| NSF Org: |
CMMI Division of Civil, Mechanical, and Manufacturing Innovation |
| Recipient: |
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| Initial Amendment Date: | May 23, 2013 |
| Latest Amendment Date: | May 23, 2013 |
| Award Number: | 1300019 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Atul Kelkar
CMMI Division of Civil, Mechanical, and Manufacturing Innovation ENG Directorate for Engineering |
| Start Date: | June 1, 2013 |
| End Date: | May 31, 2017 (Estimated) |
| Total Intended Award Amount: | $289,417.00 |
| Total Awarded Amount to Date: | $289,417.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
926 DALNEY ST NW ATLANTA GA US 30318-6395 (404)894-4819 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
225 North Avenue Atlanta GA US 30332-0002 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | DYNAMICAL SYSTEMS |
| Primary Program Source: |
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| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.041 |
ABSTRACT
The goal of this award is to establish a physiologically inspired framework for recruiting compliant modular actuators for biological movement generation. The research will result in an in-depth understanding of how modularity and variability in the neuromuscular system play key roles in coordinating multiple muscles. The research studies a non-uniform recruitment method for compliant actuator arrays. Floating point quantization (FPQ), a popular numbering scheme in digital communications, appeared as a viable option which mimics aspects of Henneman's size principle of motor unit recruitment. The research hypothesizes that (1) variability in muscle forces and signal-dependency can be characterized by quantized actuation profiles in non-uniform modular actuator system, (2) a quantized actuator recruitment approach reproduces such signal-dependent variability without introducing an artificial source of noise into robotic architecture, and (3) optimization principles in muscle coordination can be modeled mathematically by mapping the variability at the level of individual muscles to the variability at an end-point from a stochastic control perspective. Quantized control methods for recruiting muscle-type robotic actuators for biological trajectory generation will be developed, and resultant motor performance in artificial robotic limbs will be compared to performance in humans.
If successful, the research will advance the field of biologically inspired robotics, rehabilitation robotics, computational neuroscience, and character animation. A deeper understanding of neuromuscular physiology will provide more sophisticated computational models, resulting in a novel architecture for robotics that captures the advantages inherent in biological motor systems. Research outcomes will be integrated into current courses including offering a special topic for undergraduate design research. Graduate and undergraduate students will be recruited from interdisciplinary and multicultural groups including under-represented groups. Outreach activities for K-12 students will be conducted through a Robotics Summer Camp program, Georgia Tech the Student and Teacher Enhancement Partnership Program, and FIRST Robotics.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
Currently, the large majority of robotic systems utilize motors as actuating units. While motors offer distinct advantages, such as linear response and ease of control, they can be prohibitive in other respects. Larger load capacity or higher dynamic performance requirements can necessitate motors that are large, heavy and costly. The development of novel actuation technologies that are power-dense, lighter, and compact can expand the design space for robotic systems to enable new applications. Furthermore, utilizing a physiologically-inspired framework for actuation can lead to designs that generate smooth human-like motion. Such robots would be better suited for applications that require interaction with humans or imitation of human motion, which include prosthetics, rehabilitation robotics, and character animation.
This project explored cellular actuation technologies which utilize active materials in a distributed architecture. Each cellular actuator is composed of many cells, that are controlled in a binary fashion (On-Off), and is considered a quantized system driven by impulsive actions. Discrete switching controllers have been developed for rapid point-to-point reaching motions of a mechanical structure driven by cellular actuators. The working principle for the proposed methods is vibration suppression, through minimization of the number of switches used in the control policy. These methods have been experimentally validated on a robotic manipulator driven by shape memory alloy actuators and a camera positioning system driven by piezoelectric cellular actuators, configured in an antagonistic structure; taking inspiration from anatomical studies of the human musculoskeletal system. A stochastic optimization method has been implemented to find patterns to move the robotic systems.
One graduate student and two undergraduate students have been involved in the project for the development and programming of the robotic platforms. Findings have been presented at domestic and international conferences, poster sessions, journal articles, and a book. Demonstrations have been given for high school students during National Robotics Week events in springs 2015-2017. The research results have been introduced in the PI’s undergraduate and graduate mechanical and robotics courses.
Last Modified: 06/08/2017
Modified by: Jun Ueda
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