PCA-Based Algorithms to Find Synergies for Humanoid Robot Motion Behavior

Abstract

Applying principal component analysis (PCA) to find synergy signal for specific motion of Robot is a standard method. However, implementation of PCA gives synergy solely on quantitative basis. The algorithms proposed in this paper advocates the enhancement of qualitative measure of PCA to locate well-coordinated synergy signals. The two main control strategies of central nervous system (CNS) are taken into account for enhancement of algorithms. First one is the CNS strategy of separate synergy generation for individual limbs and second is the trajectory generation of complex movement using via-points. The proposed algorithms find the synergy without loss of generality of implementation. Humanoid robot NAO is used as a robotic platform to test the result of the algorithm. The synergy for a group of motors is calculated by implementing the algorithm on motors position sensor data of the robot corresponding to three motion pattern 1. Knee bend sitting–standing, 2. Sitting–standing on chair, and 3. Walking. The improvement in result is statistically measured by calculating error between original and reconstructed signal for proposed algorithms and applying Z-test tested on error signals. Another statistical measure of improvement is treated by calculating ‘Goodness of Fit’ for original and reconstructed signal.