Estimation of the transverse sway motion of a biped robot during the march

Debido al balanceo generado por la dinámica natural y perturbaciones durante la marcha de un robot humanoide, es difícil predecir su postura en determinado instante a lo largo de la misma, complicando así el desarrollo de tareas de manipulación, cooperación, evasión de obstáculos, retroalimentación servo-visual, entre otras. En este documento se describe una metodología para predecir el movimiento de balanceo en el plano transversal del robot, a partir de la trayectoria de un punto fijo en su estructura mecánica. Se considera el estudio de dos modelos matemáticos para aproximar el movimiento de balanceo del humanoide: aproximación mediante una función sinusoidal y aproximación por series de Fourier. En ambos casos, es necesario el conocimiento de los parámetros involucrados del modelo, por lo que se implementan tres técnicas de aproximación paramétrica: mínimos cuadrados e identificación algebraica para el caso de la aproximación sinusoidal, y el cálculo de coeficientes para el caso de las series de Fourier. Para validar la metodología, se lleva a cabo el seguimiento del robot en tiempo real puesto que la trayectoria del punto de interés es afectada por diversos factores como la fricción, inclinación e imperfecciones de la superficie, el estado de conservación del robot, entre otros. A partir de diversos experimentos, se desarrolla una comparación cuantitativa entre las diferentes aproximaciones para verificar aquel que mejor reproduce a la dinámica del balanceo del robot.Peer Reviewe

Fast foot prints re-planning and motion generation during walking in physical human-humanoid interaction

Abstract-In this paper a system allowing real-time interaction between a human and a humanoid robot while walking is presented. The aim of this work is to integrate humanoid robots into collaborative working environment. Co-located realization of a task is one instance of such collaboration. To achieve such task whole-body motion generation while keeping balance is mandatory. This is obtained using a real-time pattern generator allowing on-line foot-print modification integrated in a stack of controllers. Several experiments of direct interaction between a human and a HRP-2 humanoid robot illustrates the results

ZMP support areas for multi-contact mobility under frictional constraints

We propose a method for checking and enforcing multi-contact stability based on the Zero-tilting Moment Point (ZMP). The key to our development is the generalization of ZMP support areas to take into account (a) frictional constraints and (b) multiple non-coplanar contacts. We introduce and investigate two kinds of ZMP support areas. First, we characterize and provide a fast geometric construction for the support area generated by valid contact forces, with no other constraint on the robot motion. We call this set the full support area. Next, we consider the control of humanoid robots using the Linear Pendulum Mode (LPM). We observe that the constraints stemming from the LPM induce a shrinking of the support area, even for walking on horizontal floors. We propose an algorithm to compute the new area, which we call pendular support area. We show that, in the LPM, having the ZMP in the pendular support area is a necessary and sufficient condition for contact stability. Based on these developments, we implement a whole-body controller and generate feasible multi-contact motions where an HRP-4 humanoid locomotes in challenging multi-contact scenarios.Comment: 14 pages, 10 figure

ZMP Support Areas for Multicontact Mobility Under Frictional Constraints

International audienceWe propose a method for checking and enforcing multi-contact stability based on the Zero-tilting Moment Point (ZMP). The key to our development is the generalization of ZMP support areas to take into account (a) frictional constraints and (b) multiple non-coplanar contacts. We introduce and investigate two kinds of ZMP support areas. First, we characterize and provide a fast geometric construction for the support area generated by valid contact forces, with no other constraint on the robot motion. We call this set the full support area. Next, we consider the control of humanoid robots using the Linear Pendulum Mode (LPM). We observe that the constraints stemming from the LPM induce a shrinking of the support area, even for walking on horizontal floors. We propose an algorithm to compute the new area, which we call pendular support area. We show that, in the LPM, having the ZMP in the pendular support area is a necessary and sufficient condition for contact stability. Based on these developments, we implement a whole-body controller and generate feasible multi-contact motions where an HRP-4 humanoid locomotes in challenging multi-contact scenarios

Motor Control For Human Jumping To A Target

Investigating how humans perform dynamic movements is important for applications such as movement rehabilitation, sports training, humanoid robot design and control, and human-robot interaction. There are several hypotheses as to how humans perform dynamic movements based on movement variability, task optimization, and motor learning concepts. This thesis develops a methodology for analyzing dynamic movements, determining what factors are crucial to task success, and understanding the motor learning process. The jumping to a target movement was chosen as the exemplar motion for investigating human dynamic motor control because of the following reasons: the movement difficulty can be scaled to a person's physical characteristics and ability; jumping to a target is a movement that many people can perform but few have practiced, making it a good candidate for investigating motor learning; jumping to target has a clear metric for success, enabling novice-expert classification of participants based on objective task performance. Additionally, existing human jumping research has focused primarily on maximum height vertical jumping or maximum distance long jumping. This thesis is the first known work to investigate the kinematics and motor control of the standing broad jump to a target. An experiment was conducted to collect motion capture data of 22 participants (ages 19-34 years, 9 females and 13 males), each performing 12 jumps to three specified targets of various distances. These motion capture data were used with Extended Kalman Filter pose estimation to extract the kinematic joint trajectories of each jump, and the center of mass (CoM) trajectories were then computed. Analysis of these trajectories then proceeded in two stages. A kinematic trajectory analysis was performed to identify trends between the jumping trajectories and jump success. The identified trends, and other information found in the literature, were used to generate hypotheses for using a sliding window Inverse Optimal Control (IOC) approach for identifying optimized motor control tasks. The findings from the kinematic trajectory analysis of jumping motion trajectories suggest a strong relation between the jumper controlling the velocity of their CoM at takeoff and the success of the jump. The angle and magnitude of the takeoff velocity must be matched to generate an appropriate ballistic trajectory to reach the desired target. At landing, the jumper can use their foot placement pose to correct for inaccuracy in their takeoff velocity and CoM trajectory to still land on the target. Novice jumpers demonstrated more consistent CoM takeoff velocities as they performed more jumps, however it was less likely that their foot placement control improved noticeably during the study. Expert jumpers were observed to control their foot placement pose more effectively, therefore making higher jumping success rates possible even when the variability of their CoM takeoff velocity was greater than some novice jumpers. A sliding window IOC approach was used to estimate what motor control tasks jumpers optimize throughout the movement. The cost terms of the objective function were designed based on jumping-specific control tasks and criteria relevant to general human motion. The recovered IOC cost term weights were averaged over different sets of jump features. Changes in average cost term weights were observed relative to jump grade, target distance, and jump performance. Experts were observed to optimize CoM forward velocity before takeoff more than novice jumpers, who optimized CoM height more. As novice jumpers improved their success rate during the experiment, their motor control behavior more closely resembled that of experts. The IOC approach demonstrates evidence for a repeatable, general optimal motor control method for jumping to a target. Parallels were also drawn between the kinematic trajectory results and IOC motor control task results. Optimizing for the CoM velocity control task before takeoff and toe velocity control task prior to landing, as identified in the IOC results, can be related to controlling takeoff velocity and foot placement pose respectively, as observed in the kinematic analysis. Finally, the IOC sliding window approach was used alongside unsupervised clustering techniques to identify four jump styles into which experiment participants could be categorized into. All style groups included novice and expert jumpers, and were independent of jump success or motor learning, suggesting there are multiple general motor control patterns that can be used for successfully jumping to a target. This analysis framework can be extended to analyzing jumping motions in varied environment conditions, or be used to define the motor control methods of other dynamic human motions

運動計画をフィードバックループに含むヒューマノイドロボットの多点接触全身制御のための計算基盤

学位の種別: 課程博士審査委員会委員 : （主査）東京大学教授 中村 仁彦, 東京大学教授 下山 勲, 東京大学教授 稲葉 雅幸, 東京大学教授 國吉 康夫, 東京大学准教授 高野 渉, LAAS-CNRSSenior Researcher LAUMOND Jean-PaulUniversity of Tokyo(東京大学

Planification de pas pour robots humanoïdes : approches discrètes et continues

Dans cette thèse nous nous intéressons à deux types d'approches pour la planification de pas pour robots humanoïdes : d'une part les approches discrètes où le robot n'a qu'un nombre fini de pas possibles, et d'autre part les approches où le robot se base sur des zones de faisabilité continues. Nous étudions ces problèmes à la fois du point de vue théorique et pratique. En particulier nous décrivons deux méthodes originales, cohérentes et efficaces pour la planification de pas, l'une dans le cas discret (chapitre 5) et l'autre dans le cas continu (chapitre 6). Nous validons ces méthodes en simulation ainsi qu'avec plusieurs expériences sur le robot HRP-2. ABSTRACT : In this thesis we investigate two types of approaches for footstep planning for humanoid robots: on one hand the discrete approaches where the robot has only a finite set of possible steps, and on the other hand the approaches where the robot uses continuous feasibility regions. We study these problems both on a theoretical and practical level. In particular, we describe two original, coherent and efficient methods for footstep planning, one in the discrete case (chapter 5), and one in the continuous case (chapter 6). We validate these methods in simulation and with several experiments on the robot HRP-2