WHOLE-BODY MOTION GENERATION OF ANDROID ROBOT USING MOTION CAPTURE AND NONLINEAR CONSTRAINED OPTIMIZATION

2013 ◽  
Vol 10 (02) ◽  
pp. 1350003 ◽  
Author(s):  
JUNG-YUP KIM ◽  
YOUNG-SEOG KIM
Keyword(s):  
Constrained Optimization ◽  
Motion Capture ◽  
Optimization Technique ◽  
Human Motion ◽  
Body Motion ◽  
Whole Body ◽  
Motion Generation ◽  
Nonlinear Constrained Optimization ◽  
Gimbal Lock ◽  
Android Robot

This paper describes a whole-body motion generation scheme for an android robot using motion capture and an optimization method. Android robots basically require human-like motions due to their human-like appearances. However, they have various limitations on joint angle, and joint velocity as well as different numbers of joints and dimensions compared to humans. Because of these limitations and differences, one appropriate approach is to use an optimization technique for the motion capture data. Another important issue in whole-body motion generation is the gimbal lock problem, where a degree of freedom at the three-DOF shoulder disappears. Since the gimbal lock causes two DOFs at the shoulder joint diverge, a simple and effective strategy is required to avoid the divergence. Therefore, we propose a novel algorithm using nonlinear constrained optimization with special cost functions to cope with the aforementioned problems. To verify our algorithm, we chose a fast boxing motion that has a large range of motion and frequent gimbal lock situations as well as dynamic stepping motions. We then successfully obtained a suitable boxing motion very similar to captured human motion and also derived a zero moment point (ZMP) trajectory that is realizable for a given android robot model. Finally, quantitative and qualitative evaluations in terms of kinematics and dynamics are carried out for the derived android boxing motion.

From human motion capture to humanoid locomotion imitation Application to the robots HRP-2 and HOAP-3

Robotica ◽  
2010 ◽  
Vol 29 (2) ◽  
pp. 325-334 ◽  
Author(s):  
Luc Boutin ◽  
Antoine Eon ◽  
Said Zeghloul ◽  
Patrick Lacouture
Keyword(s):  
Motion Capture ◽  
Human Locomotion ◽  
Inverse Kinematic ◽  
Human Motion ◽  
Simulation Software ◽  
Body Motion ◽  
Whole Body ◽  
Human Gait ◽  
Reference Trajectory ◽  
Double Support

SUMMARYThis paper presents a method to generate humanoid gaits from a human locomotion pattern recorded by a motion capture system. Thirty seven reflective markers were fixed on the human subject skin in order to get the subject whole body motion. To reproduce the human gait, especially the toes and heel contacts, the front and back edges of the robot's feet are used as support at the start and the end of the double support phase. The balance of the robot is respected using the zero moment point (ZMP) criterion and confirmed by the simulation software OPENHRP (General Robotics, Inc®). First, the feet trajectory as well as the ZMP reference trajectory are defined from the motion of the robot controlled as a marionette with the measured human joint angles. Then a specific inverse kinematic (IK) algorithm is proposed to find the humanoid robot's joint trajectories respecting the constraints of balance, floor contacts, and joint limits. The studied motion presented in this paper is a human walking trajectory containing a start, a movement in a straight line, a stop, and a quarter turn. The method was developed to be easily used for human-like robots of different sizes, masses, and structures and has been tested on the robot HRP-2 (AIST, Kawada Industries, Inc®) and on the small-sized humanoid robot HOAP-3 (Fujitsu Automation Ltd®).

A Mobile Motion Capture System Based on Inertial Sensors and Smart Shoes

2013 ◽  
Vol 136 (1) ◽  
Author(s):  
Pyeong-Gook Jung ◽  
Sehoon Oh ◽  
Gukchan Lim ◽  
Kyoungchul Kong
Keyword(s):  
Motion Capture ◽  
Inertial Sensors ◽  
Kinematic Model ◽  
Three Dimensional ◽  
Health Condition ◽  
Body Motion ◽  
Whole Body ◽  
Motion Capture System ◽  
Outdoor Sports ◽  
Motion Capture Systems

Motion capture systems play an important role in health-care and sport-training systems. In particular, there exists a great demand on a mobile motion capture system that enables people to monitor their health condition and to practice sport postures anywhere at any time. The motion capture systems with infrared or vision cameras, however, require a special setting, which hinders their application to a mobile system. In this paper, a mobile three-dimensional motion capture system is developed based on inertial sensors and smart shoes. Sensor signals are measured and processed by a mobile computer; thus, the proposed system enables the analysis and diagnosis of postures during outdoor sports, as well as indoor activities. The measured signals are transformed into quaternion to avoid the Gimbal lock effect. In order to improve the precision of the proposed motion capture system in an open and outdoor space, a frequency-adaptive sensor fusion method and a kinematic model are utilized to construct the whole body motion in real-time. The reference point is continuously updated by smart shoes that measure the ground reaction forces.

WHOLE-BODY COOPERATIVE BALANCED MOTION GENERATION FOR REACHING

2005 ◽  
Vol 02 (04) ◽  
pp. 437-457 ◽  
Author(s):  
KOICHI NISHIWAKI ◽  
MAMORU KUGA ◽  
SATOSHI KAGAMI ◽  
MASAYUKI INABA ◽  
HIROCHIKA INOUE
Keyword(s):  
Visual Information ◽  
Degrees Of Freedom ◽  
Dynamic Balance ◽  
Construction Method ◽  
Body Motion ◽  
Whole Body ◽  
Motion Generation ◽  
Final Goal ◽  
Posture Selection

This paper addresses a construction method of a system that realizes whole body reaching motion of humanoids. Humanoids have many redundant degrees of freedom for reaching, and even the base can be moved by making the robot step. Therefore, there are infinite final posture solutions for a final goal position of reaching, and there are also infinite solutions for reaching trajectories that realize a final reaching posture. It is, however, difficult to find an appropriate solution because of the constraint of dynamic balance, and relatively narrow movable range for each joint. We prepared basic postures heuristically, and a final reaching posture is generated by modifying one of them. Heuristics, such as the fact that kneeling down is suitable for reaching near the ground, can be implemented easily by using this method. Methods that compose the reaching system, that is, basic posture selection, modification of postures for generating final reaching postures, balance compensation, footstep planning to realize desired feet position, and generation and execution of whole body motion to final reaching postures are described. Reaching to manually set positions and picking up a bat at various postures using visual information are shown as experiments to show the performance of the system.

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