111 research outputs found
Real-time ECG Monitoring using Compressive sensing on a Heterogeneous Multicore Edge-Device
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.In a typical ambulatory health monitoring systems, wearable medical sensors
are deployed on the human body to continuously collect and transmit physiological
signals to a nearby gateway that forward the measured data to the
cloud-based healthcare platform. However, this model often fails to respect the
strict requirements of healthcare systems. Wearable medical sensors are very
limited in terms of battery lifetime, in addition, the system reliance on a cloud
makes it vulnerable to connectivity and latency issues. Compressive sensing
(CS) theory has been widely deployed in electrocardiogramme ECG monitoring
application to optimize the wearable sensors power consumption. The proposed
solution in this paper aims to tackle these limitations by empowering a gatewaycentric
connected health solution, where the most power consuming tasks are
performed locally on a multicore processor. This paper explores the efficiency
of real-time CS-based recovery of ECG signals on an IoT-gateway embedded
with ARMโs big.littleTM multicore for different signal dimension and allocated
computational resources. Experimental results show that the gateway is able
to reconstruct ECG signals in real-time. Moreover, it demonstrates that using
a high number of cores speeds up the execution time and it further optimizes
energy consumption. The paper identifies the best configurations of resource
allocation that provides the optimal performance. The paper concludes that
multicore processors have the computational capacity and energy efficiency to
promote gateway-centric solution rather than cloud-centric platforms
Engineering Resilient Space Systems
Several distinct trends will influence space exploration missions in the next decade. Destinations are
becoming more remote and mysterious, science questions more sophisticated, and, as mission experience
accumulates, the most accessible targets are visited, advancing the knowledge frontier to more difficult,
harsh, and inaccessible environments. This leads to new challenges including: hazardous conditions that
limit mission lifetime, such as high radiation levels surrounding interesting destinations like Europa or
toxic atmospheres of planetary bodies like Venus; unconstrained environments with navigation hazards,
such as free-floating active small bodies; multielement missions required to answer more sophisticated
questions, such as Mars Sample Return (MSR); and long-range missions, such as Kuiper belt exploration,
that must survive equipment failures over the span of decades. These missions will need to be successful
without a priori knowledge of the most efficient data collection techniques for optimum science return.
Science objectives will have to be revised โon the flyโ, with new data collection and navigation decisions
on short timescales.
Yet, even as science objectives are becoming more ambitious, several critical resources remain
unchanged. Since physics imposes insurmountable light-time delays, anticipated improvements to the
Deep Space Network (DSN) will only marginally improve the bandwidth and communications cadence to
remote spacecraft. Fiscal resources are increasingly limited, resulting in fewer flagship missions, smaller
spacecraft, and less subsystem redundancy. As missions visit more distant and formidable locations, the
job of the operations team becomes more challenging, seemingly inconsistent with the trend of shrinking
mission budgets for operations support. How can we continue to explore challenging new locations
without increasing risk or system complexity?
These challenges are present, to some degree, for the entire Decadal Survey mission portfolio, as
documented in Vision and Voyages for Planetary Science in the Decade 2013โ2022 (National Research
Council, 2011), but are especially acute for the following mission examples, identified in our recently
completed KISS Engineering Resilient Space Systems (ERSS) study:
1. A Venus lander, designed to sample the atmosphere and surface of Venus, would have to perform
science operations as components and subsystems degrade and fail;
2. A Trojan asteroid tour spacecraft would spend significant time cruising to its ultimate destination
(essentially hibernating to save on operations costs), then upon arrival, would have to act as its
own surveyor, finding new objects and targets of opportunity as it approaches each asteroid,
requiring response on short notice; and
3. A MSR campaign would not only be required to perform fast reconnaissance over long distances
on the surface of Mars, interact with an unknown physical surface, and handle degradations and
faults, but would also contain multiple components (launch vehicle, cruise stage, entry and
landing vehicle, surface rover, ascent vehicle, orbiting cache, and Earth return vehicle) that
dramatically increase the need for resilience to failure across the complex system.
The concept of resilience and its relevance and application in various domains was a focus during the
study, with several definitions of resilience proposed and discussed. While there was substantial variation
in the specifics, there was a common conceptual core that emergedโadaptation in the presence of
changing circumstances. These changes were couched in various waysโanomalies, disruptions,
discoveriesโbut they all ultimately had to do with changes in underlying assumptions. Invalid
assumptions, whether due to unexpected changes in the environment, or an inadequate understanding of
interactions within the system, may cause unexpected or unintended system behavior. A system is
resilient if it continues to perform the intended functions in the presence of invalid assumptions.
Our study focused on areas of resilience that we felt needed additional exploration and integration,
namely system and software architectures and capabilities, and autonomy technologies. (While also an
important consideration, resilience in hardware is being addressed in multiple other venues, including
2
other KISS studies.) The study consisted of two workshops, separated by a seven-month focused study
period. The first workshop (Workshop #1) explored the โproblem spaceโ as an organizing theme, and the
second workshop (Workshop #2) explored the โsolution spaceโ. In each workshop, focused discussions
and exercises were interspersed with presentations from participants and invited speakers.
The study period between the two workshops was organized as part of the synthesis activity during the
first workshop. The study participants, after spending the initial days of the first workshop discussing the
nature of resilience and its impact on future science missions, decided to split into three focus groups,
each with a particular thrust, to explore specific ideas further and develop material needed for the second
workshop. The three focus groups and areas of exploration were:
1. Reference missions: address/refine the resilience needs by exploring a set of reference missions
2. Capability survey: collect, document, and assess current efforts to develop capabilities and
technology that could be used to address the documented needs, both inside and outside NASA
3. Architecture: analyze the impact of architecture on system resilience, and provide principles and
guidance for architecting greater resilience in our future systems
The key product of the second workshop was a set of capability roadmaps pertaining to the three
reference missions selected for their representative coverage of the types of space missions envisioned for
the future. From these three roadmaps, we have extracted several common capability patterns that would
be appropriate targets for near-term technical development: one focused on graceful degradation of
system functionality, a second focused on data understanding for science and engineering applications,
and a third focused on hazard avoidance and environmental uncertainty. Continuing work is extending
these roadmaps to identify candidate enablers of the capabilities from the following three categories:
architecture solutions, technology solutions, and process solutions.
The KISS study allowed a collection of diverse and engaged engineers, researchers, and scientists to think
deeply about the theory, approaches, and technical issues involved in developing and applying resilience
capabilities. The conclusions summarize the varied and disparate discussions that occurred during the
study, and include new insights about the nature of the challenge and potential solutions:
1. There is a clear and definitive need for more resilient space systems. During our study period,
the key scientists/engineers we engaged to understand potential future missions confirmed the
scientific and risk reduction value of greater resilience in the systems used to perform these
missions.
2. Resilience can be quantified in measurable termsโproject cost, mission risk, and quality of
science return. In order to consider resilience properly in the set of engineering trades performed
during the design, integration, and operation of space systems, the benefits and costs of resilience
need to be quantified. We believe, based on the work done during the study, that appropriate
metrics to measure resilience must relate to risk, cost, and science quality/opportunity. Additional
work is required to explicitly tie design decisions to these first-order concerns.
3. There are many existing basic technologies that can be applied to engineering resilient space
systems. Through the discussions during the study, we found many varied approaches and
research that address the various facets of resilience, some within NASA, and many more
beyond. Examples from civil architecture, Department of Defense (DoD) / Defense Advanced
Research Projects Agency (DARPA) initiatives, โsmartโ power grid control, cyber-physical
systems, software architecture, and application of formal verification methods for software were
identified and discussed. The variety and scope of related efforts is encouraging and presents
many opportunities for collaboration and development, and we expect many collaborative
proposals and joint research as a result of the study.
4. Use of principled architectural approaches is key to managing complexity and integrating
disparate technologies. The main challenge inherent in considering highly resilient space
systems is that the increase in capability can result in an increase in complexity with all of the
3
risks and costs associated with more complex systems. What is needed is a better way of
conceiving space systems that enables incorporation of capabilities without increasing
complexity. We believe principled architecting approaches provide the needed means to convey a
unified understanding of the system to primary stakeholders, thereby controlling complexity in
the conception and development of resilient systems, and enabling the integration of disparate
approaches and technologies. A representative architectural example is included in Appendix F.
5. Developing trusted resilience capabilities will require a diverse yet strategically directed
research program. Despite the interest in, and benefits of, deploying resilience space systems, to
date, there has been a notable lack of meaningful demonstrated progress in systems capable of
working in hazardous uncertain situations. The roadmaps completed during the study, and
documented in this report, provide the basis for a real funded plan that considers the required
fundamental work and evolution of needed capabilities.
Exploring space is a challenging and difficult endeavor. Future space missions will require more
resilience in order to perform the desired science in new environments under constraints of development
and operations cost, acceptable risk, and communications delays. Development of space systems with
resilient capabilities has the potential to expand the limits of possibility, revolutionizing space science by
enabling as yet unforeseen missions and breakthrough science observations.
Our KISS study provided an essential venue for the consideration of these challenges and goals.
Additional work and future steps are needed to realize the potential of resilient systemsโthis study
provided the necessary catalyst to begin this process
Engineering Resilient Space Systems
Several distinct trends will influence space exploration missions in the next decade. Destinations are
becoming more remote and mysterious, science questions more sophisticated, and, as mission experience
accumulates, the most accessible targets are visited, advancing the knowledge frontier to more difficult,
harsh, and inaccessible environments. This leads to new challenges including: hazardous conditions that
limit mission lifetime, such as high radiation levels surrounding interesting destinations like Europa or
toxic atmospheres of planetary bodies like Venus; unconstrained environments with navigation hazards,
such as free-floating active small bodies; multielement missions required to answer more sophisticated
questions, such as Mars Sample Return (MSR); and long-range missions, such as Kuiper belt exploration,
that must survive equipment failures over the span of decades. These missions will need to be successful
without a priori knowledge of the most efficient data collection techniques for optimum science return.
Science objectives will have to be revised โon the flyโ, with new data collection and navigation decisions
on short timescales.
Yet, even as science objectives are becoming more ambitious, several critical resources remain
unchanged. Since physics imposes insurmountable light-time delays, anticipated improvements to the
Deep Space Network (DSN) will only marginally improve the bandwidth and communications cadence to
remote spacecraft. Fiscal resources are increasingly limited, resulting in fewer flagship missions, smaller
spacecraft, and less subsystem redundancy. As missions visit more distant and formidable locations, the
job of the operations team becomes more challenging, seemingly inconsistent with the trend of shrinking
mission budgets for operations support. How can we continue to explore challenging new locations
without increasing risk or system complexity?
These challenges are present, to some degree, for the entire Decadal Survey mission portfolio, as
documented in Vision and Voyages for Planetary Science in the Decade 2013โ2022 (National Research
Council, 2011), but are especially acute for the following mission examples, identified in our recently
completed KISS Engineering Resilient Space Systems (ERSS) study:
1. A Venus lander, designed to sample the atmosphere and surface of Venus, would have to perform
science operations as components and subsystems degrade and fail;
2. A Trojan asteroid tour spacecraft would spend significant time cruising to its ultimate destination
(essentially hibernating to save on operations costs), then upon arrival, would have to act as its
own surveyor, finding new objects and targets of opportunity as it approaches each asteroid,
requiring response on short notice; and
3. A MSR campaign would not only be required to perform fast reconnaissance over long distances
on the surface of Mars, interact with an unknown physical surface, and handle degradations and
faults, but would also contain multiple components (launch vehicle, cruise stage, entry and
landing vehicle, surface rover, ascent vehicle, orbiting cache, and Earth return vehicle) that
dramatically increase the need for resilience to failure across the complex system.
The concept of resilience and its relevance and application in various domains was a focus during the
study, with several definitions of resilience proposed and discussed. While there was substantial variation
in the specifics, there was a common conceptual core that emergedโadaptation in the presence of
changing circumstances. These changes were couched in various waysโanomalies, disruptions,
discoveriesโbut they all ultimately had to do with changes in underlying assumptions. Invalid
assumptions, whether due to unexpected changes in the environment, or an inadequate understanding of
interactions within the system, may cause unexpected or unintended system behavior. A system is
resilient if it continues to perform the intended functions in the presence of invalid assumptions.
Our study focused on areas of resilience that we felt needed additional exploration and integration,
namely system and software architectures and capabilities, and autonomy technologies. (While also an
important consideration, resilience in hardware is being addressed in multiple other venues, including
2
other KISS studies.) The study consisted of two workshops, separated by a seven-month focused study
period. The first workshop (Workshop #1) explored the โproblem spaceโ as an organizing theme, and the
second workshop (Workshop #2) explored the โsolution spaceโ. In each workshop, focused discussions
and exercises were interspersed with presentations from participants and invited speakers.
The study period between the two workshops was organized as part of the synthesis activity during the
first workshop. The study participants, after spending the initial days of the first workshop discussing the
nature of resilience and its impact on future science missions, decided to split into three focus groups,
each with a particular thrust, to explore specific ideas further and develop material needed for the second
workshop. The three focus groups and areas of exploration were:
1. Reference missions: address/refine the resilience needs by exploring a set of reference missions
2. Capability survey: collect, document, and assess current efforts to develop capabilities and
technology that could be used to address the documented needs, both inside and outside NASA
3. Architecture: analyze the impact of architecture on system resilience, and provide principles and
guidance for architecting greater resilience in our future systems
The key product of the second workshop was a set of capability roadmaps pertaining to the three
reference missions selected for their representative coverage of the types of space missions envisioned for
the future. From these three roadmaps, we have extracted several common capability patterns that would
be appropriate targets for near-term technical development: one focused on graceful degradation of
system functionality, a second focused on data understanding for science and engineering applications,
and a third focused on hazard avoidance and environmental uncertainty. Continuing work is extending
these roadmaps to identify candidate enablers of the capabilities from the following three categories:
architecture solutions, technology solutions, and process solutions.
The KISS study allowed a collection of diverse and engaged engineers, researchers, and scientists to think
deeply about the theory, approaches, and technical issues involved in developing and applying resilience
capabilities. The conclusions summarize the varied and disparate discussions that occurred during the
study, and include new insights about the nature of the challenge and potential solutions:
1. There is a clear and definitive need for more resilient space systems. During our study period,
the key scientists/engineers we engaged to understand potential future missions confirmed the
scientific and risk reduction value of greater resilience in the systems used to perform these
missions.
2. Resilience can be quantified in measurable termsโproject cost, mission risk, and quality of
science return. In order to consider resilience properly in the set of engineering trades performed
during the design, integration, and operation of space systems, the benefits and costs of resilience
need to be quantified. We believe, based on the work done during the study, that appropriate
metrics to measure resilience must relate to risk, cost, and science quality/opportunity. Additional
work is required to explicitly tie design decisions to these first-order concerns.
3. There are many existing basic technologies that can be applied to engineering resilient space
systems. Through the discussions during the study, we found many varied approaches and
research that address the various facets of resilience, some within NASA, and many more
beyond. Examples from civil architecture, Department of Defense (DoD) / Defense Advanced
Research Projects Agency (DARPA) initiatives, โsmartโ power grid control, cyber-physical
systems, software architecture, and application of formal verification methods for software were
identified and discussed. The variety and scope of related efforts is encouraging and presents
many opportunities for collaboration and development, and we expect many collaborative
proposals and joint research as a result of the study.
4. Use of principled architectural approaches is key to managing complexity and integrating
disparate technologies. The main challenge inherent in considering highly resilient space
systems is that the increase in capability can result in an increase in complexity with all of the
3
risks and costs associated with more complex systems. What is needed is a better way of
conceiving space systems that enables incorporation of capabilities without increasing
complexity. We believe principled architecting approaches provide the needed means to convey a
unified understanding of the system to primary stakeholders, thereby controlling complexity in
the conception and development of resilient systems, and enabling the integration of disparate
approaches and technologies. A representative architectural example is included in Appendix F.
5. Developing trusted resilience capabilities will require a diverse yet strategically directed
research program. Despite the interest in, and benefits of, deploying resilience space systems, to
date, there has been a notable lack of meaningful demonstrated progress in systems capable of
working in hazardous uncertain situations. The roadmaps completed during the study, and
documented in this report, provide the basis for a real funded plan that considers the required
fundamental work and evolution of needed capabilities.
Exploring space is a challenging and difficult endeavor. Future space missions will require more
resilience in order to perform the desired science in new environments under constraints of development
and operations cost, acceptable risk, and communications delays. Development of space systems with
resilient capabilities has the potential to expand the limits of possibility, revolutionizing space science by
enabling as yet unforeseen missions and breakthrough science observations.
Our KISS study provided an essential venue for the consideration of these challenges and goals.
Additional work and future steps are needed to realize the potential of resilient systemsโthis study
provided the necessary catalyst to begin this process
Computational resources of miniature robots: classification & implications
When it comes to describing robots, many roboticists choose to focus on the size, types of actuators, or other physical capabilities. As most areas of robotics deploy robots with large memory and processing power, the question โhow computational resources limit what a robot can doโ is often overlooked. However, the capabilities of many miniature robots are limited by significantly less memory and processing power. At present, there is no systematic approach to comparing and quantifying the computational resources as a whole and their implications. This letter proposes computational indices that systematically quantify computational resourcesโindividually and as a whole. Then, by comparing 31 state-of-the-art miniature robots, a computational classification ranging from non-computing to minimally-constrained robots is introduced. Finally, the implications of computational constraints on robotic software are discussed
ํ์ ๋ก๋ด์ ์ํ ์๋น์ค ๊ธฐ๋ฐ๊ณผ ๋ชจ๋ธ ๊ธฐ๋ฐ์ ์ํํธ์จ์ด ๊ฐ๋ฐ ๋ฐฉ๋ฒ๋ก
ํ์๋
ผ๋ฌธ(๋ฐ์ฌ)--์์ธ๋ํ๊ต ๋ํ์ :๊ณต๊ณผ๋ํ ์ ๊ธฐยท์ปดํจํฐ๊ณตํ๋ถ,2020. 2. ํ์ํ.๊ฐ๊น์ด ๋ฏธ๋์๋ ๋ค์ํ ๋ก๋ด์ด ๋ค์ํ ๋ถ์ผ์์ ํ๋์ ์๋ฌด๋ฅผ ํ๋ ฅํ์ฌ ์ํํ๋ ๋ชจ์ต์ ํํ ๋ณผ ์ ์๊ฒ ๋ ๊ฒ์ด๋ค. ๊ทธ๋ฌ๋ ์ค์ ๋ก ์ด๋ฌํ ๋ชจ์ต์ด ์คํ๋๊ธฐ์๋ ๋ ๊ฐ์ง์ ์ด๋ ค์์ด ์๋ค. ๋จผ์ ๋ก๋ด์ ์ด์ฉํ๊ธฐ ์ํ ์ํํธ์จ์ด๋ฅผ ๋ช
์ธํ๋ ๊ธฐ์กด ์ฐ๊ตฌ๋ค์ ๋๋ถ๋ถ ๊ฐ๋ฐ์๊ฐ ๋ก๋ด์ ํ๋์จ์ด์ ์ํํธ์จ์ด์ ๋ํ ์ง์์ ์๊ณ ์๋ ๊ฒ์ ๊ฐ์ ํ๊ณ ์๋ค. ๊ทธ๋์ ๋ก๋ด์ด๋ ์ปดํจํฐ์ ๋ํ ์ง์์ด ์๋ ์ฌ์ฉ์๋ค์ด ์ฌ๋ฌ ๋์ ๋ก๋ด์ด ํ๋ ฅํ๋ ์๋๋ฆฌ์ค๋ฅผ ์์ฑํ๊ธฐ๋ ์ฝ์ง ์๋ค. ๋ํ, ๋ก๋ด์ ์ํํธ์จ์ด๋ฅผ ๊ฐ๋ฐํ ๋ ๋ก๋ด์ ํ๋์จ์ด์ ํน์ฑ๊ณผ ๊ด๋ จ์ด ๊น์ด์, ๋ค์ํ ๋ก๋ด์ ์ํํธ์จ์ด๋ฅผ ๊ฐ๋ฐํ๋ ๊ฒ๋ ๊ฐ๋จํ์ง ์๋ค. ๋ณธ ๋
ผ๋ฌธ์์๋ ์์ ์์ค์ ๋ฏธ์
๋ช
์ธ์ ๋ก๋ด์ ํ์ ํ๋ก๊ทธ๋๋ฐ์ผ๋ก ๋๋์ด ์๋ก์ด ์ํํธ์จ์ด ๊ฐ๋ฐ ํ๋ ์์ํฌ๋ฅผ ์ ์ํ๋ค. ๋ํ, ๋ณธ ํ๋ ์์ํฌ๋ ํฌ๊ธฐ๊ฐ ์์ ๋ก๋ด๋ถํฐ ๊ณ์ฐ ๋ฅ๋ ฅ์ด ์ถฉ๋ถํ ๋ก๋ด๋ค์ด ์๋ก ๊ตฐ์ง์ ์ด๋ฃจ์ด ๋ฏธ์
์ ์ํํ ์ ์๋๋ก ์ง์ํ๋ค.
๋ณธ ์ฐ๊ตฌ์์๋ ๋ก๋ด์ ํ๋์จ์ด๋ ์ํํธ์จ์ด์ ๋ํ ์ง์์ด ๋ถ์กฑํ ์ฌ์ฉ์๋ ๋ก๋ด์ ๋์์ ์์ ์์ค์์ ๋ช
์ธํ ์ ์๋ ์คํฌ๋ฆฝํธ ์ธ์ด๋ฅผ ์ ์ํ๋ค. ์ ์ํ๋ ์ธ์ด๋ ๊ธฐ์กด์ ์คํฌ๋ฆฝํธ ์ธ์ด์์๋ ์ง์ํ์ง ์๋ ๋ค ๊ฐ์ง์ ๊ธฐ๋ฅ์ธ ํ์ ๊ตฌ์ฑ, ๊ฐ ํ์ ์๋น์ค ๊ธฐ๋ฐ ํ๋ก๊ทธ๋๋ฐ, ๋์ ์ผ๋ก ๋ชจ๋ ๋ณ๊ฒฝ, ๋ค์ค ์์
(๋ฉํฐ ํ์คํน)์ ์ง์ํ๋ค. ์ฐ์ ๋ก๋ด์ ํ์ผ๋ก ๊ทธ๋ฃน ์ง์ ์ ์๊ณ , ๋ก๋ด์ด ์ํํ ์ ์๋ ๊ธฐ๋ฅ์ ์๋น์ค ๋จ์๋ก ์ถ์ํํ์ฌ ์๋ก์ด ๋ณตํฉ ์๋น์ค๋ฅผ ๋ช
์ธํ ์ ์๋ค. ๋ํ ๋ก๋ด์ ๋ฉํฐ ํ์คํน์ ์ํด 'ํ๋' ์ด๋ผ๋ ๊ฐ๋
์ ๋์
ํ์๊ณ , ๋ณตํฉ ์๋น์ค ๋ด์์ ์ด๋ฒคํธ๋ฅผ ๋ฐ์์์ผ์ ๋์ ์ผ๋ก ๋ชจ๋๊ฐ ๋ณํํ ์ ์๋๋ก ํ์๋ค. ๋์๊ฐ ์ฌ๋ฌ ๋ก๋ด์ ํ๋ ฅ์ด ๋์ฑ ๊ฒฌ๊ณ ํ๊ณ , ์ ์ฐํ๊ณ , ํ์ฅ์ฑ์ ๋์ด๊ธฐ ์ํด, ๊ตฐ์ง ๋ก๋ด์ ์ด์ฉํ ๋ ๋ก๋ด์ด ์๋ฌด๋ฅผ ์ํํ๋ ๋์ค์ ๋ฌธ์ ๊ฐ ์๊ธธ ์ ์์ผ๋ฉฐ, ์ํฉ์ ๋ฐ๋ผ ๋ก๋ด์ ๋์ ์ผ๋ก ๋ค๋ฅธ ํ์๋ฅผ ์ํํ ์ ์๋ค๊ณ ๊ฐ์ ํ๋ค. ์ด๋ฅผ ์ํด ๋์ ์ผ๋ก๋ ํ์ ๊ตฌ์ฑํ ์ ์๊ณ , ์ฌ๋ฌ ๋์ ๋ก๋ด์ด ํ๋์ ์๋น์ค๋ฅผ ์ํํ๋ ๊ทธ๋ฃน ์๋น์ค๋ฅผ ์ง์ํ๊ณ , ์ผ๋ ๋ค ํต์ ๊ณผ ๊ฐ์ ์๋ก์ด ๊ธฐ๋ฅ์ ์คํฌ๋ฆฝํธ ์ธ์ด์ ๋ฐ์ํ์๋ค. ๋ฐ๋ผ์ ํ์ฅ๋ ์์ ์์ค์ ์คํฌ๋ฆฝํธ ์ธ์ด๋ ๋น์ ๋ฌธ๊ฐ๋ ๋ค์ํ ์ ํ์ ํ๋ ฅ ์๋ฌด๋ฅผ ์ฝ๊ฒ ๋ช
์ธํ ์ ์๋ค.
๋ก๋ด์ ํ์๋ฅผ ํ๋ก๊ทธ๋๋ฐํ๊ธฐ ์ํด ๋ค์ํ ์ํํธ์จ์ด ๊ฐ๋ฐ ํ๋ ์์ํฌ๊ฐ ์ฐ๊ตฌ๋๊ณ ์๋ค. ํนํ ์ฌ์ฌ์ฉ์ฑ๊ณผ ํ์ฅ์ฑ์ ์ค์ ์ผ๋ก ๋ ์ฐ๊ตฌ๋ค์ด ์ต๊ทผ ๋ง์ด ์ฌ์ฉ๋๊ณ ์์ง๋ง, ๋๋ถ๋ถ์ ์ด๋ค ์ฐ๊ตฌ๋ ๋ฆฌ๋
์ค ์ด์์ฒด์ ์ ๊ฐ์ด ๋ง์ ํ๋์จ์ด ์์์ ํ์๋ก ํ๋ ์ด์์ฒด์ ๋ฅผ ๊ฐ์ ํ๊ณ ์๋ค. ๋ํ, ํ๋ก๊ทธ๋จ์ ๋ถ์ ๋ฐ ์ฑ๋ฅ ์์ธก ๋ฑ์ ๊ณ ๋ คํ์ง ์๊ธฐ ๋๋ฌธ์, ์์ ์ ์ฝ์ด ์ฌํ ํฌ๊ธฐ๊ฐ ์์ ๋ก๋ด์ ์ํํธ์จ์ด๋ฅผ ๊ฐ๋ฐํ๊ธฐ์๋ ์ด๋ ต๋ค. ๊ทธ๋์ ๋ณธ ์ฐ๊ตฌ์์๋ ์๋ฒ ๋๋ ์ํํธ์จ์ด๋ฅผ ์ค๊ณํ ๋ ์ฐ์ด๋ ์ ํ์ ์ธ ๋ชจ๋ธ์ ์ด์ฉํ๋ค. ์ด ๋ชจ๋ธ์ ์ ์ ๋ถ์๊ณผ ์ฑ๋ฅ ์์ธก์ด ๊ฐ๋ฅํ์ง๋ง, ๋ก๋ด์ ํ์๋ฅผ ํํํ๊ธฐ์๋ ์ ์ฝ์ด ์๋ค. ๊ทธ๋์ ๋ณธ ๋
ผ๋ฌธ์์ ์ธ๋ถ์ ์ด๋ฒคํธ์ ์ํด ์ํ ์ค๊ฐ์ ํ์๋ฅผ ๋ณ๊ฒฝํ๋ ๋ก๋ด์ ์ํด ์ ํ ์ํ ๋จธ์ ๋ชจ๋ธ๊ณผ ๋ฐ์ดํฐ ํ๋ก์ฐ ๋ชจ๋ธ์ด ๊ฒฐํฉํ์ฌ ๋์ ํ์๋ฅผ ๋ช
์ธํ ์ ์๋ ํ์ฅ๋ ๋ชจ๋ธ์ ์ ์ฉํ๋ค. ๊ทธ๋ฆฌ๊ณ ๋ฅ๋ฌ๋๊ณผ ๊ฐ์ด ๊ณ์ฐ๋์ ๋ง์ด ํ์๋ก ํ๋ ์์ฉ์ ๋ถ์ํ๊ธฐ ์ํด, ๋ฃจํ ๊ตฌ์กฐ๋ฅผ ๋ช
์์ ์ผ๋ก ํํํ ์ ์๋ ๋ชจ๋ธ์ ์ ์ํ๋ค. ๋ง์ง๋ง์ผ๋ก ์ฌ๋ฌ ๋ก๋ด์ ํ์
์ด์ฉ์ ์ํด ๋ก๋ด ์ฌ์ด์ ๊ณต์ ๋๋ ์ ๋ณด๋ฅผ ๋ํ๋ด๊ธฐ ์ํด ๋ ๊ฐ์ง ๋ชจ๋ธ์ ์ฌ์ฉํ๋ค. ๋จผ์ ์ค์์์ ๊ณต์ ์ ๋ณด๋ฅผ ๊ด๋ฆฌํ๊ธฐ ์ํด ๋ผ์ด๋ธ๋ฌ๋ฆฌ ํ์คํฌ๋ผ๋ ํน๋ณํ ํ์คํฌ๋ฅผ ํตํด ๊ณต์ ์ ๋ณด๋ฅผ ๋ํ๋ธ๋ค. ๋ํ, ๋ก๋ด์ด ์์ ์ ์ ๋ณด๋ฅผ ๊ฐ๊น์ด ๋ก๋ด๋ค๊ณผ ๊ณต์ ํ๊ธฐ ์ํด ๋ฉํฐ์บ์คํ
์ ์ํ ์๋ก์ด ํฌํธ๋ฅผ ์ถ๊ฐํ๋ค. ์ด๋ ๊ฒ ํ์ฅ๋ ์ ํ์ ์ธ ๋ชจ๋ธ์ ์ค์ ๋ก๋ด ์ฝ๋๋ก ์๋ ์์ฑ๋์ด, ์ํํธ์จ์ด ์ค๊ณ ์์ฐ์ฑ ๋ฐ ๊ฐ๋ฐ ํจ์จ์ฑ์ ์ด์ ์ ๊ฐ์ง๋ค.
๋น์ ๋ฌธ๊ฐ๊ฐ ๋ช
์ธํ ์คํฌ๋ฆฝํธ ์ธ์ด๋ ์ ํ์ ์ธ ํ์คํฌ ๋ชจ๋ธ๋ก ๋ณํํ๊ธฐ ์ํด ์ค๊ฐ ๋จ๊ณ์ธ ์ ๋ต ๋จ๊ณ๋ฅผ ์ถ๊ฐํ์๋ค. ์ ์ํ๋ ๋ฐฉ๋ฒ๋ก ์ ํ๋น์ฑ์ ๊ฒ์ฆํ๊ธฐ ์ํด, ์๋ฎฌ๋ ์ด์
๊ณผ ์ฌ๋ฌ ๋์ ์ค์ ๋ก๋ด์ ์ด์ฉํ ํ์
ํ๋ ์๋๋ฆฌ์ค์ ๋ํด ์คํ์ ์งํํ์๋ค.In the near future, it will be common that a variety of robots are cooperating to perform a mission in various fields. There are two software challenges when deploying collaborative robots: how to specify a cooperative mission and how to program each robot to accomplish its mission. In this paper, we propose a novel software development framework that separates mission specification and robot behavior programming, which is called service-oriented and model-based (SeMo) framework. Also, it can support distributed robot systems, swarm robots, and their hybrid.
For mission specification, a novel scripting language is proposed with the expression capability. It involves team composition and service-oriented behavior specification of each team, allowing dynamic mode change of operation and multi-tasking. Robots are grouped into teams, and the behavior of each team is defined with a composite service. The internal behavior of a composite service is defined by a sequence of services that the robots will perform. The notion of plan is applied to express multi-tasking. And the robot may have various operating modes, so mode change is triggered by events generated in a composite service. Moreover, to improve the robustness, scalability, and flexibility of robot collaboration, the high-level mission scripting language is extended with new features such as team hierarchy, group service, one-to-many communication. We assume that any robot fails during the execution of scenarios, and the grouping of robots can be made at run-time dynamically. Therefore, the extended mission specification enables a casual user to specify various types of cooperative missions easily.
For robot behavior programming, an extended dataflow model is used for task-level behavior specification that does not depend on the robot hardware platform. To specify the dynamic behavior of the robot, we apply an extended task model that supports a hybrid specification of dataflow and finite state machine models. Furthermore, we propose a novel extension to allow the explicit specification of loop structures. This extension helps the compute-intensive application, which contains a lot of loop structures, to specify explicitly and analyze at compile time. Two types of information sharing, global information sharing and local knowledge sharing, are supported for robot collaboration in the dataflow graph. For global information, we use the library task, which supports shared resource management and server-client interaction. On the other hand, to share information locally with near robots, we add another type of port for multicasting and use the knowledge sharing technique. The actual robot code per robot is automatically generated from the associated task graph, which minimizes the human efforts in low-level robot programming and improves the software design productivity significantly.
By abstracting the tasks or algorithms as services and adding the strategy description layer in the design flow, the mission specification is refined into task-graph specification automatically. The viability of the proposed methodology is verified with preliminary experiments with three cooperative mission scenarios with heterogeneous robot platforms and robot simulator.Chapter 1. Introduction 1
1.1 Motivation 1
1.2 Contribution 7
1.3 Dissertation Organization 9
Chapter 2. Background and Existing Research 11
2.1 Terminologies 11
2.2 Robot Software Development Frameworks 25
2.3 Parallel Embedded Software Development Framework 31
Chapter 3. Overview of the SeMo Framework 41
3.1 Motivational Examples 45
Chapter 4. Robot Behavior Programming 47
4.1 Related works 48
4.2 Model-based Task Graph Specification for Individual Robots 56
4.3 Model-based Task Graph Specification for Cooperating Robots 70
4.4 Automatic Code Generation 74
4.5 Experiments 78
Chapter 5. High-level Mission Specification 81
5.1 Service-oriented Mission Specification 82
5.2 Strategy Description 93
5.3 Automatic Task Graph Generation 96
5.4 Related works 99
5.5 Experiments 104
Chapter 6. Conclusion 114
6.1 Future Research 116
Bibliography 118
Appendices 133
์์ฝ 158Docto
High-Performance Modelling and Simulation for Big Data Applications
This open access book was prepared as a Final Publication of the COST Action IC1406 โHigh-Performance Modelling and Simulation for Big Data Applications (cHiPSet)โ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications
High-Performance Modelling and Simulation for Big Data Applications
This open access book was prepared as a Final Publication of the COST Action IC1406 โHigh-Performance Modelling and Simulation for Big Data Applications (cHiPSet)โ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications
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