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®).

Dynamic locomotion synchronization of bipedal robot and human operator via bilateral feedback teleoperation

2019 ◽  
Vol 4 (35) ◽  
pp. eaav4282 ◽  
Author(s):  
Joao Ramos ◽  
Sangbae Kim
Keyword(s):  
Sagittal Plane ◽  
Frontal Plane ◽  
Humanoid Robots ◽  
Human Locomotion ◽  
Human Motion ◽  
Body Motion ◽  
Human Operator ◽  
Whole Body ◽  
Physical Endurance ◽  
Bipedal Robot

Despite remarkable progress in artificial intelligence, autonomous humanoid robots are still far from matching human-level manipulation and locomotion proficiency in real applications. Proficient robots would be ideal first responders to dangerous scenarios such as natural or man-made disasters. When handling these situations, robots must be capable of navigating highly unstructured terrain and dexterously interacting with objects designed for human workers. To create humanoid machines with human-level motor skills, in this work, we use whole-body teleoperation to leverage human control intelligence to command the locomotion of a bipedal robot. The challenge of this strategy lies in properly mapping human body motion to the machine while simultaneously informing the operator how closely the robot is reproducing the movement. Therefore, we propose a solution for this bilateral feedback policy to control a bipedal robot to take steps, jump, and walk in synchrony with a human operator. Such dynamic synchronization was achieved by (i) scaling the core components of human locomotion data to robot proportions in real time and (ii) applying feedback forces to the operator that are proportional to the relative velocity between human and robot. Human motion was sped up to match a faster robot, or drag was generated to synchronize the operator with a slower robot. Here, we focused on the frontal plane dynamics and stabilized the robot in the sagittal plane using an external gantry. These results represent a fundamental solution to seamlessly combine human innate motor control proficiency with the physical endurance and strength of humanoid robots.

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.

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.

Coordinated Motion Planning of Legged Mobile Manipulator for Tracking the Given End-Effector’s Trajectory

2019 ◽  
Author(s):  
Kondalarao Bhavanibhatla ◽  
Sulthan Suresh-Fazeela ◽  
Dilip Kumar Pratihar
Keyword(s):  
Motion Planning ◽  
Degrees Of Freedom ◽  
Robotic System ◽  
Simulation Software ◽  
Body Motion ◽  
Whole Body ◽  
Mobile Manipulator ◽  
Coordinated Motion ◽  
Multibody Dynamic ◽  
The Given

Abstract In this paper, a novel algorithm is presented to achieve the coordinated motion planning of a Legged Mobile Manipulator (LMM) for tracking the given end-effector’s trajectory. LMM robotic system can be obtained by mounting a manipulator on the top of a multi-legged platform for achieving the capabilities of both manipulation and mobility. To exploit the advantages of these capabilities, the manipulator should be able to accomplish the task, while the hexapod platform moves simultaneously. In the presented approach, the whole-body motion planning is achieved in two steps. In the first step, the robotic system is assumed to be a mobile manipulator, in which the manipulator has two additional translational degrees of freedom at the base. The redundancy of this robotic system is solved by treating it as an optimization problem. Then, in the second step, the omnidirectional motion of the legged platform is achieved with a combination of straight forward and crab motions. The proposed algorithm is tested through a numerical simulation in MATLAB and then, validated on a virtual model of the robot using multibody dynamic simulation software, MSC ADAMS. Multiple trajectories of the end-effector have been tested and the results show that the proposed algorithm accomplishes the given task successfully by providing a singularity-free whole-body motion.

Computer Simulation of Lower Extremities Movement during Stair Climbing

2013 ◽  
Vol 330 ◽  
pp. 407-411 ◽  
Author(s):  
Vesna Raspudić
Keyword(s):  
Human Body ◽  
Human Locomotion ◽  
Great Difficulty ◽  
Human Motion ◽  
Body Motion ◽  
Metabolic Energy ◽  
Stair Climbing ◽  
Interactive Methods ◽  
Limb Amputation ◽  
Magnetic Markers

Tracking of human body motion is applied in many fields, such as virtual reality, clinical biomechanics, the study of man-machine-environment relationship, the analysis of sports movements, etc. Nowadays, the preferred approach to tracking human body motion is based on the use of appropriate optical or magnetic markers, which are placed on specific landmark points, and real-time estimating of their spatial coordinates. With the improvements introduced in computerized monitoring of human motion kinematics, it is important to emphasize the significance of combining motion capture data with commercial CAD packages. The aim of this research was to develop new interactive methods in creating virtual models within the highly sophisticated CAD computer technologies, as well as computer simulations for analyzing the various forms of human locomotion. Within this research, special attention is focused on the study of locomotion when climbing stairs, as an activity that requires large amount of metabolic energy, and thus represents great difficulty in performing daily activities for people with disorders of the musculoskeletal system, and particularly for people with lower limb amputation.

Whole-Body Human-to-Humanoid Motion Imitation

2013 ◽  
Vol 479-480 ◽  
pp. 617-621
Author(s):  
Hsien I Lin ◽  
Zan Sheng Chen
Keyword(s):  
Degrees Of Freedom ◽  
Humanoid Robot ◽  
Optimal Solution ◽  
Human Motion ◽  
Body Motion ◽  
Upper Body ◽  
Whole Body ◽  
Nao Robot ◽  
Novel Approach ◽  
Intuitive Method

Human-to-Humanoid motion imitation is an intuitive method to teach a humanoid robot how to act by human demonstration. For example, teaching a robot how to stand is simply showing the robot how a human stands. Much of previous work in motion imitation focuses on either upper-body or lower-body motion imitation. In this paper, we propose a novel approach to imitate human whole-body motion by a humanoid robot. The main problem of the proposed work is how to control robot balance and keep the robot motion as similar as taught human motion simultaneously. Thus, we propose a balance criterion to assess how well the root can balance and use the criterion and a genetic algorithm to search a sub-optimal solution, making the root balanced and its motion similar to human motion. We have validated the proposed work on an Aldebaran Robotics NAO robot with 25 degrees of freedom. The experimental results show that the root can imitate human postures and autonomously keep itself balanced.

scholarly journals Wearable Sensors for Monitoring Human Motion: A Review on Mechanisms, Materials, and Challenges

2019 ◽  
Vol 25 (1) ◽  
pp. 9-24 ◽  
Author(s):  
S. Zohreh Homayounfar ◽  
Trisha L. Andrew
Keyword(s):  
Large Scale ◽  
Large Range ◽  
State Of The Art ◽  
Wearable Sensors ◽  
Human Locomotion ◽  
Human Motion ◽  
Human Gait ◽  
Wearable Electronics ◽  
New Horizons ◽  
User Friendly

The emergence of flexible wearable electronics as a new platform for accurate, unobtrusive, user-friendly, and longitudinal sensing has opened new horizons for personalized assistive tools for monitoring human locomotion and physiological signals. Herein, we survey recent advances in methodologies and materials involved in unobtrusively sensing a medium to large range of applied pressures and motions, such as those encountered in large-scale body and limb movements or posture detection. We discuss three commonly used methodologies in human gait studies: inertial, optical, and angular sensors. Next, we survey the various kinds of electromechanical devices (piezoresistive, piezoelectric, capacitive, triboelectric, and transistive) that are incorporated into these sensor systems; define the key metrics used to quantitate, compare, and optimize the efficiency of these technologies; and highlight state-of-the-art examples. In the end, we provide the readers with guidelines and perspectives to address the current challenges of the field.

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