From bio-inspired locomotion models To controllers for lower-limb exoskeletons

Human locomotion shows fascinating abilities which are the results of the interplay between the environment, the biomechanics, the spinal cord, and modulation from higher control centers. How the different structures interact to generate meaningful behavior is an active field of research, and understanding the key principles underlying bipedal locomotion could have a strong impact and important implications in several fields related to both medicine and robotics, such as improved rehabilitation procedures, predicting surgery outcome or facilitated human-robot interaction.

In this context, the development of biologically relevant bipedal models that faithfully recapitulate human locomotion are urgently needed. Existing such bio-inspired models usually rely on one of the two following principles: the Central Pattern Generators (CPGs) and the reflexes. In the first part of the thesis, we present a method to introduce a CPGs as feedforward components in a feedback based (i.e. reflex) model of human walking, named neuromuscular model (NMM). The proposed strategy is based on the idea that, in a feedback driven system, the feedforward component can be viewed as a feedback predictor. We implement the feedback predictors using morph oscillators as abstract models of biological CPGs. Thanks to the intrinsic robustness inherited from the feedback pathways, modulation of CPGs network's frequency and amplitudes becomes possible, over a broad range, without affecting the overall walking stability. Furthermore, the modulation of the CPGs network's parameters allowed smooth and stable gait modulation (such as changes in speed and adaptation to increasing slope) suggesting that the idea of using feedback predictor as gait modulator can be extended to a large range of applications, highlighting the role biological CPGs could play on top of a reflex-based rhythmic movement.

Building on the NMM, we present, in the second part of the thesis, the implementations of the models as controllers on different ortheses and exoskeletons. Wearable devices designed to assist abnormal gaits require controllers that interact with the user in an intuitive and unobtrusive manner. Here, we rationalized that such a neuromuscular controller could be implemented based on the NMM models. The implementation of NMM model on a controller (NMC) was demonstrated for human healthy subject and was confirmed with experiment on SCI subjects with different devices. Overall, the bio-inspired NMCs successfully demonstrated remarkable versatility in generating gait patterns tuned to the subjects' dynamics and producing near-physiological gait at near-normative speeds. The positive SCI subject-machine interaction stemmed from replacing the subject's impaired function with dynamical virtual muscles that require few sensors.

These preliminary but auspicious results have important implications towards the exploitation of natural walking dynamics through understanding human biological behavior in the design of controllers for wearable devices that are amenable to various environmental conditions and promote intuitive and unobtrusive human-machine interaction.

An Adaptive Neuromuscular Controller for Assistive Lower-Limb Exoskeletons: A Preliminary Study on Subjects with Spinal Cord Injury

Tycho Jan Henricus Brug, Florin Dzeladini, Auke Ijspeert, Amy Roning Wu

Versatility is important for a wearable exoskeleton controller to be responsive to both the user and the environment. These characteristics are especially important for subjects with spinal cord injury (SCI), where active recruitment of their own neuromuscular system could promote motor recovery. Here we demonstrate the capability of a novel, biologically-inspired neuromuscular controller (NMC) which uses dynamical models of lower limb muscles to assist the gait of SCI subjects. Advantages of this controller include robustness, modularity, and adaptability. The controller requires very few inputs (i.e., joint angles, stance, and swing detection), can be decomposed into relevant control modules (e.g., only knee or hip control), and can generate walking at different speeds and terrains in simulation. We performed a preliminary evaluation of this controller on a lower-limb knee and hip robotic gait trainer with seven subjects (N = 7, four with complete paraplegia, two incomplete, one healthy) to determine if the NMC could enable normal-like walking. During the experiment, SCI subjects walked with body weight support on a treadmill and could use the handrails. With controller assistance, subjects were able to walk at fast walking speeds for ambulatory SCI subjects-from 0.6 to 1.4 m/s. Measured joint angles and NMC-provided joint torques agreed reasonably well with kinematics and biological joint torques of a healthy subject in shod walking. Some differences were found between the torques, such as the lack of knee flexion near mid-stance, but joint angle trajectories did not seem greatly affected. The NMC also adjusted its torque output to provide more joint work at faster speeds and thus greater joint angles and step length. We also found that the optimal speed-step length curve observed in healthy humans emerged for most of the subjects, albeit with relatively longer step length at faster speeds. Therefore, with very few sensors and no predefined settings for multiple walking speeds or adjustments for subjects of differing anthropometry and walking ability, NMC enabled SCI subjects to walk at several speeds, including near healthy speeds, in a healthy-like manner. These preliminary results are promising for future implementation of neuromuscular controllers on wearable prototypes for real-world walking conditions.

Frontiers Research Foundation2017

Implementation of a Human Feedback-based Locomotion and its Control by means of a Feedforward Component inspired by Central Pattern Generators

Florin Dzeladini

Walking gaits of various animals have been modeled using this framework of differential equations, and more specifically, using network of coupled oscillators or CPG (Central Pattern Generators). In these models, oscillators are coupled among themselves and thus influence each other. Nerve signals generating swimming in the Lampreys have been modeled using such a system. These oscillatory networks were successfully used to model several swimming animals. However, results obtained with walking animals have been rather disappointing. This is not surprising, since CPGs does not take into account interaction with the environment for shaping the movement, which seems to be more important for walking animals. In this project we study the interaction between feedback and CPGs in humans using a bio-inspired musculoskeletal model of human walking. We start with a pure feedback based model of human walking and extend it by introducing a feedforward component inspired by CPGs. We then test the properties of such a hybrid feedback and feedforward system. We show that, not only those new models are stable with characteristics close to the original model, but with online control they showed a clear increase of the robustness compared to pure feedback model. Moreover, modifications of some general parameters of the feedforward component allow easy changes in gait characteristics, such as gait speed.

2013

Wearable Technologies for Embodied Human-Robot Interaction

Carine Rognon

Robotic teleoperation is fundamental to augment the resilience, precision, and force of robots with the cognition of the operator. However, current interfaces, such as joysticks and remote controllers, are often complicated to handle since they require cognitive effort and learned skills. Wearable interfaces can enable more natural and intuitive interactions with robots, which would make robotic teleoperation accessible to a larger population of users for demanding tasks, such as manipulation or search-and-rescue. The aim of this thesis is to explore solutions to simplify our interactions with robots promoting their access to a broader range of the population. To achieve this, we are presenting a soft upper body exoskeleton, called the FlyJacket, for the bidirectional control of drones. Drones can greatly benefit us as they extend our perception and range of action. The exoskeleton controls a drone by recording torso movement and, through embedded haptic feedback devices, renders either kinesthetic guidance to improve the flight performance or tactile feedback to render the sensation of flying. We developed and tested an interface to control both a simulated and a real drone. The FlyJacket is a soft exoskeleton with arm support conceived to address the challenges of adapting to different morphologies and supporting the user during flight to prevent fatigue. We demonstrated that this novel interface allowed more consistent performance than when performing the same task with a remote controller and users felt more immersed into the flight. Interacting with a robot can be greatly enhanced by having multiple channels of sensorial feedback to increase the awareness of the operator. Information on the state of the drone can be intuitively rendered with haptic feedback. To create this bidirectional interaction with the drone, the two types of haptic feedback - kinesthetic and tactile - have been explored. Kinesthetic feedback was implemented with a cable-driven system to give guidance to the userâs torso position. Performing user studies, we could determine that the embedded guidance improved the flight performance and that a quadratically shaped force feedback curve was the most adequate profile to guide the user. We also established the minimal force difference, defined the perceived magnitude of this system and studied the learning process of users. Tactile feedback was investigated to render the sensation of flying by enhancing flight awareness, realism and immersion. To this end, we developed and embedded a new type of soft actuator that was compliant and lightweight such that it remained wearable and portable. Four devices, placed on the torso, provided feedback by compressing closed air pouches against the skin rendering the sensation of air pressure. A mechanical model and simulation of the pouch device were developed to determine appropriate parameters. We evaluated whether it conveyed useful information to the user and whether it enhanced the experience of flying. We demonstrated that users were able to understand the direction of the cues without prompting, could distinguish the cues quickly, and do so with high accuracy. The device was also used in a simulated flight task and users indicated that it increased the flight realism. We believe that the contributions of this thesis provides insights to the design of intuitive interfaces for human-robot interaction and increases their accessibility to a wider range of the population.