This paper proposes an artificial intelligence (AI)-based new control algorithm for a self-balancing quadruped robot. A quadruped robot is a good example of a redundant degree-of-freedom (DOF) system and is designed for locomotion over extreme terrain conditions. Even though a relevant control algorithm exerts a great effect on the performance of the locomotion control of quadruped robots, controlling them is difficult and complex because of the redundant DOF and interlocked movement of their four legs. This paper presents an effective control algorithm that can replace the typical analysis-based control theory, including inverse kinematics, differential equations of motion, and governing equations, which is based on reinforcement learning (RL) and artificial neural network (ANN). RL generates the training data to train the ANN model, and the trained ANN model is finally used to control a quadruped robot. The proposed AI-based robot-control algorithm is validated experimentally using a customized test-bed and a self-balancing quadruped robot. The results show that the proposed method is a promising new control algorithm that can replace the mathematically incomprehensible robot-control system.

本文提出了一种基于人工智能（AI）的自平衡四足机器人新控制算法。四足机器人是冗余自由度（DOF）系统的一个很好的例子，并且设计用于在极端地形条件下进行运动。尽管相关的控制算法对四足机器人的运动控制性能产生了很大影响，但由于冗余的自由度和四腿的联锁运动，控制它们仍然是困难而复杂的。本文提出了一种有效的控制算法，该算法可替代基于增强学习（RL）和人工神经网络（ANN）的典型的基于分析的控制理论，包括逆运动学，运动微分方程和控制方程。RL生成训练数据以训练ANN模型，最后，训练有素的人工神经网络模型将用于控制四足机器人。提出的基于AI的机器人控制算法已通过使用定制的测试台和自平衡四足机器人进行了实验验证。结果表明，该方法是一种有希望的新控制算法，可以代替数学上难以理解的机器人控制系统。