Neural Computing and Applications

, Volume 31, Issue 12, pp 9185–9205 | Cite as

Learning sampling distribution for motion planning with local reconstruction-based self-organizing incremental neural network

  • Chongkun Xia
  • Yunzhou ZhangEmail author
  • I-Ming Chen
Original Article


For sampling-based motion planners (e.g., PRM and RRT*), collision detection dominates the asymptotic running time and reduces the execution efficiency. The reason of this problem is that obtaining a high-dimensional implicit representation (i.e., configuration space distribution) of the state space is not easy, especially in the complicated environment with various obstacles. Though sampling-based planning algorithms and their variants perform well, most of these algorithms have strict restrictions and narrow applications. A possible ideal solution is to design a non-uniform sampling strategy to ensure the sampling process only occurs in collision-free region \(\chi _{free}\) but not in collision region \(\chi _{col}\). Therefore, we propose a new methodology to learn the sampling distribution for non-uniform sampling. The sampling distribution is learned through a local reconstruction-based self-organizing incremental neural network and allows to generate samples from the learned latent distribution. Besides, our method can adapt well to environmental non-vigorous changes and adjust the learned distribution quickly. The method can effectively exploit the underlying structure of the planning problem and be spread for general use in combination with any sampling-based planning algorithms. Specifically, we use two typical planning problems to show that the proposed method can effectively learn and update the sampling distribution from the high-dimensional configuration space in the changed environment, resulting in a dominant performance in terms of the cost, running time and success rate.


Motion planning Sampling distribution Local reconstruction Self-organizing incremental neural network Non-uniform sampling 



We would like to thank other members from Robotics Research Centre (RRC) of Nanyang Technological University (Singapore) for helping us to improve this work. We benefited a lot from the academic discussion and exchanges in RRC. Additionally, the authors would like to thank experienced anonymous reviewers for their constructive and valuable suggestions for improving the overall quality of this paper.


This work was supported by Fundamental Research Funds for the Central Universities, China (N172608005, N182608004), the Distinguished Creative Talent Program of Shenyang (RC170490) and National Natural Science Foundation of China (No. 61471110, 61733003), China Scholarship Council (CSC) scholarships (No. 201806080120).

Compliance with ethical standards

Conflict of interest

No conflict of interest exits in the submission of this manuscript, and the manuscript is approved by all authors for publication. We would like to declare that the work described was original research that has not been published previously, and not under consideration for publication elsewhere, in whole or in part. All authors listed have approved the manuscript that is enclosed.


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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.College of Information Science and EngineeringNortheastern UniversityShenyangChina
  2. 2.School of Mechanical and Aerospace EngineeringNanyang Technological University (NTU)SingaporeSingapore

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