Selecting the right actuator for a portable exoskeleton involves a comprehensive evaluation of various design characteristics. In this study, we introduce a methodology for actuator selection based on specific tasks, enhancing the practical adoption of portable exoskeletons. By examining a range of candidate actuators designed for lower limb exoskeletons, our objective is to engineer a system that is both lightweight and power-efficient. These candidate actuators, developed by integrating diverse motors and transmission systems, were rigorously tested against defined tasks. Our methodology, applied to an assistive exoskeleton catered to the elderly, showed its potential in tailoring an efficient system with matching capabilities. The obtained results indicated that the ideal configuration achieved reductions in weight and power requirements by 35% and 80%, respectively. The present research delineates a strategic approach for actuator selection in portable exoskeletons, contributing to the evolution of high-performing assistive devices.

A novel framework for probabilistic-based structural assessment of existing structures, which combines model identification and reliability assessment procedures, considering in an objective way different sources of uncertainty, is presented in this paper. A short description of structural assessment applications, provided in literature, is initially given. Then, the developed model identification procedure, supported in a robust optimization algorithm, is presented. Special attention is given to both experimental and numerical errors, to be considered in this algorithm convergence criterion. An updated numerical model is obtained from this process. The reliability assessment procedure, which considers a probabilistic model for the structure in analysis, is then introduced, incorporating the results of the model identification procedure. The developed model is then updated, as new data is acquired, through a Bayesian inference algorithm, explicitly addressing statistical uncertainty. Finally, the developed framework is validated with a set of reinforced concrete beams, which were loaded up to failure in laboratory.

The problem of tuning nonlinear dynamical systems parameters, such that the attained results are considered good ones, is a relevant one. This article describes the development of a gait optimization system that allows a fast but stable robot quadruped crawl gait. We combine bio-inspired Central Patterns Generators (CPGs) and Genetic Algorithms (GA). CPGs are modelled as autonomous differential equations, that generate the necessar y limb movement to perform the required walking gait. The GA finds parameterizations of the CPGs parameters which attain good gaits in terms of speed, vibration and stability. Moreover, two constraint handling techniques based on tournament selection and repairing mechanism are embedded in the GA to solve the proposed constrained optimization problem and make the search more efficient. The experimental results, performed on a simulated Aibo robot, demonstrate that our approach allows low vibration with a high velocity and wide stability margin for a quadruped slow crawl gait.