Centroidal Momentum Analysis for Defining a Stability Index for Human-Exoskeleton Interactive Walking : Perturbation Detection in Human Gait

Recently, exoskeletons have been in the spotlight as many studies demonstrated the effectiveness of the exoskeletons as a means that enables to not only resolve long-standing issues such as increase of societal burden for the care of ageing populations but also augments productivity in several fields, such as rehabilitation and industrial fields. In particular, lower limb exoskeletons have attracted the medical field, especially related to the ageing society due to its impact on augmentation and recovery of walking capability which is one of the core determinants of independent daily living. For practical use of the lower limb exoskeletons in real environments, however, there are still several issues to be resolved. One of them is how to manage balance of human walking supported by the exoskeleton, in other words, how to monitor walking stability of a system combined with human and exoskeleton and maintain (or recover) the system balance when the user meets unpredicted disturbances, and thus to avoid falls. The former is a rationale of the study and this paper deals with a ‘stability index’, referred to as a kind of measure to monitor the actual (in)stability state during walking. The proposed stability index is based on the Centroidal Momentum (CM) that consists of linear and angular momenta at the Center of Mass (CoM). CM is a fundamental parameter used to describe physical motion of a system in classical mechanics, and it has been studied widely in biomechanics and bipedal robot fields over the last decade as it, specifically angular momentum-based analysis, offers important clues on how humans maintain balance during walking as well as facilitates postural balance control of humanoid robots in standing. As an extension of this context, in our previous work, we analyzed CM behavior during human walking under perturbations, specifically lateral perturbations applied to the pelvis. As a continuation of the study, in this paper, we examine whether CM could be used as the stability index to detect the perturbations as well as an initial loss of balance. In other words, a perturbation detection method on the basis of calculation of CM while waking is presented. In the method, variation of CM patterns between unperturbed and perturbed walking plays a crucial role in detecting perturbations. The method has been evaluated with experimental data of human walking and results show that the method is capable of detecting moderate and strong perturbations determined by combination of diverse durations and magnitudes of disturbance force. Average detection time obtained was about 334 msec. This study was carried out in the context of the EU FP7 project BALANCE that aims at supporting the function of maintaining postural balance directly through a leg exoskeleton. For this purpose CM-based stability index to be developed and related findings will be extended to the exoskeleton cooperating with a human and assessed on performance inEuropean Commission FP

Trajectory generation for multi-contact momentum-control

Simplified models of the dynamics such as the linear inverted pendulum model (LIPM) have proven to perform well for biped walking on flat ground. However, for more complex tasks the assumptions of these models can become limiting. For example, the LIPM does not allow for the control of contact forces independently, is limited to co-planar contacts and assumes that the angular momentum is zero. In this paper, we propose to use the full momentum equations of a humanoid robot in a trajectory optimization framework to plan its center of mass, linear and angular momentum trajectories. The model also allows for planning desired contact forces for each end-effector in arbitrary contact locations. We extend our previous results on LQR design for momentum control by computing the (linearized) optimal momentum feedback law in a receding horizon fashion. The resulting desired momentum and the associated feedback law are then used in a hierarchical whole body control approach. Simulation experiments show that the approach is computationally fast and is able to generate plans for locomotion on complex terrains while demonstrating good tracking performance for the full humanoid control

Momentum Control of Humanoid Robots with Series Elastic Actuators

Humanoid robots may require a degree of compliance at the joint level for improving efficiency, shock tolerance, and safe interaction with humans. The presence of joint elasticity, however, complexifies the design of balancing and walking controllers. This paper proposes a control framework for extending momentum based controllers developed for stiff actuators to the case of series elastic actuators. The key point is to consider the motor velocities as an intermediate control input, and then apply high-gain control to stabilise the desired motor velocities achieving momentum control. Simulations carried out on a model of the robot iCub verify the soundness of the proposed approach

Humanoid Momentum Estimation Using Sensed Contact Wrenches

This work presents approaches for the estimation of quantities important for the control of the momentum of a humanoid robot. In contrast to previous approaches which use simplified models such as the Linear Inverted Pendulum Model, we present estimators based on the momentum dynamics of the robot. By using this simple yet dynamically-consistent model, we avoid the issues of using simplified models for estimation. We develop an estimator for the center of mass and full momentum which can be reformulated to estimate center of mass offsets as well as external wrenches applied to the robot. The observability of these estimators is investigated and their performance is evaluated in comparison to previous approaches.Comment: Submitted to the 15th IEEE RAS Humanoids Conference, to be held in Seoul, Korea on November 3 - 5, 201

Automatic Gain Tuning of a Momentum Based Balancing Controller for Humanoid Robots

This paper proposes a technique for automatic gain tuning of a momentum based balancing controller for humanoid robots. The controller ensures the stabilization of the centroidal dynamics and the associated zero dynamics. Then, the closed-loop, constrained joint space dynamics is linearized and the controller's gains are chosen so as to obtain desired properties of the linearized system. Symmetry and positive definiteness constraints of gain matrices are enforced by proposing a tracker for symmetric positive definite matrices. Simulation results are carried out on the humanoid robot iCub.Comment: Accepted at IEEE-RAS International Conference on Humanoid Robots (HUMANOIDS). 201

Balancing experiments on a torque-controlled humanoid with hierarchical inverse dynamics

Recently several hierarchical inverse dynamics controllers based on cascades of quadratic programs have been proposed for application on torque controlled robots. They have important theoretical benefits but have never been implemented on a torque controlled robot where model inaccuracies and real-time computation requirements can be problematic. In this contribution we present an experimental evaluation of these algorithms in the context of balance control for a humanoid robot. The presented experiments demonstrate the applicability of the approach under real robot conditions (i.e. model uncertainty, estimation errors, etc). We propose a simplification of the optimization problem that allows us to decrease computation time enough to implement it in a fast torque control loop. We implement a momentum-based balance controller which shows robust performance in face of unknown disturbances, even when the robot is standing on only one foot. In a second experiment, a tracking task is evaluated to demonstrate the performance of the controller with more complicated hierarchies. Our results show that hierarchical inverse dynamics controllers can be used for feedback control of humanoid robots and that momentum-based balance control can be efficiently implemented on a real robot.Comment: appears in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 201