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Robust motion planning for mobile robots under attacks against obstacle localization

Published online by Cambridge University Press:  18 September 2024

Fenghua Wu ,
Wenbing Tang Open the ORCID record for Wenbing Tang [Opens in a new window] ,
Yuan Zhou Open the ORCID record for Yuan Zhou [Opens in a new window] ,
Shang-Wei Lin ,
Zuohua Ding  and
Yang Liu
Fenghua Wu
Affiliation:
School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore
Wenbing Tang*
Affiliation:
Shanghai Key Laboratory of Trustworthy Computing, East China Normal University, Shanghai, PR China
Yuan Zhou
Affiliation:
School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore
Shang-Wei Lin
Affiliation:
School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore
Zuohua Ding
Affiliation:
School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou, PR China
Yang Liu
Affiliation:
School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore
*
Corresponding author: Wenbing Tang; Email: wenbingtang@hotmail.com
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Abstract

Thanks to its real-time computation efficiency, deep reinforcement learning (DRL) has been widely applied in motion planning for mobile robots. In DRL-based methods, a DRL model computes an action for a robot based on the states of its surrounding obstacles, including other robots that may communicate with it. These methods always assume that the environment is attack-free and the obtained obstacles’ states are reliable. However, in the real world, a robot may suffer from obstacle localization attacks (OLAs), such as sensor attacks, communication attacks, and remote-control attacks, which cause the robot to retrieve inaccurate positions of the surrounding obstacles. In this paper, we propose a robust motion planning method ObsGAN-DRL, integrating a generative adversarial network (GAN) into DRL models to mitigate OLAs in the environment. First, ObsGAN-DRL learns a generator based on the GAN model to compute the approximation of obstacles’ accurate positions in benign and attack scenarios. Therefore, no detectors are required for ObsGAN-DRL. Second, by using the approximation positions of the surrounding obstacles, ObsGAN-DRL can leverage the state-of-the-art DRL methods to compute collision-free motion commands (e.g., velocity) efficiently. Comprehensive experiments show that ObsGAN-DRL can mitigate OLAs effectively and guarantee safety. We also demonstrate the generalization of ObsGAN-DRL.

Keywords

motion planningdeep reinforcement learninggenerative adversarial networkobstacle localization attacks

Information

Type
Research Article
Information
Robotica , Volume 42 , Issue 8 , August 2024 , pp. 2781 - 2800
Copyright
© The Author(s), 2024. Published by Cambridge University Press

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