Optimal Design of a Walking Robot: Analytical, Numerical, and Machine Learning Methods for Multicriteria Synthesis

TL;DR

This paper presents a comprehensive approach to designing an efficient walking robot by combining analytical, numerical, and machine learning methods for multicriteria optimization, prototype validation, and innovative structural design.

Contribution

It introduces a novel 'rational' mechanical structure and a multicriteria synthesis framework, including a genetic algorithm and isotropy criterion, for optimized walking robot design.

Findings

Development of a new mechanical structure for efficiency

Implementation of multicriteria synthesis methods

Experimental validation with prototypes and LiDAR navigation

Abstract

This paper addresses several critical stages of designing a walking robot, including optimal structural synthesis, introducing a novel 'rational' mechanical structure aimed at enhancing efficiency and simplifying control system, while addressing practical limitations observed in existing designs. The study includes development of novel multicriteria synthesis methods for achieving optimal leg design, integrating analytical and numerical methods. In addition, a method based on Non-dominated Sorting Genetic Algorithm II is presented. Turning modes are investigated, and for the first time, the isotropy criterion, typically applied to parallel manipulators, is used for optimizing walking robot parameters to ensure optimal force and motion transfer in all directions. Several physical prototypes are developed to experimentally validate the functionality of different mechanisms of the robot, including adaptation to the surface irregularities and navigation using LiDAR.