WALKING PATTERN MAPPING FROM IMPERFECT MOTION CAPTURE DATA ONTO BIPED HUMANOID ROBOTS

This paper proposes an efficient walking pattern mapping algorithm from motion capture data onto biped humanoid robots. Currently, the technology known as human motion capture is widely utilized to generate various humanlike motions in many applications, including robotics. An important thing is that several difficulties are associated with motion capture data. These include a data offset issue, noise, and drift problems due to measurement errors caused by imperfect camera calibration, and marker position. If a biped robot uses motion capture data without suitable post-processes, the walking motion of the robot will differ from an actual walking motion, and the Zero Moment Point (ZMP) will be asymmetrical and noisy, leading to unstable walking. A further difficulty exists in the walking pattern mapping process due to the different joint numbers, link sizes, and weights between a human and a robot. Although walking pattern mapping is suitable after addressing the above difficulties, a slip problem between the feet and the ground can continue to cause problems. To solve these difficulties efficiently, a Fourier fitting method is proposed in this research. Improvements of walking pattern and the ZMP trajectory are confirmed using the proposed method. Furthermore, a geometric mapping method is introduced to generate walking patterns for various biped robots while maintaining a degree of similarity to humans. By applying a no-slip constraint to the feet and modifying the joint angles through inverse kinematics, the slip problem is also solved. The effectiveness of the proposed algorithm is verified through computer simulations of two different biped robots that have different sizes, weights, walking cycles, and step lengths.