Optimization-Based Reference Generator for Nonlinear Model Predictive Control of Legged Robots

Abstract

Model predictive control (MPC) approaches are widely used in robotics, because 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., to 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 underactuation 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. 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 by using the optimized reference to drive the robot toward the goal. We demonstrate the effectiveness of our approach both in simulations and experiments in different scenarios with the Aliengo robot.

Design, Modelling, and Analysis of Legged Robot for Terrains Exploration

“…The major driver of malfunctions in such robots is a result of their mechanical complication, which significantly raises the price and weight of the robot [27], [28]. The robot could lose energy due to air friction, which might also cause a problem with the stability of the robot if it is going at high speed [29], [30].…”

Special Issue “Legged Robots into the Real World”

“…Optimization strategies in model predictive control form a core component of advanced robotic control, shaping a robot's interaction with its environment in real-time. Paper [2] unfolds an innovative optimization-based reference generator for the model predictive control of legged robots. The proposed methodology tackles the intricate problem of balancing the robot's dynamic behaviour with the requirements of a specific goal and environmental disturbances.…”