OPTIMAL LEG LINKAGE DESIGN FOR HORIZONTAL PROPEL OF A WALKING ROBOT USING NON-DOMINATED SORTING GENETIC ALGORITHM

Abstract

In the ever-evolving domain of robotic locomotion, leg linkage design emerges not just as an intricate engineering puzzle, but also as a decisive element in realizing optimal horizontal propulsion. The research meticulously interrogates this pivotal concern, endeavouring to harmonize multiple design objectives that traditionally exist in tension. Leveraging the robust computational prowess of the non-dominated Sorting Genetic Algorithm (NSGA) for multiobjective optimization, this study orchestrates a deliberate foray into the expansive and complex design space. The overarching aim is not merely to pinpoint a singular, universal design zenith, but to painstakingly chart a continuum of Pareto-optimal solutions, thereby accommodating the myriad, often contradictory, imperatives that animate robotic design from the quest for energy efficiency to the pursuit of agility, speed, and robust structural integrity. This methodology yields a rich tapestry of insights: notable among them is the discernible predilection of specific linkage configurations towards distinct performance outcomes. While certain geometries resonate more profoundly with rapid, fluid motion, others evince a marked inclination towards stability or frugal energy consumption. By dissecting these intricate relationships, and presenting them within a structured framework, this study contributes profoundly to the literature, offering both theoretical depth and pragmatic design templates to the robotics community. This synergistic marriage of computational algorithms with nuanced design challenges holds the promise to significantly recalibrate and enhance contemporary paradigms in leg linkage design for horizontally propelling robots. This study marks a significant advancement in robotic locomotion by employing the Non-dominated Sorting Genetic Algorithm (NSGA) for the first time in the optimization of leg linkage design for walking robots, providing a more nuanced understanding of the balance between structural integrity, energy efficiency, and propulsion agility. Our research elucidates a spectrum of Pareto optimal solutions, a novel approach that offers a comprehensive understanding of the trade-offs involved in leg linkage design. Specifically, the optimized designs achieved an improvement in propulsion efficiency by reducing the approximation error to less than 0.006, and enhancing force transmission angles to over 25 degrees. These experimental results validate the practical applicability of these designs, demonstrating a balance of improved efficiency and stability, thereby setting a new benchmark for leg linkage design in walking robots. The findings underscore the potential of NSGA in robotic design, offering a robust framework for future advancements in the field.