Toward a human-like biped robot gait: Biomechanical analysis of human locomotion recorded by Kinect-based Motion Capture system

This paper presents biomechanical analysis of human locomotion recorded by Motion Capture (MoCap) system based on four Kinect 2 sensors and iPi Soft markerless tracking and visualization technology. To analyze multi-depth sensor video recordings we utilize iPi Mocap Studio software and iPi Biomech Add-on plug-in, which provide us visual and biomechanical human gait data: linear and angular joint coordinates, velocity, acceleration, center of mass (CoM) position, skeleton and 3D point cloud. The final analysis was performed in MATLAB environment, calculating zero moment point (ZMP) and ground projection of the CoM (GCoM) trajectories from human body dynamics by considering human body as a single weight point. These were followed by GCoM and ZMP error estimation. The further objective of our research is to reproduce the obtained with our MoCap system human-like gait with Russian biped robot AR-601M.

[1]  Zhiwei Luo,et al.  Energy-Efficient and High-Speed Dynamic Biped Locomotion Based on Principle of Parametric Excitation , 2008, IEEE Transactions on Robotics.

[2]  Tomás Pajdla,et al.  3D with Kinect , 2011, 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops).

[3]  Jan Awrejcewicz,et al.  Analysis of stability of the human gait , 2015 .

[4]  Ambarish Goswami,et al.  Foot rotation indicator (FRI) point: a new gait planning tool to evaluate postural stability of biped robots , 1999, Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C).

[5]  Evgeni Magid,et al.  Toward a human-like locomotion: Modelling dynamically stable locomotion of an anthropomorphic robot in simulink environment , 2015, 2015 12th International Conference on Informatics in Control, Automation and Robotics (ICINCO).

[6]  Yoshihiko Nakamura,et al.  Making feasible walking motion of humanoid robots from human motion capture data , 1999, Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C).

[7]  Andrea Fossati,et al.  Consumer Depth Cameras for Computer Vision , 2013, Advances in Computer Vision and Pattern Recognition.

[8]  Sander Oude Elberink,et al.  Accuracy and Resolution of Kinect Depth Data for Indoor Mapping Applications , 2012, Sensors.

[9]  Fazel Naghdy,et al.  Motion capture in robotics review , 2009, 2009 IEEE International Conference on Control and Automation.

[10]  C. S. George Lee,et al.  Whole-body human-to-humanoid motion transfer , 2005, 5th IEEE-RAS International Conference on Humanoid Robots, 2005..

[11]  Kazuhito Yokoi,et al.  Biped walking pattern generation by using preview control of zero-moment point , 2003, 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422).

[12]  Miomir Vukobratovic,et al.  Zero-Moment Point - Thirty Five Years of its Life , 2004, Int. J. Humanoid Robotics.

[13]  Kasper Støy,et al.  Energy Efficiency of Robot Locomotion Increases Proportional to Weight , 2011, FET.