基于DDPG算法的双轮腿机器人运动控制研究

Research on DDPG-based motion control of two-wheel-legged robot

轮腿式机器人兼具轮式和足式机器人的机动性和灵活性,在多种场景中具有广泛的应用前景。针对双轮腿机器人在崎岖地形运动控制缺陷、高度依赖于精确动力学模型、无法自适应求解等问题,提出一种基于深度确定性策略梯度(deep deterministic policy gradient,DDPG)算法的双轮腿机器人控制方法。首先,分析了双轮腿机器人模型及其模糊动力学模型;然后,使用DDPG算法生成双轮腿机器人在崎岖地面的运动控制策略;最后,为了验证控制器性能,分别进行了3组运动控制对比实验。仿真实验表明,在缺少地面状况先验知识的条件下,采用DDPG算法生成的运动控制策略实现了双轮腿式机器人在崎岖地面快速稳定运动的功能,其平均速度相比双轮机器人提高了约29.2%,姿态角偏移峰值相比双足机器人分别减小了约43.9%、66%、50%。

Wheel-legged robots combine the mobility and flexibility of wheeled and legged robots and have a wide range of application prospects in various scenarios.Aiming at the defects of existed motion control method of two-wheel-legged robots in rough ground,their high dependence on accurate dynamic models and their lucking of adaptive solving capability,a control method of the two-wheeled-legged robot based on deep deterministic policy gradient(DDPG)algorithm is proposed.First,the two-wheel-legged robot model and its fuzzy dynamics model are analyzed.Then,the motion control policy of the two-wheel-legged robot on the rugged ground is generated using the DDPG algorithm;Finally,In order to verify the performance of the controller,three groups of motion control comparison experiments were carried out respectively.Simulation experiments show that,in the absence of prior knowledge of ground conditions,the function of the fast and stable movement of the two-wheel-legged robot in the face of rugged ground is achieved;the average speed of the motion control strategy generated by the DDPG algorithm is about 29.2%higher than that of the two-wheeled robot;the peak value of Euler angle offset is reduced by about 43.9%,66%,and 50%compared with the bipedal robot.