ASU Electronic Theses and Dissertations
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.
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- All Subjects: Human-Robot Interaction
- Creators: Sugar, Thomas
Description
Myoelectric control is lled with potential to signicantly change human-robot interaction.
Humans desire compliant robots to safely interact in dynamic environments
associated with daily activities. As surface electromyography non-invasively measures
limb motion intent and correlates with joint stiness during co-contractions,
it has been identied as a candidate for naturally controlling such robots. However,
state-of-the-art myoelectric interfaces have struggled to achieve both enhanced
functionality and long-term reliability. As demands in myoelectric interfaces trend
toward simultaneous and proportional control of compliant robots, robust processing
of multi-muscle coordinations, or synergies, plays a larger role in the success of the
control scheme. This dissertation presents a framework enhancing the utility of myoelectric
interfaces by exploiting motor skill learning and
exible muscle synergies for
reliable long-term simultaneous and proportional control of multifunctional compliant
robots. The interface is learned as a new motor skill specic to the controller,
providing long-term performance enhancements without requiring any retraining or
recalibration of the system. Moreover, the framework oers control of both motion
and stiness simultaneously for intuitive and compliant human-robot interaction. The
framework is validated through a series of experiments characterizing motor learning
properties and demonstrating control capabilities not seen previously in the literature.
The results validate the approach as a viable option to remove the trade-o
between functionality and reliability that have hindered state-of-the-art myoelectric
interfaces. Thus, this research contributes to the expansion and enhancement of myoelectric
controlled applications beyond commonly perceived anthropomorphic and
\intuitive control" constraints and into more advanced robotic systems designed for
everyday tasks.
Humans desire compliant robots to safely interact in dynamic environments
associated with daily activities. As surface electromyography non-invasively measures
limb motion intent and correlates with joint stiness during co-contractions,
it has been identied as a candidate for naturally controlling such robots. However,
state-of-the-art myoelectric interfaces have struggled to achieve both enhanced
functionality and long-term reliability. As demands in myoelectric interfaces trend
toward simultaneous and proportional control of compliant robots, robust processing
of multi-muscle coordinations, or synergies, plays a larger role in the success of the
control scheme. This dissertation presents a framework enhancing the utility of myoelectric
interfaces by exploiting motor skill learning and
exible muscle synergies for
reliable long-term simultaneous and proportional control of multifunctional compliant
robots. The interface is learned as a new motor skill specic to the controller,
providing long-term performance enhancements without requiring any retraining or
recalibration of the system. Moreover, the framework oers control of both motion
and stiness simultaneously for intuitive and compliant human-robot interaction. The
framework is validated through a series of experiments characterizing motor learning
properties and demonstrating control capabilities not seen previously in the literature.
The results validate the approach as a viable option to remove the trade-o
between functionality and reliability that have hindered state-of-the-art myoelectric
interfaces. Thus, this research contributes to the expansion and enhancement of myoelectric
controlled applications beyond commonly perceived anthropomorphic and
\intuitive control" constraints and into more advanced robotic systems designed for
everyday tasks.
ContributorsIson, Mark (Author) / Artemiadis, Panagiotis (Thesis advisor) / Santello, Marco (Committee member) / Greger, Bradley (Committee member) / Berman, Spring (Committee member) / Sugar, Thomas (Committee member) / Fainekos, Georgios (Committee member) / Arizona State University (Publisher)
Created2015
Description
Human running requires extensive training and conditioning for an individual to maintain high speeds (greater than 10mph) for an extended duration of time. Studies have shown that running at peak speeds generates a high metabolic cost due to the use of large muscle groups in the legs associated with the human gait cycle. Applying supplemental external and internal forces to the human body during the gait cycle has been shown to decrease the metabolic cost for walking, allowing individuals to carry additional weight and walk further distances. Significant research has been conducted to reduce the metabolic cost of walking, however, there are few if any documented studies that focus specifically on reducing the metabolic cost associated with high speed running. Three mechanical systems were designed to work in concert with the human user to decrease metabolic cost and increase the range and speeds at which a human can run.
The methods of design require a focus on mathematical modeling, simulations, and metabolic cost. Mathematical modeling and simulations are used to aid in the design process of robotic systems and metabolic testing is regarded as the final analysis process to determine the true effectiveness of robotic prototypes. Metabolic data, (VO2) is the volumetric consumption of oxygen, per minute, per unit mass (ml/min/kg). Metabolic testing consists of analyzing the oxygen consumption of a test subject while performing a task naturally and then comparing that data with analyzed oxygen consumption of the same task while using an assistive device.
Three devices were designed and tested to augment high speed running. The first device, AirLegs V1, is a mostly aluminum exoskeleton with two pneumatic linear actuators connecting from the lower back directly to the user's thighs, allowing the device to induce a torque on the leg by pushing and pulling on the user's thigh during running. The device also makes use of two smaller pneumatic linear actuators which drive cables connecting to small lever arms at the back of the heel, inducing a torque at the ankles. Device two, AirLegs V2, is also pneumatically powered but is considered to be a soft suit version of the first device. It uses cables to interface the forces created by actuators located vertically on the user's back. These cables then connect to the back of the user's knees resulting in greater flexibility and range of motion of the legs. Device three, a Jet Pack, produces an external force against the user's torso to propel a user forward and upward making it easier to run. Third party testing, pilot demonstrations and timed trials have demonstrated that all three of the devices effectively reduce the metabolic cost of running below that of natural running with no device.
The methods of design require a focus on mathematical modeling, simulations, and metabolic cost. Mathematical modeling and simulations are used to aid in the design process of robotic systems and metabolic testing is regarded as the final analysis process to determine the true effectiveness of robotic prototypes. Metabolic data, (VO2) is the volumetric consumption of oxygen, per minute, per unit mass (ml/min/kg). Metabolic testing consists of analyzing the oxygen consumption of a test subject while performing a task naturally and then comparing that data with analyzed oxygen consumption of the same task while using an assistive device.
Three devices were designed and tested to augment high speed running. The first device, AirLegs V1, is a mostly aluminum exoskeleton with two pneumatic linear actuators connecting from the lower back directly to the user's thighs, allowing the device to induce a torque on the leg by pushing and pulling on the user's thigh during running. The device also makes use of two smaller pneumatic linear actuators which drive cables connecting to small lever arms at the back of the heel, inducing a torque at the ankles. Device two, AirLegs V2, is also pneumatically powered but is considered to be a soft suit version of the first device. It uses cables to interface the forces created by actuators located vertically on the user's back. These cables then connect to the back of the user's knees resulting in greater flexibility and range of motion of the legs. Device three, a Jet Pack, produces an external force against the user's torso to propel a user forward and upward making it easier to run. Third party testing, pilot demonstrations and timed trials have demonstrated that all three of the devices effectively reduce the metabolic cost of running below that of natural running with no device.
ContributorsKerestes, Jason (Author) / Sugar, Thomas (Thesis advisor) / Redkar, Sangram (Committee member) / Rogers, Bradley (Committee member) / Arizona State University (Publisher)
Created2014