{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T00:18:03Z","timestamp":1775261883509,"version":"3.50.1"},"reference-count":31,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2018,11,1]],"date-time":"2018-11-01T00:00:00Z","timestamp":1541030400000},"content-version":"vor","delay-in-days":304,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U1713222"],"award-info":[{"award-number":["U1713222"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61773139"],"award-info":[{"award-number":["61773139"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61473015"],"award-info":[{"award-number":["61473015"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005046","name":"Natural Science Foundation of Heilongjiang Province","doi-asserted-by":"publisher","award":["F2015008"],"award-info":[{"award-number":["F2015008"]}],"id":[{"id":"10.13039\/501100005046","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Complexity"],"published-print":{"date-parts":[[2018,1]]},"abstract":"<jats:p>The sampling\u2010based motion planner is the mainstream method to solve the motion planning problem in high\u2010dimensional space. In the process of exploring robot configuration space, this type of algorithm needs to perform collision query on a large number of samples, which greatly limits their planning efficiency. Therefore, this paper uses machine learning methods to establish a probabilistic model of the obstacle region in configuration space by learning a large number of labeled samples. Based on this, the high\u2010dimensional samples\u2019 rapid collision query is realized. The influence of number of Gaussian components on the fitting accuracy is analyzed in detail, and a self\u2010adaptive model training method based on Greedy expectation\u2010maximization (EM) algorithm is proposed. At the same time, this method has the capability of online updating and can eliminate model fitting errors due to environmental changes. Finally, the model is combined with a variety of sampling\u2010based motion planners and is validated in multiple sets of simulations and real world experiments. The results show that, compared with traditional methods, the proposed method has significantly improved the planning efficiency.<\/jats:p>","DOI":"10.1155\/2018\/4358747","type":"journal-article","created":{"date-parts":[[2018,11,1]],"date-time":"2018-11-01T19:51:36Z","timestamp":1541101896000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Robot Motion Planning Method Based on Incremental High\u2010Dimensional Mixture Probabilistic Model"],"prefix":"10.1155","volume":"2018","author":[{"given":"Fusheng","family":"Zha","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5327-9496","authenticated-orcid":false,"given":"Yizhou","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2659-6677","authenticated-orcid":false,"given":"Xin","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4397-0931","authenticated-orcid":false,"given":"Fei","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingxuan","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Guo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2018,11]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"publisher","DOI":"10.1177\/0278364911406761"},{"key":"e_1_2_9_2_2","volume-title":"Probabilistic roadmaps for path planning in high-dimensional configuration spaces","author":"Kavraki L.","year":"1994"},{"key":"e_1_2_9_3_2","doi-asserted-by":"publisher","DOI":"10.1177\/02783640122067453"},{"key":"e_1_2_9_4_2","doi-asserted-by":"publisher","DOI":"10.1177\/0278364915577958"},{"key":"e_1_2_9_5_2","doi-asserted-by":"publisher","DOI":"10.1080\/10867651.1997.10487480"},{"key":"e_1_2_9_6_2","doi-asserted-by":"publisher","DOI":"10.1177\/027836402320556458"},{"key":"e_1_2_9_7_2","doi-asserted-by":"publisher","DOI":"10.1177\/0278364911403335"},{"key":"e_1_2_9_8_2","doi-asserted-by":"crossref","unstructured":"HsuD. JiangT. ReifJ. andSunZ. The bridge test for sampling narrow passages with probabilistic roadmap planners Proceedings of the 2003 IEEE International Conference on Robotics and Automation September 2003 Taiwan 4420\u20134426 2-s2.0-0344014348.","DOI":"10.1109\/ROBOT.2003.1242285"},{"key":"e_1_2_9_9_2","doi-asserted-by":"crossref","unstructured":"ZhangL.andManochaD. An efficient retraction-based RRT planner Proceedings of the 2008 IEEE International Conference on Robotics and Automation ICRA 2008 May 2008 USA 3743\u20133750 2-s2.0-51649118901.","DOI":"10.1109\/ROBOT.2008.4543785"},{"key":"e_1_2_9_10_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10514-005-4748-1"},{"key":"e_1_2_9_11_2","doi-asserted-by":"crossref","unstructured":"BurnsB.andBrockO. Information theoretic construction of probabilistic roadmaps 1 Proceedings of the Intelligent Robots and Systems 2003.(IROS 2003) 2003 IEEE\/RSJ International Conference 2003 650\u2013655.","DOI":"10.1109\/IROS.2003.1250703"},{"key":"e_1_2_9_12_2","doi-asserted-by":"crossref","unstructured":"ArslanO.andTsiotrasP. Machine learning guided exploration for sampling-based motion planning algorithms Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems IROS 2015 October 2015 Germany 2646\u20132652 2-s2.0-84958149939.","DOI":"10.1109\/IROS.2015.7353738"},{"key":"e_1_2_9_13_2","volume-title":"The role of vertex consistency in sampling-based algorithms for optimal motion planning","author":"Arslan O.","year":"2012"},{"key":"e_1_2_9_14_2","doi-asserted-by":"crossref","unstructured":"BialkowskiJ. OtteM. andFrazzoliE. Free-configuration biased sampling for motion planning Proceedings of the 2013 26th IEEE\/RSJ International Conference on Intelligent Robots and Systems: New Horizon IROS 2013 November 2013 Japan 1272\u20131279 2-s2.0-84893797109.","DOI":"10.1109\/IROS.2013.6696513"},{"key":"e_1_2_9_15_2","volume-title":"Model-based motion planning","author":"Burns B.","year":"2004"},{"key":"e_1_2_9_16_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2016.2573837"},{"key":"e_1_2_9_17_2","unstructured":"PanJ.andManochaD. Fast and robust motion planning with noisy data using machine learning Proceedings of the 30th International Conference on Machine Learning 2013."},{"key":"e_1_2_9_18_2","doi-asserted-by":"publisher","DOI":"10.1080\/01691864.2013.756386"},{"key":"e_1_2_9_19_2","doi-asserted-by":"crossref","unstructured":"HuhJ.andLeeD. D. Learning high-dimensional Mixture Models for fast collision detection in Rapidly-Exploring Random Trees Proceedings of the Robotics and Automation (ICRA) 2016 IEEE International Conference 2016 63\u201369.","DOI":"10.1109\/ICRA.2016.7487116"},{"key":"e_1_2_9_20_2","volume-title":"Learning sampling distributions for robot motion planning","author":"Ichter B.","year":"2017"},{"key":"e_1_2_9_21_2","article-title":"Neural networks enhanced adaptive admittance control of optimized robot-environment interaction","volume":"99","author":"Yang C.","year":"2018","journal-title":"IEEE Transactions on Cybernetics"},{"key":"e_1_2_9_22_2","article-title":"E-Graphs: Bootstrapping Planning with Experience Graphs","volume":"5","author":"Phillips M.","year":"2012","journal-title":"Robotics: Science and Systems"},{"key":"e_1_2_9_23_2","unstructured":"YangC. ChenC. HeW. CuiR. andLiZ. Robot Learning System Based on Adaptive Neural Control and Dynamic Movement Primitives 99 Proceedings of the EEE transactions on neural networks and learning systems 2018 1\u201311."},{"key":"e_1_2_9_24_2","doi-asserted-by":"publisher","DOI":"10.1023\/A:1013844811137"},{"key":"e_1_2_9_25_2","first-page":"279","article-title":"Mixture density estimation","author":"Li J. Q.","year":"2000","journal-title":"Advances in neural infor- mation processing systems"},{"key":"e_1_2_9_26_2","doi-asserted-by":"crossref","unstructured":"PanJ. ChittaS. andManochaD. FCL: A general purpose library for collision and proximity queries 3859\u20133866 2-s2.0-84864484328.","DOI":"10.1109\/ICRA.2012.6225337"},{"key":"e_1_2_9_27_2","doi-asserted-by":"publisher","DOI":"10.1109\/MRA.2012.2205651"},{"key":"e_1_2_9_28_2","doi-asserted-by":"crossref","unstructured":"KuffnerJ. J.Jr.andla ValleS. M. RRT-connect: an efficient approach to single-query path planning Proceedings of the IEEE International Conference on Robotics and Automation April 2000 995\u20131001 2-s2.0-0033726628.","DOI":"10.1109\/ROBOT.2000.844730"},{"key":"e_1_2_9_29_2","doi-asserted-by":"crossref","unstructured":"HsuD. LatombeJ.-C. andMotwaniR. Path planning in expansive configuration spaces Proceedings of the 1997 IEEE International Conference on Robotics and Automation ICRA. Part 3 (of 4) April 1997 2719\u20132726 2-s2.0-0030654330.","DOI":"10.1109\/ROBOT.1997.619371"},{"key":"e_1_2_9_30_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-00312-7_28"},{"key":"e_1_2_9_31_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10514-012-9321-0"}],"container-title":["Complexity"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/complexity\/2018\/4358747.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/complexity\/2018\/4358747.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/2018\/4358747","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T23:03:21Z","timestamp":1775257401000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1155\/2018\/4358747"}},"subtitle":[],"editor":[{"given":"Andy","family":"Annamalai","sequence":"additional","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2018,1]]},"references-count":31,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2018,1]]}},"alternative-id":["10.1155\/2018\/4358747"],"URL":"https:\/\/doi.org\/10.1155\/2018\/4358747","archive":["Portico"],"relation":{},"ISSN":["1076-2787","1099-0526"],"issn-type":[{"value":"1076-2787","type":"print"},{"value":"1099-0526","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,1]]},"assertion":[{"value":"2018-06-11","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2018-09-19","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2018-11-01","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"4358747"}}