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Volume 2018, Article ID 4358747, 14 pages
Research Article

Robot Motion Planning Method Based on Incremental High-Dimensional Mixture Probabilistic Model

1State key Laboratory of Robotics and Systems, Harbin Institute of Technology, Harbin, China
2Shenzhen Academy of Aerospace Technology, Shenzhen, China
3Istituto Italiano di Tecnologia, Via Morego 30, Genova, Italy

Correspondence should be addressed to Xin Wang; moc.liamg@s70tihgnawnix and Fei Chen; ti.tii@nehc.ief

Received 11 June 2018; Revised 18 August 2018; Accepted 19 September 2018; Published 1 November 2018

Guest Editor: Andy Annamalai

Copyright © 2018 Fusheng Zha et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The sampling-based motion planner is the mainstream method to solve the motion planning problem in high-dimensional 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-dimensional samples’ rapid collision query is realized. The influence of number of Gaussian components on the fitting accuracy is analyzed in detail, and a self-adaptive model training method based on Greedy expectation-maximization (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-based 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.