MPC-based Controller with Terrain Insight for Dynamic Legged Locomotion

Abstract

We present a novel control strategy for dynamic legged locomotion in complex scenarios, that considers information about the morphology of the terrain in contexts when only on-board mapping and computation are available. The strategy is built on top of two main elements: first a contact sequence task that provides safe foothold locations based on a convolutional neural network to perform fast and continuous evaluation of the terrain in search of safe foothold locations; then a model predictive controller that considers the foothold locations given by the contact sequence task to optimize target ground reaction forces. We assess the performance of our strategy through simulations of the hydraulically actuated quadruped robot HyQReal traversing rough terrain under realistic on-board sensing and computing conditions.

ExoNet Database: Wearable Camera Images of Human Locomotion Environments

“…Available publicly through IEEE DataPort, ExoNet provides researchers an unprecedented communal platform to develop and compare next-generation image classification algorithms for human locomotion environment recognition. Although ExoNet was originally designed for environment-aware control systems for lower-limb exoskeletons and prostheses, applications could extend to humanoids and autonomous legged robots (Park et al, 2015 ; Villarreal et al, 2020 ). Users of the ExoNet database are requested to reference this dataset report.…”

Multi-Layered Safety for Legged Robots via Control Barrier Functions and Model Predictive Control

“…In contrast, Model Predictive Control has become a central method for the online synthesis and control of dynamic systems over a given time horizon [15]. In the context of the stepping-stones problem, a distinction can be made between MPC based approaches where the footholds locations are determined separately from the torso motion optimization [16], [17], [18], and MPC based approaches where the foothold location and torso motions are jointly optimized. The benefit of jointly optimizing torso and leg motions has been demonstrated in the field of trajectory optimization [19], [20].…”

Model Predictive Control With Environment Adaptation for Legged Locomotion

“…One specific feature of legged robots is the need to ensure that the values of the GRFs equal to zero for a swinging leg. This is typically done by introducing complementarity constraints [32,46]. These constraints, however, pose several difficulties in the solution of the optimization problem, since the vast majority of the NLP algorithms cannot handle them and tailored solvers are required.…”

“…Since, including the mobility cost enables NMPC to provide the optimal base orientation for a particular locomotion that retains mobility, there is no need to separately specify tracking cost for roll and pitch in the NMPC. This relieves a user from the burden of implementing a customized heuristic (e.g., to align the robot base to the terrain), as was necessary in, e.g., [3,46,49]. The relative tracking task for the CoM Z position is no longer required either, because maximizing the mobility in the Z direction automatically takes care of keeping an average distance of hips from the terrain to C p hf,z , consequently keeping the robot base at a certain height.…”