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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Biomimetics |
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| Online Access: | https://www.mdpi.com/2313-7673/7/4/210 |
| _version_ | 1827641636955357184 |
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| author | Guoyu Zuo Mi Li Jianjun Yu Chun Wu Gao Huang |
| author_facet | Guoyu Zuo Mi Li Jianjun Yu Chun Wu Gao Huang |
| author_sort | Guoyu Zuo |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-09T17:17:37Z |
| format | Article |
| id | doaj.art-c7b98760e75f483b89ea23550aeacb26 |
| institution | Directory Open Access Journal |
| issn | 2313-7673 |
| language | English |
| last_indexed | 2024-03-09T17:17:37Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomimetics |
| spelling | doaj.art-c7b98760e75f483b89ea23550aeacb262023-11-24T13:31:30ZengMDPI AGBiomimetics2313-76732022-11-017421010.3390/biomimetics7040210An Efficient Motion Planning Method with a Lazy Demonstration Graph for Repetitive Pick-and-PlaceGuoyu Zuo0Mi Li1Jianjun Yu2Chun Wu3Gao Huang4Faculty of Information Technology, Beijing University of Technology, Beijing 100124, ChinaFaculty of Information Technology, Beijing University of Technology, Beijing 100124, ChinaFaculty of Information Technology, Beijing University of Technology, Beijing 100124, ChinaFaculty of Information Technology, Beijing University of Technology, Beijing 100124, ChinaFaculty of Information Technology, Beijing University of Technology, Beijing 100124, ChinaRobotic 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.https://www.mdpi.com/2313-7673/7/4/210manipulation planningmotion and path planninglearning sampling distributionautonomous robot |
| spellingShingle | Guoyu Zuo Mi Li Jianjun Yu Chun Wu Gao Huang An Efficient Motion Planning Method with a Lazy Demonstration Graph for Repetitive Pick-and-Place Biomimetics manipulation planning motion and path planning learning sampling distribution autonomous robot |
| 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 | manipulation planning motion and path planning learning sampling distribution autonomous robot |
| url | https://www.mdpi.com/2313-7673/7/4/210 |
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