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Autonomous mobile robot path planning in unknown dynamic environments using neural dynamics

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Abstract

In this paper, a novel variant of bio-inspired planning algorithms is presented for robot collision-free path planning in dynamic environments without prior information. The first contribution of this paper is that, with mild technical analysis, the traditional neural dynamic model almost always returns a sub-optimal choice in some challenging scenarios, such as the boundary map and the narrow pathway map. Second, the proposed planning algorithm, namely the padding mean neural dynamic model, is a topologically organized network with connections among neighbouring neurons and is good for spreading nerve impulses such as a waves without coupling effects. The signal transduction method within a network is based on a dynamic neural activity field, which propagates high neural activity from the target state to the whole field, excluding obstacle regions. Third, simulation studies are conducted to compare the performance of the proposed planning algorithm and other popular planning algorithms in terms of effectiveness and efficiency. As a result, the proposed method can drive a robot to find more reasonable paths in both static maps and unknown dynamic scenarios with moving obstacles and a moving target. Finally, the novel excitatory input design of the proposed algorithm is discussed and analysed to explore the neural stimulus propagation mechanism within the network.

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Acknowledgements

This work was partially supported by National Natural Science Foundation of China (Project No. 61803089), Natural Science Foundation of Fujian Province (No. 2019J01213), Fuzhou University Startup Research Project (No. XRC-17065) and Educational Research Projects for Young and Middle-aged Teachers of Fujian Province (No. JT180024). With the permission of the all authors, this manuscript is submitted to the Soft Computing and expected to publish.

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Authors and Affiliations

  1. School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, China

    Yanjie Chen & Jiacheng Liang

  2. Guilin University of Aerospace Technology, Guilin, China

    Qi Pan

  3. College of Electrical and Information Engineering, Hunan University, Changsha, China

    Yaonan Wang, Jianhao Tan & Jianxu Mao

  4. National Engineering Laboratory for Robot Visual Perception and Control Technology, Changsha, China

    Yaonan Wang, Jianhao Tan & Jianxu Mao

Authors
  1. Yanjie Chen
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  2. Jiacheng Liang
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  3. Yaonan Wang
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  4. Qi Pan
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  5. Jianhao Tan
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  6. Jianxu Mao
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Correspondence to Yanjie Chen.

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Chen, Y., Liang, J., Wang, Y. et al. Autonomous mobile robot path planning in unknown dynamic environments using neural dynamics. Soft Comput 24, 13979–13995 (2020). https://doi.org/10.1007/s00500-020-04771-5

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