Model Predictive Control (MPC) approaches are widely used in robotics, since they guarantee feasibility and allow the computation of updated trajectories while the robot is moving. They generally require heuristic references for the tracking terms and proper tuning of the parameters of the cost function in order to obtain good performance. For instance, when a legged robot has to react to disturbances from the environment (e.g., recover after a push) or track a specific goal with statically unstable gaits, the effectiveness of the algorithm can degrade. In this work, we propose a novel optimization-based Reference Generator which exploits a Linear Inverted Pendulum (LIP) 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 ensures guarantees on the response time to reach a goal without the need to tune the weights of the cost terms. In addition, footholds are corrected using the optimized reference 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)方法在机器人中广泛使用,因为模型预测控制(MPC)方法能够保证可行性,并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和适当调整成本函数参数的超常参考,以便取得良好的性能。例如,当脚步机器人必须对环境干扰(例如,推后恢复)作出反应,或跟踪一个具体目标,使用静态不稳定的轨迹,算法的有效性可以降低。在这项工作中,我们建议采用新的优化参考生成器,利用线性反转的中转模型计算质量中心的参考轨迹,同时考虑可能存在的轨迹(例如,在轨迹中)作用不足的情况。获得的轨迹被用来作为我们先前工作[1]中提出的非线性MPC成本功能的参考。我们还提出一种配方,确保反应时间达到一个目标,而不需要调整成本参数的重量。我们用最优化的机器人模拟方法演示了我们的成本模型。