Kinematic and dynamic performance analysis of artificial legged systems

Abstract

This paper studies the mechanical configuration and the periodic gaits of multi-legged locomotion systems based on its kinematic and dynamic models. The purpose is to determine the system performance during walking, and the best set of locomotion variables that minimize a set of optimization indices. In this perspective, two kinematic and four dynamic indices are formulated to quantitatively measure the performance of the walking robot. The kinematic indices consist of the perturbation analysis and the locomobility measure, and the dynamic performance indices of the walking robot locomotion are the mean absolute density of energy, the mean power density dispersion, the density of power lost and the mean force at the bodylegs interface. A set of model-based simulation experiments reveals the system configuration and the type of movements that lead to a better performance, for a specific locomotion mode, from the viewpoint of the proposed indices.

Optimization of Quadruped Robot Locomotion Gaits Through a Genetic Algorithm

“…For the fitness function is used the mean absolute density of energy per travelled distance E av [17]. This index is computed assuming that energy regeneration is not available by actuators doing negative work (by taking the absolute value of the power).…”

A literature review on the optimization of legged robots

“…The objective function consisted in the minimization of the energy consumption of the robot joint motors, estimated from the reaction forces foreseen on the robot feet. Silva and Machado (2008) studied the locomotion parameters of an hexapod robot using four performance measures computed through the dynamic model of the studied mechanism.…”

“…Silva and Machado (1997) for a biped, and Silva and Machado (2008) for an hexapod, used an approach based on the concepts of manipulability (Yoshikawa, 1990), called the locomobility analysis, to study the optimum locomotion parameters for a multi-legged robot.…”

Optimization of Hexapod Locomotion using Genetic Algorithms

“…Two global measures of the overall performance of the mechanism (in an average sense) were established. One index is inspired on the system dynamics {E av } and the other is based on the trajectory tracking errors {ε xyH } [15]. The performance optimization can be achieved through the separate minimization of each index or through the simultaneously minimization of both indices, applying a Pareto optimal front [16].…”