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<p>In this paper, a control method of double-swing-arm tracked robot based on deep reinforcement learning is proposed to solve the problem of stable operation of the robot on uneven terrain. A control algorithm without complex kinematics analysis is designed, so that the robot can learn independently and keep balance on various irregular terrain. This paper mainly studies the stability of the robot when crossing the terrain, so as to reduce the damage to the robot hardware. The main contributions are as follows: (1) Combining the hierarchical control strategy with the curiosity module, and testing in the simulation environment has achieved good performance; (2) By adding stability design, the robot can pass through uneven terrain more smoothly; (3) Three kinds of terrain task scenes are developed in the simulation environment, and the effectiveness of the control algorithm is verified. The experimental results show that this method can effectively improve the robot’s ability to cross complex terrain while maintaining high stability.</p>

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Control of Double Swing Arm Tracked Robot Based on Deep Reinforcement Learning in Various Uneven Terrains

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年4月21日

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  • Zhongye Gao,
  • Furao Shen,
  • Jian Zhao

摘要

In this paper, a control method of double-swing-arm tracked robot based on deep reinforcement learning is proposed to solve the problem of stable operation of the robot on uneven terrain. A control algorithm without complex kinematics analysis is designed, so that the robot can learn independently and keep balance on various irregular terrain. This paper mainly studies the stability of the robot when crossing the terrain, so as to reduce the damage to the robot hardware. The main contributions are as follows: (1) Combining the hierarchical control strategy with the curiosity module, and testing in the simulation environment has achieved good performance; (2) By adding stability design, the robot can pass through uneven terrain more smoothly; (3) Three kinds of terrain task scenes are developed in the simulation environment, and the effectiveness of the control algorithm is verified. The experimental results show that this method can effectively improve the robot’s ability to cross complex terrain while maintaining high stability.