Many-objective robust gait optimization for a 25-DOFs NAO robot using NSGA-III
Abstract
Researchers increasingly employ evolutionary algorithms to tackle complex robotics problems owing to their ability to handle nonlinear dynamics. Robust solutions that remain stable despite variations in design parameters are essential for decision-makers. This study investigates Type I and Type II robustness approaches within constrained many-objective optimization (MaOO) frameworks, which are rarely explored in humanoid robotics. It focuses on optimizing the gait cycle of a 25-degrees-of-freedom (DOFs) NAO humanoid robot (NAO is an acronym for ‘Nao d'Amour’, also sometimes referred to as ‘Now, Autonomous and Operative’) during single- and double-support phases. The Non-dominated Sorting Genetic Algorithm III (NSGA-III) algorithm is employed to address four conflicting objectives, such as minimizing power consumption, maximizing stability, minimizing torque fluctuations, and reducing gait cycle time. A detailed comparative analysis highlights the superiority of Type II robustness, offering better-distributed and more convergent solutions in real-world scenarios with several constraints and variables. Furthermore, the study examines the influence of gait parameters on objectives, enhancing the understanding of humanoid robot dynamics and presenting a robust methodology for similar complex challenges.
Authors: Pushpendra Gupta, Dilip Kumar Pratihar, Kalyanmoy Deb
Published in: Engineering Optimization (2025)