao Biomimetics (Basel) Article Robotic systems frequently need to plan consecutive similar manipulation in some scenarios (e.g., pick-and-place tasks), leading to similar motion plans. Moreover, the workspace of a robot changes with the difference in operation actions, which affects subsequent tasks. Therefore, it is significant to reuse information from previous solutions for new motion planning instances to adapt to workplace changes. This paper proposes the Lazy Demonstration Graph (LDG) planner, a novel motion planner that exploits successful and high-quality planning cases as prior knowledge. In addition, a Gaussian Mixture Model (GMM) is established by learning the distribution of samples in the planning cases. Through the trained GMM, more samples are placed in a promising location related to the planning tasks for achieving the purpose of adaptive sampling. This adaptive sampling strategy is combined with the Lazy Probabilistic Roadmap (LazyPRM) algorithm; in the subsequent planning tasks, this paper uses the multi-query property of a road map to solve motion planning problems without planning from scratch. The lazy collision detection of the LazyPRM algorithm helps overcome changes in the workplace by searching candidate paths. Our method also improves the quality and success rate of the path planning of LazyPRM. Compared with other state-of-the-art motion planning algorithms, our method achieved better performance in the planning time and path quality. In the repetitive motion planning experiment of the manipulator for pick-and-place tasks, we designed two different experimental scenarios in the simulation environment. The physical experiments are also carried out in AUBO−i5 robot arm. Accordingly, the experimental results verified our method’s validity and robustness. MDPI 2022-11-21 /pmc/articles/PMC9703960/ /pubmed/36412738 http://dx.doi.org/10.3390/biomimetics7040210 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Zuo, Guoyu Li, Mi Yu, Jianjun Wu, Chun Huang, Gao An Efficient Motion Planning Method with a Lazy Demonstration Graph for Repetitive Pick-and-Place |
title | An Efficient Motion Planning Method with a Lazy Demonstration Graph for Repetitive Pick-and-Place |
title_full | An Efficient Motion Planning Method with a Lazy Demonstration Graph for Repetitive Pick-and-Place |
title_fullStr | An Efficient Motion Planning Method with a Lazy Demonstration Graph for Repetitive Pick-and-Place |
title_full_unstemmed | An Efficient Motion Planning Method with a Lazy Demonstration Graph for Repetitive Pick-and-Place |
title_short | An Efficient Motion Planning Method with a Lazy Demonstration Graph for Repetitive Pick-and-Place |
title_sort | efficient motion planning method with a lazy demonstration graph for repetitive pick-and-place |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9703960/ https://www.ncbi.nlm.nih.gov/pubmed/36412738 http://dx.doi.org/10.3390/biomimetics7040210 |
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