Walking pattern mapping algorithm using fourier fitting and geometric approach for biped humanoid robots

Currently, the technology known as human motion capture is widely utilized to generate human-like walking patterns in humanoid 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. To solve these difficulties efficiently, we propose an algorithm which uses the Fourier fitting and geometric mapping method. 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.