{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T05:24:43Z","timestamp":1761110683494,"version":"build-2065373602"},"reference-count":32,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2022,11,21]],"date-time":"2022-11-21T00:00:00Z","timestamp":1668988800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Nature Science Foundation of China","award":["61873008","62103053","2018YFB1307004"],"award-info":[{"award-number":["61873008","62103053","2018YFB1307004"]}]},{"name":"National Key Research and Development Program of China","award":["61873008","62103053","2018YFB1307004"],"award-info":[{"award-number":["61873008","62103053","2018YFB1307004"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Biomimetics"],"abstract":"<jats:p>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\u2212i5 robot arm. Accordingly, the experimental results verified our method\u2019s validity and robustness.<\/jats:p>","DOI":"10.3390\/biomimetics7040210","type":"journal-article","created":{"date-parts":[[2022,11,22]],"date-time":"2022-11-22T03:04:20Z","timestamp":1669086260000},"page":"210","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["An Efficient Motion Planning Method with a Lazy Demonstration Graph for Repetitive Pick-and-Place"],"prefix":"10.3390","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7624-4728","authenticated-orcid":false,"given":"Guoyu","family":"Zuo","sequence":"first","affiliation":[{"name":"Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China"},{"name":"Beijing Key Laboratory of Computing Intelligence and Intelligent Systems, Beijing 100124, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mi","family":"Li","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China"},{"name":"Beijing Key Laboratory of Computing Intelligence and Intelligent Systems, Beijing 100124, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianjun","family":"Yu","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China"},{"name":"Beijing Key Laboratory of Computing Intelligence and Intelligent Systems, Beijing 100124, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chun","family":"Wu","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China"},{"name":"Beijing Key Laboratory of Computing Intelligence and Intelligent Systems, Beijing 100124, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0003-0184","authenticated-orcid":false,"given":"Gao","family":"Huang","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China"},{"name":"Beijing Key Laboratory of Computing Intelligence and Intelligent Systems, Beijing 100124, China"},{"name":"Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1109\/TASE.2020.3020050","article-title":"Manipulator motion planning for part pickup and transport operations from a moving base","volume":"19","author":"Thakar","year":"2020","journal-title":"IEEE Trans. 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