Quadruped robots manifest great potential to traverse rough terrains with payload. Numerous traditional control methods for legged dynamic locomotion are model-based and exhibit high sensitivity to model uncertainties and payload variations. Therefore, high-performance model parameter estimation becomes indispensable. However, the inertia parameters of payload are usually unknown and dynamically changing when the quadruped robot is deployed in versatile tasks. To address this problem, online identification of the inertia parameters and the Center of Mass (CoM) position of the payload for the quadruped robots draw an increasing interest. This study presents an adaptive controller based on the online payload identification for the high payload capacity (the ratio between payload and robot's self-weight) quadruped locomotion. We name it as Adaptive Controller for Quadruped Locomotion (ACQL), which consists of a recursive update law and a control law. ACQL estimates the external forces and torques induced by the payload online. The estimation is incorporated in inverse-dynamics-based Quadratic Programming (QP) to realize a trotting gait. As such, the tracking accuracy of the robot's CoM and orientation trajectories are improved. The proposed method, ACQL, is verified in a real quadruped robot platform. Experiments prove the estimation efficacy for the payload weighing from 20 $kg$ to 75 $kg$ and loaded at different locations of the robot's torso.
翻译:四振机器人显然极有可能用有效载荷穿越粗糙的地形。 有许多传统的腿动动动移动控制方法以模型为基础, 对模型不确定性和有效载荷变异具有高度的敏感性。 因此, 高性能模型参数估计变得不可或缺。 但是, 当四振机器人被部署完成多种任务时, 有效载荷的惯性参数通常是未知的, 并且动态变化。 为了解决这个问题, 在线识别惯性参数和四振机器人有效载荷中心( COM)的位置引起越来越多的兴趣。 这项研究展示了基于高有效载荷( 有效载荷与机器人自重之比之比) 的在线有效载荷识别的适应控制器。 我们把它命名为四振动性 Locomotion (ACQL) 的适应性控制器, 由循环更新更新法和控制法组成。 ACQL 估计了四振动的四振动机器人的外部力量和质量中心位置。 该估计被纳入基于反动力的二次编程程序( QP) 以在高载载有效载荷能力( 有效载荷和机器人自重重量位置之间比率之比 ) 。 将实时的精度的精度定位用于实时机器人, 测试。 。 正在对高载机机机的精度的精度的精度 。 。