Model Predictive Control (MPC) approaches are widely used in robotics, since they allow to compute updated trajectories while the robot is moving. They generally require heuristic references for the tracking terms and proper tuning of parameters of the cost function in order to obtain good performance. When for example, a legged robot has to react to disturbances from the environment (e.g., recover after a push) or track a certain goal with statically unstable gaits, the effectiveness of the algorithm can degrade. In this work we propose a novel optimization-based Reference Generator, named Governor, which exploits a Linear Inverted Pendulum model to compute reference trajectories for the Center of Mass, while taking into account the possible under-actuation of a gait (e.g. in a trot). The obtained trajectories are used as references for the cost function of the Nonlinear MPC presented in our previous work [1]. We also present a formulation that can guarantee a certain response time to reach a goal, without the need to tune the weights of the cost terms. In addition, foothold locations are corrected to drive the robot towards the goal. We demonstrate the effectiveness of our approach both in simulations and experiments in different scenarios with the Aliengo robot.
翻译:模型预测控制(MPC)方法在机器人移动时广泛使用,因为模型预测控制(MPC)方法允许在机器人移动时计算更新的轨迹,通常需要用于跟踪术语的超常参考和适当调整成本函数参数,以便取得良好的性能。例如,当脚踏脚踏两条机器人必须对环境干扰(例如,推后恢复)作出反应,或以静态不稳定的轨迹跟踪某一目标时,算法的有效性可以降低。在这项工作中,我们提议了一个新的优化参考发电机,名为州长,它利用线形颠倒的平板模型为质量中心计算参考轨迹,同时考虑可能发生的轨迹(例如,在音轨中)的触动不足。获得的轨迹被用来作为我们先前工作[1]中介绍的非线性MPC成本功能的参照。我们还提出了一个配方,可以保证达到目标的一定的反应时间,而不必调整成本参数的重量。此外,我们还在模拟实验中用不同机床位定位来修正了我们的机器人目标。