Exoskeleton-assisted locomotion: design, control and evaluation of wearable robotic devices

Abstract

Assistive robotic devices such as exoskeletons and prosthetic limbs have great potential as tools for both augmentation and rehabilitation. However, due to the complexity of controlling these devices, especially in unstructured environments where factors such as walking speed and incline can vary rapidly, it is uncommon to see exoskeletons outside of a clinical or research setting. Prostheses, whilst more common, are typically passive, which limits their ability to match the push off forces associated with healthy gait.

Motivated by modern techniques for controlling legged robots, this thesis motivates the pursuit of an optimisation-based approach to the control and design of exoskeletons. We identify a number of open problems within the field, namely (1) how to model the dynamic interaction between a human subject and an attached exoskeleton; (2) identifying the appropriate metric or combination of metrics to optimise for in exoskeleton-assisted locomotion; and (3) how to account for changes in human walking style induced by the presence of external assistive forces. This thesis details attempts to solve each of these problems.

We present a methodology for expressing human-exoskeleton system models as a combination of musculoskeletal models, exoskeleton inertial parameters and constraint forces. A specific human-exoskeleton model is detailed, along with a range of methods for modelling the interaction forces which occur at the attachment points between the human and exoskeleton agents. Experimental motion data is analysed using musculoskeletal modelling software (OpenSim) to quantify the effect that each of these interaction models, which represent various degrees of approximation, have on the resulting humanexoskeleton dynamics.

Applying exoskeleton assistance is inherently a shared control problem. The overall goal is not to achieve a prescribed motion at any cost, or to do so while minimising exoskeleton joint torques, but rather to enhance aspects of the assisted humans motions; for example, increasing energy efficiency or stability. Therefore, in order to optimise exoskeleton control patterns we must first consider what it means for the resultant gait patterns to be optimal, or even good. We present a detailed analysis of exoskeleton-assisted walking in healthy subjects, with a particular focus on identifying those metrics which are invariant to changes in walking condition (e.g. walking speed or incline). We posit that such metrics, which exhibit strong invariance properties, are good candidates for the objective function of an optimisation-based controller. Human walking strategies are unique and complex, and the problem of predicting the effect of exoskeleton assistance on a subjects gait pattern is a challenging one. In recent years, success has been had by methods which aim to learn suitable assistance strategies directly from a subject, via a process known as human-in-the-loop optimisation. We present a novel humanin- the-loop framework which utilises musculoskeletal modelling to make the learning process more time-efficient. Our method is evaluated on a number of subjects walking on a treadmill with exoskeleton assistance. In addition, we also explore how human-in-the-loop optimisation can be used to inform the design of exoskeletons to enhance their assistive capabilities.

Overall, these contributions represent a step towards enabling the wider usage of exoskeletons and other assistive robotic devices, which could lead to significant improvements to quality of life for many.