A Knee-Based Multi-objective Optimization for Gait Cycle of 25-DOF NAO Humanoid Robot

A multi-objective optimization problem finds multiple optimal solutions represented on a Pareto front (PF), for conflicting objectives. Focusing on the “knee” region (KR) of the PF is preferred to targeting the entire PF since there is a significant degradation in one objective for a minor gain in another outside the KR. This paper applies two knee-finding methodologies—angle- and utility-based methods within the elitist non-dominated sorting genetic algorithm (NSGA-II), to address a multi-objective optimization problem of a 25-DOF NAO humanoid robot’s gait cycle. The objectives are minimizing power consumption and maximizing dynamic balance margin. The single support phase exhibits a single KR, whereas the double support phase shows two KRs. This research demonstrates a knee-based multi-objective optimization algorithm to reduce the burden on decision-makers in selecting the most preferred solutions. It compares two knee-finding techniques and provides insights into a practical robotics problem for different gait cycle phases.