5 research outputs found
Robust Whole-Body Motion Control of Legged Robots
We introduce a robust control architecture for the whole-body motion control
of torque controlled robots with arms and legs. The method is based on the
robust control of contact forces in order to track a planned Center of Mass
trajectory. Its appeal lies in the ability to guarantee robust stability and
performance despite rigid body model mismatch, actuator dynamics, delays,
contact surface stiffness, and unobserved ground profiles. Furthermore, we
introduce a task space decomposition approach which removes the coupling
effects between contact force controller and the other non-contact controllers.
Finally, we verify our control performance on a quadruped robot and compare its
performance to a standard inverse dynamics approach on hardware.Comment: 8 Page
Real-Time Motion Planning of Legged Robots: A Model Predictive Control Approach
We introduce a real-time, constrained, nonlinear Model Predictive Control for
the motion planning of legged robots. The proposed approach uses a constrained
optimal control algorithm known as SLQ. We improve the efficiency of this
algorithm by introducing a multi-processing scheme for estimating value
function in its backward pass. This pass has been often calculated as a single
process. This parallel SLQ algorithm can optimize longer time horizons without
proportional increase in its computation time. Thus, our MPC algorithm can
generate optimized trajectories for the next few phases of the motion within
only a few milliseconds. This outperforms the state of the art by at least one
order of magnitude. The performance of the approach is validated on a quadruped
robot for generating dynamic gaits such as trotting.Comment: 8 page
Recent Progress in Legged Robots Locomotion Control
International audiencePurpose of review. In recent years, legged robots locomotion has been transitioning from mostly flat ground in controlled settings to generic indoor and outdoor environments, approaching now real industrial scenarios. This paper aims at documenting some of the key progress made in legged locomotion control that enabled this transition. Recent findings. Legged locomotion control makes extensive use of numerical trajectory optimization and its online implementation, Model Predictive Control. A key progress has been how this optimization is handled, with refined models and refined numerical methods. This led the legged locomotion research community to heavily invest in and contribute to the development of new optimization methods and efficient numerical software
STANCE: Locomotion Adaptation over Soft Terrain
Whole-body Control (WBC) has emerged as an important framework in locomotion
control for legged robots. However, most of WBC frameworks fail to generalize
beyond rigid terrains. Legged locomotion over soft terrain is difficult due to
the presence of unmodeled contact dynamics that WBCs do not account for. This
introduces uncertainty in locomotion and affects the stability and performance
of the system. In this paper, we propose a novel soft terrain adaptation
algorithm called STANCE: Soft Terrain Adaptation and Compliance Estimation.
STANCE consists of a WBC that exploits the knowledge of the terrain to generate
an optimal solution that is contact consistent and an online terrain compliance
estimator that provides the WBC with terrain knowledge. We validated STANCE
both in simulation and experiment on the Hydraulically actuated Quadruped (HyQ)
robot, and we compared it against the state of the art WBC. We demonstrated the
capabilities of STANCE with multiple terrains of different compliances,
aggressive maneuvers, different forward velocities, and external disturbances.
STANCE allowed HyQ to adapt online to terrains with different compliances
(rigid and soft) without pre-tuning. HyQ was able to successfully deal with the
transition between different terrains and showed the ability to differentiate
between compliances under each foot.Comment: 12 pages, 11 figure