Generative Adversarial Network Based Heuristics for Sampling-Based Path Planning

Tianyi Zhang; Jiankun Wang; Max Q.-H. Meng

doi:10.1109/JAS.2021.1004275

Volume 9 Issue 1

Jan. 2022

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2022 > 9(1): 64-74

T. Y. Zhang, J. K. Wang, and M. 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

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T. Y. Zhang, J. K. Wang, and M. 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

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Generative Adversarial Network Based Heuristics for Sampling-Based Path Planning

doi: 10.1109/JAS.2021.1004275

1.
Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen 518055, China
2.
Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong, China
3.
Shenzhen Research Institute, The Chinese University of Hong Kong, Hong Kong, China

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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Author Bio:
Tianyi Zhang received the B.E. degree in automation from Harbin Institute of Technology in 2020. She is currently a Research Assistant with the Department of Electronic and Electrical Engineering of the Southern University of Science and Technology. Her current research interests include machine learning in robotics and computer vision

Jiankun Wang received the B.E. degree in automation from Shandong University in 2015, and the Ph.D. degree in Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong, China, in 2019. He is currently a Research Assistant Professor with the Department of Electronic and Electrical Engineering of the Southern University of Science and Technology. During the Ph.D. study, he spent six months at Stanford University, USA, as a Visiting Student Scholar supervised by Prof. Oussama Khatib. His current research interests include motion planning and control, human robot interaction, and machine learning in robotics

Max Q.-H. Meng (Fellow, IEEE) is currently a Chair Professor and the Head of the Department of Electronic and Electrical Engineering at the Southern University of Science and Technology, on leave from the Department of Electronic Engineering at The Chinese University of Hong Kong, China. He received the Ph.D. degree in electrical and computer engineering from the University of Victoria, Canada, in 1992. He joined The Chinese University of Hong Kong, China, in 2001 as a Professor and later the Chairman of Department of Electronic Engineering. He was with the Department of Electrical and Computer Engineering at the University of Alberta in Canada, where he served as the Director of the ART (Advanced Robotics and Teleoperation) Lab and held the positions of Assistant Professor (1994), Associate Professor (1998), and Professor (2000), respectively. He is an Honorary Chair Professor at Harbin Institute of Technology and Zhejiang University, and also the Honorary Dean of the School of Control Science and Engineering at Shandong University. His research interests include robotics, perception, and intelligence. He has published more than 750 journal and conference papers and book chapters and led more than 60 funded research projects to completion as Principal Investigator. He has been serving as the Editor-in-Chief and editorial board of a number of international journals and as the General Chair or Program Chair of many international conferences, including the General Chair of IROS 2005 and General Chair of ICRA 2021 held in Xi’an in May 2021. He served as an Associate VP for Conferences of the IEEE Robotics and Automation Society (2004–2007), Co-Chair of the Fellow Evaluation Committee and an elected Member of the AdCom of IEEE RAS. He is a Recipient of the IEEE Millennium Medal, a Fellow of IEEE, a Fellow of Hong Kong Institution of Engineers, and an Academician of the Canadian Academy of Engineering
Corresponding author: Max Q.-H. Meng, e-mail: max.meng@ieee.org
Tianyi Zhang and Jiankun Wang contributed equally to this work.
Received Date: 2021-01-27
Revised Date: 2021-03-30
Accepted Date: 2021-06-22

Available Online: 2021-07-20

Abstract

Abstract

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.
- Generative adversarial network (GAN),
- optimal path planning,
- robot path planning,
- sampling-based path planning

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Tianyi Zhang and Jiankun Wang contributed equally to this work.

References(35)

References

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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.

Generative Adversarial Network Based Heuristics for Sampling-Based Path Planning

doi: 10.1109/JAS.2021.1004275

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