Neuro-Fuzzy Sampling: Safe and Fast Multi-query Randomized Path Planning for Mobile Robots

  • Weria KhaksarEmail author
  • Md Zia Uddin
  • Jim Torresen
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1015)


Despite the proven advantages of probabilistic motion planning algorithms in solving complex navigation problems, their performance is restricted by the selected nodes in the configuration space. Furthermore, the choice of selecting the neighbor nodes and expanding the graph structure is limited by a set of deterministic measures such as Euclidean distance. In this paper, an improved version of multi-query planners is proposed which utilizes a neuro-fuzzy structure in the sampling stage to achieve a higher level of effectiveness including safety and applicability. This planner employs a set of expert rules concerning the distance to the surrounding obstacles and constructs a fuzzy controller. Then, parameters of the resulting fuzzy system are optimized based on a hybrid learning technique. The outcome of the neuro-fuzzy system is implemented on a multi-query planner to improve the quality of the selected nodes. The planner is further improved by adding a post-processing step which shortens the path by removing the redundant segments and by smoothing the resulting path through the inscribed circle of any two consecutive segments. The planner was tested through simulation in different planning problems and was compared to a set of benchmark algorithms. Furthermore, the proposed planner was implemented on a robotic system. Simulation and experimental results indicate the superior performance of the planner in terms of safety and applicability.


Path planning Mobile robot Sampling-based Multi-query Safety Neuro-fuzzy system 



This work is supported by the Research Council of Norway as a part of Multimodal Elderly Care Systems (MECS) project, under grant agreement 247697.


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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Robotics and Intelligent Systems Group, Department of InformaticsUniversity of OsloOsloNorway

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