Ko Ayusawa

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.

High-fidelity joint drive system by torque feedback control using high precision linear encoder

When robots cooperate with humans it is necessary for robots to move safely on sudden impact. Joint torque sensing is vital for robots to realize safe behavior and enhance physical performance. Firstly, this paper describes a new torque sensor with linear encoders which demonstrates electro magnetic noise immunity and is unaffected temperature changes. Secondly, we propose a friction compensation method using a disturbance observer to improve the positioning accuracy. In addition, we describe a torque feedback control method which scales down the motor inertia and enhances the joint flexibility. Experimental results of the proposed controller are presented.

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 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.

Muscle strength and Mass Distribution Identification toward subject-specific musculoskeletal modeling

In current biomechanics approach, the assumptions are commonly used in body-segment parameters and muscle strength parameters due to the difficulty in accessing those subject-specific values. Especially in the rehabilitation and sports science where each subject can easily have quite different anthropometry and muscle condition due to disease, age or training history, it would be important to identify those parameters to take benefits correctly from the recent advances in computational musculoskeletal modeling. In this paper, Mass Distribution Identification to improve the joint torque estimation and Muscle Strength Identification to improve the muscle force estimation were performed combined with previously proposed methods in muscle tension optimization. This first result highlights that the reliable muscle force estimation could be extracted after these identifications. The proposed framework toward subject-specific musculoskeletal modeling would contribute to a patient-oriented computational rehabilitation.

Identification of standard inertial parameters for large-DOF robots considering physical consistency

The identification method for industrial manipulators considering physical consistency such as positive definiteness of inertial parameters has been developed, however it has to solve the quadratic programming with the non-linear inequality constraints. In identifying the large DOF systems like humanoid robots, the converged solution is difficult to be obtained. In this paper, we propose the method to realize physical consistency and computational stability. As inertial parameters of each link are represented with a finite number of mass points, the constraints can be approximated by linear inequalities. We also propose to solve the optimization problem, which minimizes the errors both from measured data and the priori parameters extracted from the geometric model like CAD data. The method can estimate standard inertial parameters, which is a useful notation to be used for other applications.