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The Robot Navigation System Based on SAC Algorithm with Security Barrier Mechanism
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College of Science, Hohai University

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TP242.6

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    Abstract:

    In order to improve the intelligent level and security of mobile robot autonomous navigation system, an autonomous navigation system based on Soft Actor-Critic algorithm under the security barrier mechanism was pro-posed to make the robot more intelligent. The return function based on the distance between the robot and the nearest obstacle, the distance from the target point and the yaw angle was designed. In the gazebo simulation platform, a mobile robot with lidar and its surrounding environment were built. Experiments showed that the security barrier mechanism reduced the probability of robot hitting obstacles to a certain extent, improved the success rate of navigation, and made the mobile robot autonomous navigation system based on Soft Actor-Critic algorithm have higher generalization ability. The system still had the ability of autonomous navigation when changing the origin and destination or even changing the static environment to dynamic.

    Reference
    [1] SUTTON R S, BARTO A G. Reinforcement learning: an introduction[J]. IEEE transactions on neural networks, 1998, 9(5): 1054.
    [2] 刘志荣, 姜树海. 基于强化学习的移动机器人路径规划研究综述[J].制造业自动化,2019,41(03):90-92.
    [3] LIU ZR, JIANG SH. Review of mobile robot path plan-ning based on reinforcement learning[J]. Manufacturing automation, 2019, 41(3):90-92.
    [4] [3] MNIH V, KAVUKCUOGLU K, SILVER D, et al. Playing atari with deep reinforcement learning[J]. Computer science, 2013.
    [5] [4] LILLICRAP T P, HUNT J J, PRITZEL A, et al. Continuous control with deep reinforcement learning[J]. Computer ence, 2015.
    [6] [5] HAARNOJA T, ZHOU A, ABBEEL P, et al. Soft actor-critic: off-policy maximum entropy deep reinforcement learning with a stochastic actor[C]//International conference on machine learning, 2018.
    [7] [6] HAARNOJA T, ZHOU A, HARTIKAINEN K, et al. Soft actor-critic algorithms and applications[J]. arXiv: 1812.05905, 2018.
    [8] [7] SCHULMAN J, WOLSKI F, DHARIWAL P, et al. Proximal policy optimization algorithms, arXiv:1707.6347, 2017.
    [9] [8] XIANG JQ, LI QD, DONG XW, et al. Continuous control with deep reinforcement learning for mobile robot navigation[C]//2019 Chinese automation congress (CAC). IEEE, 2019: 1501-1506.
    [10] [9] DE Jesus JC, KICH V A, KOLLING A H, et al. Soft Actor-Critic for navigation of mobile robots[J]. Journal of intelligent & robotic systems,2021,102(2).
    [11] [10] 代珊珊, 刘全. 基于动作约束深度强化学习的安全自动驾驶方法[J]. 计算机科学, 2021, 48(9): 235-243.
    [12] DAI SS, LIU Q. Action Constrained Deep Reinforcement Learning Based Safe Automatic Driving Method[J]. Computer Science, 2021, 48(9): 235-243.
    [13] [11] POLYAK B T, JUDITSKY A B. Acceleration of stochastic approximation by averaging[J]. Siam journal on control and optimization, 2006, 30(4):838-855.
    [14] [12] KOENIG N, HOWARD A. Design and use paradigms for Gazebo, an open-source multi-robot simulator[C]//2004 IEEE/RSJ international conference on intelligent robots and systems (IROS), 2004, 3: 2149-2154.
    [15] [13] QUIGLEY M, Gerkey B P, Conley K, et al. ROS: an open-source robot operating system[C]//ICRA workshop on open-source software, 2009.
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History
  • Received:June 01,2022
  • Revised:June 29,2022
  • Adopted:July 12,2022
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