1,014 research outputs found
Joint Trajectory Generation and High-level Control for Patient-tailored Robotic Gait Rehabilitation
This dissertation presents a group of novel methods for robot-based gait rehabilitation which were developed aiming to offer more individualized therapies based on the specific condition of each patient, as well as to improve the overall rehabilitation experience for both patient and therapist. A novel methodology for gait pattern generation is proposed, which offers estimated hip and knee joint trajectories corresponding to healthy walking, and allows the therapist to graphically adapt the reference trajectories in order to fit better the patient's needs and disabilities. Additionally, the motion controllers for the hip and knee joints, mobile platform, and pelvic mechanism of an over-ground gait rehabilitation robotic system are also presented, as well as some proposed methods for assist as needed therapy. Two robot-patient synchronization approaches are also included in this work, together with a novel algorithm for online hip trajectory adaptation developed to reduce obstructive forces applied to the patient during therapy with compliant robotic systems. Finally, a prototype graphical user interface for the therapist is also presented
Technical Report on: Tripedal Dynamic Gaits for a Quadruped Robot
A vast number of applications for legged robots entail tasks in complex,
dynamic environments. But these environments put legged robots at high risk for
limb damage. This paper presents an empirical study of fault tolerant dynamic
gaits designed for a quadrupedal robot suffering from a single, known
``missing'' limb. Preliminary data suggests that the featured gait controller
successfully anchors a previously developed planar monopedal hopping template
in the three-legged spatial machine. This compositional approach offers a
useful and generalizable guide to the development of a wider range of tripedal
recovery gaits for damaged quadrupedal machines.Comment: Updated *increased font size on figures 2-6 *added a legend, replaced
text with colors in figure 5a and 6a *made variables representing vectors
boldface in equations 8-10 *expanded on calculations in equations 8-10 by
adding additional lines *added a missing "2" to equation 8 (typo) *added mass
of the robot to tables II and III *increased the width of figures 1 and
Towards a Smart Semi-Active Prosthetic Leg: Preliminary Assessment and Testing
This paper presents a development of a semi-active prosthetic knee, which can work in both active and passive modes based on the energy required during the gait cycle of various activities of daily livings (ADLs). The prosthetic limb is equipped with various sensors to measure the kinematic and kinetic parameters of both prosthetic limbs. This prosthetic knee is designed to be back-drivable in passive mode to provide a potential use in energy regeneration when there negative energy across the knee joint. Preliminary test has been performed on transfemoral amputee in passive mode to provide some insight to the amputee/prosthesis interaction and performance with the designed prosthetic knee
RLOC: Terrain-Aware Legged Locomotion using Reinforcement Learning and Optimal Control
We present a unified model-based and data-driven approach for quadrupedal
planning and control to achieve dynamic locomotion over uneven terrain. We
utilize on-board proprioceptive and exteroceptive feedback to map sensory
information and desired base velocity commands into footstep plans using a
reinforcement learning (RL) policy trained in simulation over a wide range of
procedurally generated terrains. When ran online, the system tracks the
generated footstep plans using a model-based controller. We evaluate the
robustness of our method over a wide variety of complex terrains. It exhibits
behaviors which prioritize stability over aggressive locomotion. Additionally,
we introduce two ancillary RL policies for corrective whole-body motion
tracking and recovery control. These policies account for changes in physical
parameters and external perturbations. We train and evaluate our framework on a
complex quadrupedal system, ANYmal version B, and demonstrate transferability
to a larger and heavier robot, ANYmal C, without requiring retraining.Comment: 19 pages, 15 figures, 6 tables, 1 algorithm, submitted to T-RO; under
revie
Design Principles for FES Concept Development
© Cranfield University 2013. All rights reserved. No part of this
publication may be reproduced without the written permission of the
copyright owner.A variety of pathologies can cause injury to the spinal cord and hinder movement. A range of
equipment is available to help spinal injury sufferers move their affected limbs. One method of
rehabilitation is functional electrical stimulation (FES). FES is a technique where small electrical
currents are applied to the surface of the userâs legs to stimulate the muscles. Studies have
demonstrated the benefits of using this method and it has also been incorporated into a
number of devices.
The aim of the project was to produce a number of designs for a new device that uses FES
technology. The project was completed in conjunction with an industrial partner. A review of
the literature and consultation with industrial experts suggested a number of ways current
devices could be improved. These included encouraging the user to lean forwards while
walking and powering the device using a more ergonomic method.
A group of designers were used to produce designs that allowed the user to walk with a more
natural gait and avoided cumbersome power packs. The most effective of these designs were
combined to form one design that solved both problems. A 3-dimensional model of this design
was simulated using computer-aided design software.
Groups of engineers, scientists and consumers were also invited to provide input on how a
new device should function. Each of these groups provided a design that reflected their
specific needs, depending on their experience with similar technology. Low level prototypes
were produced of these designs.
A group of designers were also used to design concepts for a functional electrical stimulation
device based on an introduction given by industry experts.
Each of the designs was presented to experienced professionals to obtain feedback.
A set of guidelines were also produced during the project that instructed how to create the
designs
Aerospace medicine and biology: A continuing bibliography with indexes, supplement 128, May 1974
This special bibliography lists 282 reports, articles, and other documents introduced into the NASA scientific and technical information system in April 1974
Down-Conditioning of Soleus Reflex Activity using Mechanical Stimuli and EMG Biofeedback
Spasticity is a common syndrome caused by various brain and neural injuries, which can severely impair walking ability and functional independence. To improve functional independence, conditioning protocols are available aimed at reducing spasticity by facilitating spinal neuroplasticity. This down-conditioning can be performed using different types of stimuli, electrical or mechanical, and reflex activity measures, EMG or impedance, used as biofeedback variable. Still, current results on effectiveness of these conditioning protocols are incomplete, making comparisons difficult. We aimed to show the within-session task- dependent and across-session long-term adaptation of a conditioning protocol based on mechanical stimuli and EMG biofeedback. However, in contrast to literature, preliminary results show that subjects were unable to successfully obtain task-dependent modulation of their soleus short-latency stretch reflex magnitude
Energy Regeneration and Environment Sensing for Robotic Leg Prostheses and Exoskeletons
Robotic leg prostheses and exoskeletons can provide powered locomotor assistance to older adults and/or persons with physical disabilities. However, limitations in automated control and energy-efficient actuation have impeded their transition from research laboratories to real-world environments. With regards to control, the current automated locomotion mode recognition systems being developed rely on mechanical, inertial, and/or neuromuscular sensors, which inherently have limited prediction horizons (i.e., analogous to walking blindfolded). Inspired by the human vision-locomotor control system, here a multi-generation environment sensing and classification system powered by computer vision and deep learning was developed to predict the oncoming walking environments prior to physical interaction, therein allowing for more accurate and robust high-level control decisions. To support this initiative, the âExoNetâ database was developed â the largest and most diverse open-source dataset of wearable camera images of indoor and outdoor real-world walking environments, which were annotated using a novel hierarchical labelling architecture. Over a dozen state-of-the-art deep convolutional neural networks were trained and tested on ExoNet for large-scale image classification and automatic feature engineering. The benchmarked CNN architectures and their environment classification predictions were then quantitatively evaluated and compared using an operational metric called âNetScoreâ, which balances the classification accuracy with the architectural and computational complexities (i.e., important for onboard real-time inference with mobile computing devices). Of the benchmarked CNN architectures, the EfficientNetB0 network achieved the highest test accuracy; VGG16 the fastest inference time; and MobileNetV2 the best NetScore. These comparative results can inform the optimal architecture design or selection depending on the desired performance of an environment classification system. With regards to energetics, backdriveable actuators with energy regeneration can improve the energy efficiency and extend the battery-powered operating durations by converting some of the otherwise dissipated energy during negative mechanical work into electrical energy. However, the evaluation and control of these regenerative actuators has focused on steady-state level-ground walking. To encompass real-world community mobility more broadly, here an energy regeneration system, featuring mathematical and computational models of human and wearable robotic systems, was developed to simulate energy regeneration and storage during other locomotor activities of daily living, specifically stand-to-sit movements. Parameter identification and inverse dynamic simulations of subject-specific optimized biomechanical models were used to calculate the negative joint mechanical work and power while sitting down (i.e., the mechanical energy theoretically available for electrical energy regeneration). These joint mechanical energetics were then used to simulate a robotic exoskeleton being backdriven and regenerating energy. An empirical characterization of an exoskeleton was carried out using a joint dynamometer system and an electromechanical motor model to calculate the actuator efficiency and to simulate energy regeneration and storage with the exoskeleton parameters. The performance calculations showed that regenerating electrical energy during stand-to-sit movements provide small improvements in energy efficiency and battery-powered operating durations. In summary, this research involved the development and evaluation of environment classification and energy regeneration systems to improve the automated control and energy-efficient actuation of next-generation robotic leg prostheses and exoskeletons for real-world locomotor assistance
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