Feedback Control of an Exoskeleton for Paraplegics: Toward Robustly Stable Hands-free Dynamic Walking

This manuscript presents control of a high-DOF fully actuated lower-limb exoskeleton for paraplegic individuals. The key novelty is the ability for the user to walk without the use of crutches or other external means of stabilization. We harness the power of modern optimization techniques and supervised machine learning to develop a smooth feedback control policy that provides robust velocity regulation and perturbation rejection. Preliminary evaluation of the stability and robustness of the proposed approach is demonstrated through the Gazebo simulation environment. In addition, preliminary experimental results with (complete) paraplegic individuals are included for the previous version of the controller.Comment: Submitted to IEEE Control System Magazine. This version addresses reviewers' concerns about the robustness of the algorithm and the motivation for using such exoskeleton

Human Activity Recognition and Control of Wearable Robots

abstract: Wearable robotics has gained huge popularity in recent years due to its wide applications in rehabilitation, military, and industrial fields. The weakness of the skeletal muscles in the aging population and neurological injuries such as stroke and spinal cord injuries seriously limit the abilities of these individuals to perform daily activities. Therefore, there is an increasing attention in the development of wearable robots to assist the elderly and patients with disabilities for motion assistance and rehabilitation. In military and industrial sectors, wearable robots can increase the productivity of workers and soldiers. It is important for the wearable robots to maintain smooth interaction with the user while evolving in complex environments with minimum effort from the user. Therefore, the recognition of the user's activities such as walking or jogging in real time becomes essential to provide appropriate assistance based on the activity. This dissertation proposes two real-time human activity recognition algorithms intelligent fuzzy inference (IFI) algorithm and Amplitude omega ($A \omega$) algorithm to identify the human activities, i.e., stationary and locomotion activities. The IFI algorithm uses knee angle and ground contact forces (GCFs) measurements from four inertial measurement units (IMUs) and a pair of smart shoes. Whereas, the $A \omega$ algorithm is based on thigh angle measurements from a single IMU. This dissertation also attempts to address the problem of online tuning of virtual impedance for an assistive robot based on real-time gait and activity measurement data to personalize the assistance for different users. An automatic impedance tuning (AIT) approach is presented for a knee assistive device (KAD) in which the IFI algorithm is used for real-time activity measurements. This dissertation also proposes an adaptive oscillator method known as amplitude omega adaptive oscillator ($A\omega AO$) method for HeSA (hip exoskeleton for superior augmentation) to provide bilateral hip assistance during human locomotion activities. The $A \omega$ algorithm is integrated into the adaptive oscillator method to make the approach robust for different locomotion activities. Experiments are performed on healthy subjects to validate the efficacy of the human activities recognition algorithms and control strategies proposed in this dissertation. Both the activity recognition algorithms exhibited higher classification accuracy with less update time. The results of AIT demonstrated that the KAD assistive torque was smoother and EMG signal of Vastus Medialis is reduced, compared to constant impedance and finite state machine approaches. The $A\omega AO$ method showed real-time learning of the locomotion activities signals for three healthy subjects while wearing HeSA. To understand the influence of the assistive devices on the inherent dynamic gait stability of the human, stability analysis is performed. For this, the stability metrics derived from dynamical systems theory are used to evaluate unilateral knee assistance applied to the healthy participants.Dissertation/ThesisDoctoral Dissertation Aerospace Engineering 201

Reactive Stepping for Humanoid Robots using Reinforcement Learning: Application to Standing Push Recovery on the Exoskeleton Atalante

State-of-the-art reinforcement learning is now able to learn versatile locomotion, balancing and push-recovery capabilities for bipedal robots in simulation. Yet, the reality gap has mostly been overlooked and the simulated results hardly transfer to real hardware. Either it is unsuccessful in practice because the physics is over-simplified and hardware limitations are ignored, or regularity is not guaranteed, and unexpected hazardous motions can occur. This paper presents a reinforcement learning framework capable of learning robust standing push recovery for bipedal robots that smoothly transfer to reality, providing only instantaneous proprioceptive observations. By combining original termination conditions and policy smoothness conditioning, we achieve stable learning, sim-to-real transfer and safety using a policy without memory nor explicit history. Reward engineering is then used to give insights into how to keep balance. We demonstrate its performance in reality on the lower-limb medical exoskeleton Atalante

A Scalable Safety Critical Control Framework for Nonlinear Systems

There are two main approaches to safety-critical control. The first one relies on computation of control invariant sets and is presented in the first part of this work. The second approach draws from the topic of optimal control and relies on the ability to realize Model-Predictive-Controllers online to guarantee the safety of a system. In the second approach, safety is ensured at a planning stage by solving the control problem subject for some explicitly defined constraints on the state and control input. Both approaches have distinct advantages but also major drawbacks that hinder their practical effectiveness, namely scalability for the first one and computational complexity for the second. We therefore present an approach that draws from the advantages of both approaches to deliver efficient and scalable methods of ensuring safety for nonlinear dynamical systems. In particular, we show that identifying a backup control law that stabilizes the system is in fact sufficient to exploit some of the set-invariance conditions presented in the first part of this work. Indeed, one only needs to be able to numerically integrate the closed-loop dynamics of the system over a finite horizon under this backup law to compute all the information necessary for evaluating the regulation map and enforcing safety. The effect of relaxing the stabilization requirements of the backup law is also studied, and weaker but more practical safety guarantees are brought forward. We then explore the relationship between the optimality of the backup law and how conservative the resulting safety filter is. Finally, methods of selecting a safe input with varying levels of trade-off between conservatism and computational complexity are proposed and illustrated on multiple robotic systems, namely: a two-wheeled inverted pendulum (Segway), an industrial manipulator, a quadrotor, and a lower body exoskeleton

Robotics 2010

Without a doubt, robotics has made an incredible progress over the last decades. The vision of developing, designing and creating technical systems that help humans to achieve hard and complex tasks, has intelligently led to an incredible variety of solutions. There are barely technical fields that could exhibit more interdisciplinary interconnections like robotics. This fact is generated by highly complex challenges imposed by robotic systems, especially the requirement on intelligent and autonomous operation. This book tries to give an insight into the evolutionary process that takes place in robotics. It provides articles covering a wide range of this exciting area. The progress of technical challenges and concepts may illuminate the relationship between developments that seem to be completely different at first sight. The robotics remains an exciting scientific and engineering field. The community looks optimistically ahead and also looks forward for the future challenges and new development

Brain activity on encoding different textures EEG signal acquisition with ExoAtlet®

Powered exoskeletons play a crucial role in the rehabilitation field improving the quality of life for those who need them. Thus, being a major contribution for patients integration into society, providing them with more autonomy and freedom. In spite of these positive outcomes, a thorough description of the brain correlates connected to exoskeleton control is still needed. For instance, the perception of different pavement textures when wearing an exoskeleton is probably going to cause changes in cerebral activity, which could impact both sensory encoding and Brain-Computer Interface (BCI) control. Therefore, the main goal of this work is to describe the brain activity response to different textured pavements using ExoAtlet ® powered exoskeleton. In order to measure, process, analyze and classify the impact of different textures on neurophysiological rhythms, 4-minute signals were recorded by Electroencephalogram (EEG) with a 16-channel cap (actiCAP by Brain Products). Each of the three experimental subjects was instructed to walk in place on four different types of pavement (flat, carpet, foam, and rubber circles) with and without the exoskeleton, for a total of eight different experimental conditions. A counterbalanced design was applied, and informed consent was obtained from participants (Committee for Health Sciences of the Universidade Católica Portuguesa - 99/2022). Additionally, four machine learning methods, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Linear Discriminant Analysis (LDA), and Artificial Neural Network (ANN), were selected in order to analyze three distinct classification problems. This study found that there were changes associated with the delta frequency band for electrodes C3 and C4, and when comparing the classifiers performance, LDA presented the best accuracy across the three classification problems involving all subjects. Thereby, this work concludes that the results are consistent with the hypothesis that sensory processing of pavement textures during exoskeleton control induces neural changes and delta variations of the C3 and C4 electrodes. Additionally, LDA demonstrated the best performance across the three classifications of subject-independent problems.Os exoesqueletos motorizados desempenham um papel crucial no campo da reabilitação, melhorando a qualidade de vida das pessoas que deles necessitam. Deste modo, são um contributo importante para que os pacientes com condições físicas limitadas sejam mais facilmente integrados na sociedade, proporcionando-lhes mais autonomia e liberdade. Embora esta tecnologia tenha os seus aspetos positivos, ainda existe a necessidade de descrever os correlatos cerebrais direcionados para o controlo do exoesqueleto. Por exemplo, a percepção de diferentes pavimentos quando se usa um exoesqueleto vai provavelmente causar alterações na actividade cerebral, o que pode ter impacto tanto na codificação sensorial como no controlo da interface cérebro-máquina (BCI). Deste modo, o principal objetivo deste trabalho é descrever a atividade cerebral às diferentes texturas dos pavimentos, utilizando o exoesqueleto ExoAtlet ®. A fim de medir, processar, analisar e classificar o impacto de diferentes texturas em ritmos neurofisiológicos, foram registados sinais de 4 minutos atravês the Eletroencefalograma (EEG) com uma touca de 16 canais (actiCAP by Brain Products). Cada um dos três voluntários foi instruído a dar passos no lugar em quatro tipos diferentes de pavimento (plano, alcatifa, espuma, e círculos de borracha) com e sem o exosqueleto, num total de oito condições experimentais diferentes. Foi aplicado um desenho contrabalançado e foi obtido o consentimento informado dos participantes (Comissão para as Ciências da Saúde da Universidade Católica Portuguesa - 99/2022). Adicionalmente, foram selecionados quatro classificadores: máquinas de vetores de suporte (SVM), k-vizinhos mais próximos (KNN), análise discriminante linear (LDA) e redes neuronais artificiais (ANN) para analisar três problemas de classificação distintos. Os resultados obtidos por este estudo demonstraram que existiam alterações associadas à banda de frequência delta para os eléctrodos C3 e C4 e, ao comparar o desempenho dos classificadores, o LDA apresentou a melhor exatidão nos três problemas de classificação envolvendo todos os sujeitos. Assim, estes resultados são consistentes com a hipótese de que o processamento sensorial dos pavimentos durante o controlo do exoesqueleto induz alterações neuronais