Gentiane Venture

Modeling and Identification of Passenger Car Dynamics Using Robotics Formalism

This paper deals with the problem of dynamic modeling and identification of passenger cars. It presents a new method that is based on robotics techniques for modeling and description of tree-structured multibody systems. This method enables us to systematically obtain the dynamic identification model, which is linear with respect to the dynamic parameters. The estimation of the parameters is carried out using a weighted least squares method. The identification is tested using vehicle dynamics simulation software used by the car manufacturer PSA Peugeot-Citroen in order to define a set of trajectories with good excitation properties and to determine the number of degrees of freedom of the model. The method has then been used to estimate the dynamic parameters of an experimental Peugeot 406, which is equipped with different position, velocity, and force sensors

Identifiability and identification of inertial parameters using the underactuated base-link dynamics for legged multibody systems

In this paper we study the dynamics of multibody systems with the base not permanently fixed to the inertial frame, or more specifically legged systems such as humanoid robots and humans. The issue is to be approached in terms of the identification theory developed in the field of robotics. The under-actuated base-link which characterizes the dynamics of legged systems is the focus of this work. The useful mechanical feature to analyze the dynamics of legged system is proven: the set of inertial parameters appearing in the equation of motion of the under-actuated base is equivalent to the set in the equations of the whole body. In particular, when no external force acts on the system, all of the parameters in the set except the total mass are generally identifiable only from the observation of the free-flying motion. We also propose a method to identify the inertial parameters based on the dynamics of the under-actuated base. The method does not require the measurement of the joint torques. Neither the joint frictions nor the actuator dynamics need to be considered. Even when the system has no external reaction force, the method is still applicable. The method has been tested on both a humanoid robot and a human, and the experimental results are shown.

Motion capture based identification of the human body inertial parameters

Identification of body inertia, masses and center of mass is an important data to simulate, monitor and understand dynamics of motion, to personalize rehabilitation programs. This paper proposes an original method to identify the inertial parameters of the human body, making use of motion capture data and contact forces measurements. It allows in-vivo painless estimation and monitoring of the inertial parameters. The method is described and then obtained experimental results are presented and discussed.

A numerical method for choosing motions with optimal excitation properties for identification of biped dynamics - An application to human

Identification results dramatically depend on the excitation properties of the motion used to sample the identification model. Strategies to define persistent exciting trajectories have been developed for manipulator robots with few DOF. However they can not easily be extended to humanoid systems and humans due to the important number of DOF; and empirical knowledge is often used to generate and select persistent exciting motions. In this paper we propose a method to choose persistent exciting motions from an existing dataset in order to optimize both the identification results and the computation time. This method is based on the use of the identification model of legged systems obtained from the base-link equations. Instead of using well-established consideration on the condition number of the regressor matrix, the method uses a decomposition of the regressor into elementary sub-regressors and the computation of the condition number for each. A selection rule is then proposed. The overall method is experimentally tested to identify the human body inertial parameters using a data-set of 40 motions. Comparative results obtained from different combinations of motions are given.

Real-time identification and visualization of human segment parameters

Mass parameters of the body segments are mandatory to study motion dynamics. No systematic method to estimate them has been proposed so far. Rather, parameters are scaled from generic tables or estimated with methods inappropriate for in-patient care. Based on our previous works, we propose a real-time software that allows to estimate the whole-body segment parameters, and to visualize the progresses of the completion of the identification. The visualization is used as a feedback to optimize the excitation and thus the identification results. The method is experimentally tested.

Identification of humanoid robots dynamics using floating-base motion dynamics

When simulating and controlling robot dynamics it is necessary to know the inertial parameters and the joint dynamics accurately. As these parameters are usually not provided by manufacturers, identification is then an essential step in robotics. In addition with the up coming wide-spreading of humanoid robots in the society the identification of humanoid dynamics has became mandatory to insure safety. This paper proposes a method to estimate humanoid robots inertial parameters using a minimal set of sensors. Only joint angles and external forces information are required. Simulations have provided exciting trajectories that are reproduced on a small-size humanoid robot. Experimental results are given.

Real-time implementation of physically consistent identification of human body segments

The mass parameters of the human body segments are important when studying motion dynamics and the in-vivo method to obtain accurate parameters is required in biomechanics studies and for some medical applications. In our previous works, we proposed the method to identify inertial parameters of human body segments in real-time during measurement of motion. However, some obtained parameters are not physically consistent; some masses are negative and inertia tensor matrices are not positive definite. These parameters generate problems in the analysis and the simulation requiring physical consistency. In this paper, we propose the real-time identification method considering physical consistency.

Identification of Human Limb Viscoelasticity using Robotics Methods to Support the Diagnosis of Neuromuscular Diseases

In this paper we present an original method to estimate in vivo the joint dynamics of the human limbs. The method is based on a non-invasive and painless technology making use of an optical motion capture system and an associated skeletal model to record the human motion and compute its kinematics and its dynamics. The formalism that is used for the identification is commonly used in robotics. The passive limb joints properties are modeled by enhanced spring-damper systems. The inverse dynamics is sampled along a movement to give an over-determined system. The obtained system is solved by the linear least-squares method. To perform the estimation, we place emphasis on giving indicators and requirements to interpret the obtained results, and on using painless, passive constraint-free movements that are usually performed during the clinical diagnosis of neuromuscular diseases. Finally the method is experimentally applied to two healthy subjects and five patients of neuromuscular diseases in order to estimate the upper-limb viscoelastic properties. The obtained results are discussed.

Recognizing Emotions Conveyed by Human Gait

Humans convey emotions through different ways. Gait is one of them. Here we propose to use gait data to highlight features that characterize emotions. Gait analysis study usually focuses on stance phase, frequency, footstep length. Here the study is based on the joint angles obtained from inverse kinematics computation from the 3D motion-capture data using a combination of degrees of freedom (DOF) out of a 34DOF human body model obtained from inverse kinematics of markers 3D position. The candidates are four professional actors, and five emotional states are simulated: Neutral, Joy, Anger, Sadness, and Fear. The paper presents first a psychological approach which results are used to propose numerical approaches. The first study provides psychological results on motion perception and the possibility of emotion recognition from gait by 32 observers. Then, the motion data is studied using a feature vector approach to verify the numerical identifiability of the emotions. Finally each motion is tested against a database of reference motions to identify the conveyed emotion. Using the first and second study results, we utilize a 6DOF model then a 12DOF model. The experimental results show that by using the gait characteristics it is possible to characterize each emotion with good accuracy for intra-subject data-base. For inter-subject database results show that recognition is more prone to error, suggesting strong inter-personal differences in emotional features.

Identification of the inertial parameters of a humanoid robot using unactuated dynamics of the base link

The inertial parameters are important to generate motion patterns for humanoid robots. Conventional identification methods can be used to estimate these parameters; however they required the joint torque estimates that can be obtained by modeling of the transmission or by direct measurements. To overcome that issue we have recently developed a new method to estimate the inertial parameters of legged systems. By using the base-link equations only, we obtain a reduced identification model that is free of joint torque estimates. In this paper we propose to apply the method to a human-size humanoid robot. The preliminary experimental results are given and discussed.