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Volume 9 Issue 1
Jan.  2022

IEEE/CAA Journal of Automatica Sinica

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Article Contents
Tianyi Zhang, Jiankun Wang and Max Q.-H. Meng, "Generative Adversarial Network Based Heuristics for Sampling-Based Path Planning," IEEE/CAA J. Autom. Sinica, vol. 9, no. 1, pp. 64-74, Jan. 2022. doi: 10.1109/JAS.2021.1004275
Citation: Tianyi Zhang, Jiankun Wang and Max Q.-H. Meng, "Generative Adversarial Network Based Heuristics for Sampling-Based Path Planning," IEEE/CAA J. Autom. Sinica, vol. 9, no. 1, pp. 64-74, Jan. 2022. doi: 10.1109/JAS.2021.1004275

Generative Adversarial Network Based Heuristics for Sampling-Based Path Planning

doi: 10.1109/JAS.2021.1004275
Funds:  This work was partially supported by National Key R&D Program of China (2019YFB1312400), Shenzhen Key Laboratory of Robotics Perception and Intelligence (ZDSYS20200810171800001), Hong Kong RGC GRF (14200618), Hong Kong RGC CRF (C4063-18G)
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  • Sampling-based path planning is a popular methodology for robot path planning. With a uniform sampling strategy to explore the state space, a feasible path can be found without the complex geometric modeling of the configuration space. However, the quality of the initial solution is not guaranteed, and the convergence speed to the optimal solution is slow. In this paper, we present a novel image-based path planning algorithm to overcome these limitations. Specifically, a generative adversarial network (GAN) is designed to take the environment map (denoted as RGB image) as the input without other preprocessing works. The output is also an RGB image where the promising region (where a feasible path probably exists) is segmented. This promising region is utilized as a heuristic to achieve non-uniform sampling for the path planner. We conduct a number of simulation experiments to validate the effectiveness of the proposed method, and the results demonstrate that our method performs much better in terms of the quality of the initial solution and the convergence speed to the optimal solution. Furthermore, apart from the environments similar to the training set, our method also works well on the environments which are very different from the training set.

     

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  • Tianyi Zhang and Jiankun Wang contributed equally to this work.
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    Highlights

    • Propose a heuristic method for sampling-based path planning algorithm using GAN.
    • Design a GAN model to predict the promising region for non-uniform sampling.
    • Apply the GAN-based heuristic method to case studies to demonstrate the effectiveness of the proposed method.

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