Soft links and actuators are nowadays emerging technologies aiming to overcome some problems in robotics such as weight, cost or human interaction. However, the nonlinear nature of their elements can make their characterization challenging and hinder the use of standard control engineering tools. In this paper, we explore different state-of-the-art identification methods for the soft neck, in order to find a reliable plant model. Even though the neck has three Degrees of freedom, in this work we only consider the planar deflection of the link as a starting point for future analysis. Given the nonlinear nature of the soft neck, we consider two identification strategies, i.e., set membership, which is a data driven, nonlinear and nonparametric identification strategy, and Recursive Least Squares at selected linearization points. A neural network identification is also given for comparison purposes. Results show that the explored methods offer a suitable alternative to identify the dynamics of the neck that allows their implementation for simulation and future control.

This work presents a new attitude balancing strategy implemented and validated on a quadrupedal robot equipped with a custom hybrid wheel-legged mobility system. The proposed method uses an inverse kinematics solution scheme based on Quadratic Programming optimization to generate full body motions that ensure the desired balancing performances. The strategy generates a compliant behaviour to cope with the applied external forces resulting in a stable and smooth reaction response. Furthermore, the method takes advantage of the robot hybrid wheeled-legged mobility system to provide new motion capabilities and balancing reactions as it will be shown through the paper. Extensive simulation studies on the Centauro robot are presented. Results show the efficiency of the propose method demonstrating significant contribution in the rejection of the applied external disturbances.

In this paper, we propose an Energy based Fall Prediction (EFP) which observes the real-time balance status of a humanoid robot during standing. The EFP provides an analytic and quantitative measure of the level of balance. Both simulation and experimental studies were conducted and compared with the previously proposed indicators, such as Capture Point (CP) and Foot Rotation Indicator (FRI). The EFP also suggests the balance augmentation by active foot tilting to create larger potential barriers. As a proof of concept, a hybrid balance controller was designed to stabilize the robot including under-actuation phases so the robot can also balance with shoes. Our study reveals that both EFP and CP successfully predict falling about 0.2s in advance for the tested robot, while the FRI fails due to the light weight of the foot and limited resolution of the force/torque measurement.

This paper presents a passivity based compliance stabilizer for humanoid robots. The proposed stabilizer is an admittance controller that uses the force/torque sensing in feet to actively regulate the compliance for the position controlled system. The low stiffness provided by the stabilizer permits compliant interaction with external forces, and the active damping control guarantees the passivity by dissipating the excessive energy delivered by disturbances. Both the theoretical work and simulation validations are presented. The effectiveness of the stabilizer is demonstrated by the simulations of a simplified cart-table model and the multi-body model of a humanoid under impulsive/periodic force perturbations during standing and walking in place. Simulation data show the quantitative evaluation of the stabilization effect by comparing the responses of body attitude, center of mass, center of pressure without and with the stabilizer.