This study presents a whole-body model predictive control (MPC) of robotic systems with rigid contacts, under a given contact sequence using online switching time optimization (STO). We treat robot dynamics with rigid contacts as a switched system and formulate an optimal control problem of switched systems to implement the MPC. We utilize an efficient solution algorithm for the MPC problem that optimizes the switching times and trajectory simultaneously. The present efficient algorithm, unlike inefficient existing methods, enables online optimization as well as switching times. The proposed MPC with online STO is compared over the conventional MPC with fixed switching times, through numerical simulations of dynamic jumping motions of a quadruped robot. In the simulation comparison, the proposed MPC successfully controls the dynamic jumping motions in twice as many cases as the conventional MPC, which indicates that the proposed method extends the ability of the whole-body MPC. We further conduct hardware experiments on the quadrupedal robot Unitree A1 and prove that the proposed method achieves dynamic motions on the real robot.
翻译:本研究在使用在线切换时间优化(STO)的某个接触序列下,对具有僵硬接触的机器人系统进行全体模型预测控制(MPC)。我们把具有僵硬接触的机器人动态作为转接系统处理,并设计了被转接系统的最佳控制问题。我们使用一种高效的MPC问题解决方案算法,同时优化转接时间和轨迹。目前的高效算法不同于效率低下的现有方法,允许在线优化和转接时间。与在线STO的拟议MPC相比,通常的MPC与固定的转接时间相比较,方法是对四重机器人动态跳动动作进行数字模拟。在模拟比较中,拟议的MPC成功地控制了动态跳动动作动作,其数量是常规的MPC的两倍,这表明拟议方法扩大了全机体MPC的能力。我们还在四重机器人Unree A1上进行了硬件实验,并证明拟议方法在真正的机器人上实现了动态动作。