Organizational Unit:

Institute for Robotics and Intelligent Machines (IRIM)

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https://hdl.handle.net/1853/70657

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Organizational Unit

Biorobotics and Human Modeling Lab

Organizational Unit

Humanoid Robotics Laboratory

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Now showing 1 - 10 of 10

Human-Expert Data Aggregation for Situation-Based Automation of Regenerative Life Support Systems

(Georgia Institute of Technology, 2012-07) Drayer, Gregorio E. ; Howard, Ayanna M.

Regenerative life support systems (RLSS) introduce novel challenges for the development of automation systems given the emerging behaviors that result from incremental system closure. Switching control paradigms offer the ability to manage such uncertainty by allowing flexibility into the control path, enabling for autonomy modes that depend on the situation of the system. Previous research proposed a granular approach that combines sensor information to define operation conditions and act upon them. It makes use of fuzzy associative memories (FAM) to define the pairs (Situation, Controller) that assign control actions to each situation. The FAM are composed granules that represent situations in which the autonomous system may operate. One of the challenges of this approach is the combinatorial explosion that arises for large numbers of sensors. Human-system interaction offers a solution to this problem and, for such purpose, this paper elaborates on the aggregation of human-expert data to obtain the granular structure of the FAM. The aggregation process consists of an optimization process based on particle swarms. The result is a three dimensional array with parameters that define n-dimensional non-interactive granules. Two alternatives are presented in this paper: (1) a four-dimensional optimization algorithm to obtain normal fuzzy sets, and (2) a five-dimensional alternative that results in subnormal fuzzy sets. The results were obtained with simulations of an aquatic habitat that serves as a small-scale model of a RLSS. The discussion elaborates on which of the two alternatives may be better suited for applications in situation assessment and automation.
Development and Evaluation of User Interfaces for Situation Observability in Life Support Systems

(Georgia Institute of Technology, 2012-07) Taylor, Hilary ; Lee, Benjamin ; Jhingory, Jerome ; Drayer, Gregorio E. ; Howard, Ayanna M.

Slow-changing characteristics of controlled environmental systems, the increasing availability of sensor information, and the need to avoid human error makes the manual control of these systems ever more challenging to human operators both on the ground and inflight. Automation systems are better suited to make some of these repetitive and critical tasks more reliable and less time-consuming. However, along with achieving reliable automation, it is beneficial to allow human operators to intervene if a problem occurs within the system, especially in order to take manual control upon anomalies. Ecological interface design, which focuses on the flow of information between the system and the human rather than in particular processes that constitute them, offers a solution to this problem. Such interfaces are user-centered and allow the human operators to gain situation awareness and intervene if necessary. This paper makes use of a granular multi-sensor data fusion method to develop ecological user interfaces for a small-scale life support system. The methodology is applied to the model of a small-scale aquatic habitat working as a groundbased bioregenerative life support system. Three ecological user interfaces were designed and tested on eight non-expert users. Results show the advantage of using situation-rich signals generated by the granular multi-sensor data fusion method that simplifies displays of information to allow for the future design of decision support tools.
Educational Value of Experiments on Life Support Systems with Ground-Based Aquatic Habitats

(Georgia Institute of Technology, 2012-07) Drayer, Gregorio E. ; Howard, Ayanna M.

On April 10th 2010, at the Kennedy Space Center, President Barack Obama pronounced his “Remarks on Space Exploration in the 21st Century." The President included closed- loop life support systems (LSS) as a technology that “can help improve daily lives of people here on Earth, as well as testing and improving upon capabilities in space." A challenge to enable research on LSS is the need for educational capacities that may open up opportunities for teachers and students to teach, learn, and experiment with a small-scale version of these systems. Such is the case in higher-education institutions with programs in life sciences and engineering. These may have educational platforms available in their laboratories to, for example, study attributes of robustness or optimality in controllers driving servomechanisms and electric motors, but there is no small-scale platform available to study the ecophysiological performance of higher plants in an isolated artificial ecosystem. This paper presents aquatic habitats as educational platforms for experiments in closed-loop LSS, and the lessons learned while working with undergraduate students at the Human-Automation Systems Lab of the Georgia Institute of Technology. It presents the challenges that these systems pose to students in engineering and sciences, and highlights the opportunities to support higher-education-level teaching and learning of concepts in science, technology, engineering, and mathematics (STEM) fields.
A Granular Multi-Sensor Data Fusion Method for Situation Observability in Life Support Systems

(Georgia Institute of Technology, 2012-07) Drayer, Gregorio E. ; Howard, Ayanna M.

Slow-changing characteristics of controlled environmental systems and the increasing availability of data from sensors and measurements offer opportunities for the development of computational methods to enhance situation observability, decrease human workload, and support real-time decision making. Multi-sensor data fusion, which combines observations and measurements from di_erent sources to provide a complete description of a system and its environment, can be used in user-centered interfaces in support situation awareness and observability. Situation observability enables humans to perceive and comprehend the state of the system at a given instant, and helps human operators to decide what actions to take at any given time that may affect the projection of such state into the near future. This paper presents a multi-sensor data fusion method that collects discrete human-inputs and measurements to generate a granular perception function that supports situation observability. These human-inputs are situation-rich, meaning they combine measurements defining the operational condition of the system with a subjective assessment of its situation. As a result, the perception function produces situation-rich signals that may be employed in user-interfaces or in adaptive automation. The perception function is a fuzzy associative memory (FAM) composed of a number of granules equal to the number of situations that may be detected by human-experts; its development is based on their interaction with the system. The human-input data sets are transformed into a granular structure by an adaptive method based on particle swarms. The paper proposed describes the multi-sensor data fusion method and its application to a ground-based aquatic habitat working as a small-scale environmental system.
Ecophysiological Models in Simulations of an Aquatic Habitat for Closed-Loop Life Support Research

(Georgia Institute of Technology, 2012-07) Drayer, Gregorio E. ; Howard, Ayanna M.

A limitation in closed-loop life support system research is the no-availability of small-scale experimental capacities that may help to better understand the challenges in system closure, integration, and control. Ground-based aquatic habitats are an option for small-scale research relevant to bioregenerative life support systems (BLSS), given that they can operate as self-contained systems enclosing a habitat composed of various species in a single volume of water. This paper elaborates on the modeling, design, and simulation of a recon gurable aquatic habitat for experiments in BLSS and automation. It focuses in the process of respiration: higher plants of the species Bacopa Monnieri produce O2 for snails of the genus Pomacea. The snails consume the O2 and generate CO2, which is used by the plants in combination with radiant energy to generate O2 through the process of photosynthesis. The paper expands the description of biological processes by introducing models of ecophysiological phenomena of the organisms involved. The model of the plants include a description of the rate of CO2 assimilation as a function of irradiance. The snails instead are modeled through their rate of consumption, treated as a combination of a constant and a random variable to account for changes in metabolic rates and aestivation.The latter consists in brief periods of torpor of the metabolism of the snails in which oxygen consumption is considerably reduced. Simulations and validation runs with hardware show how these phenomena may act as disturbances for the control mechanisms that aim to maintain safe concentration levels of dissolved oxygen in the habitat.
Educational Potential of Experiments on Life Support Systems with Ground-Based Aquatic Habitats

(Georgia Institute of Technology, 2012-05) Drayer, Gregorio E. ; Howard, Ayanna M.

On April 10th 2010, at the Kennedy Space Center, President Barack Obama pronounced his “Remarks on Space Exploration in the 21st Century.” In his speech, the President included life support systems as a technology that “can help improve daily lives of people here on Earth, as well as testing and improving upon capabilities in space.” One of challenges to enable students to conduct research on life support systems is the need for educational capabilities that open up opportunities to learn and experiment with small-scale versions of these systems. Such is the case in higher-education institutions with programs that include courses chemistry, biology, electronics and computer science. These institutions may have educational platforms in their labs to study attributes of robustness or optimality of controllers driving servomechanisms and electric motors, but there is not one that may allow the study of ecophysiological performance of higher plants in closed-loop life support systems, for example. This paper presents aquatic habitats as educational platforms for experiments in life support systems, and the lessons learned while working with undergraduate students at the Human-Automation Systems Lab of the Georgia Institute of Technology. It presents the challenges that these systems pose to students in engineering and sciences, and highlights the opportunities to support higher-education-level teaching and learning of concepts in mathematics, physics, chemistry, and biology.
A Granular Multi-Sensor Data Fusion Method for Life Support Systems that Enhances Situation Awareness

(Georgia Institute of Technology, 2012-05) Drayer, Gregorio E. ; Howard, Ayanna M.

Slow-changing characteristics of controlled environmental systems and the increasing availability of data from sensors and measurements o er opportunities for the development of computational methods that enhance situation observability, decrease human workload, and support real-time decision making. Some of these methods are known as multi-sensor data fusion; they combine measurements from multiple sources to produce a more concise representation of the information contained therein. Such information can be used to design better user-centered interfaces, allowing human operators to maintain situation awareness. Situation observability enables humans to perceive and comprehend the state the system at a given instant of time, and helps human operators in deciding what actions to take at any given time that may a ect the projection of such state into the near future. This paper presents a multi-sensor data fusion method that makes use of a collection of discrete human-inputs and measurements to generate a granular perception function that supports situation awareness. These human-inputs are situation- rich, meaning that they combine measurements defining the operational condition of the system with a subjective assessment of its situation. As a result, the perception function produces situation-rich signals that may be used in ecological human-interfaces or as a switching mechanism in automation strategies and fail-safe/fail-op mechanisms. The granular perception function is a fuzzy associative memory composed of a number of granules equal to the number situations that may be detected by human observers; its development is based in the interaction of human operators with the system. The human-input data sets are transformed into a fuzzy associative memory by an adaptive method based on particle swarms. The paper describes the multi-sensor data fusion method proposed and its application to a ground-based aquatic habitat working as a small-scale environmental system. Results show how this approach helps to generate signals that enhance the situation observability of the aquatic habitat.
A granular approach to the automation of bioregenerative life support systems that enhances situation awareness

(Georgia Institute of Technology, 2012-03) Drayer, Gregorio E. ; Howard, Ayanna M.

Bioregenerative life support systems introduce novel challenges for the development of model-based approaches to their control given the varying characteristic of the biological processes that constitute them. Switching control paradigms provide an alternative to manage such uncertainty by allowing flexibility into the control path, enabling different control modes depending on the situation of the system. This paper presents a perception-based approach that combines sensor information to define those conditions and act upon them. Combined sensor information creates sensing spaces in which the operational conditions of the system are found. The decomposition of the sensing spaces into perceptual elements or granules allows for situation assessment, system integration strategies, and the implementation of fail-safe and fail-operational mechanisms-all these critical in a wider range of complex socio-technical systems. This paper proposes the use of intelligent agents based on fuzzy associative memories (FAM) to decompose sensing spaces into granular structures composed of n-dimensional non-interactive fuzzy sets. Granular structures resulting from such decomposition allow for the incremental development and automation of the system by associating a control task to each operational condition. Furthermore, the real-time information obtained from the membership value of the granules may provide a resource for situational awareness and for the design of new ecological interfaces to enhance human-system interaction and real-time decision making. The approach presented in this paper is applied to the dynamic model of a reconfigurable aquatic habitat that serves as a small-scale bioregenerative test bed for life support control research. Results show how information generated by the FAM enhances the situation observability of the system.
A FAM-based Switched Control Approach for the Automation of Bioregenerative Life Support Systems

(Georgia Institute of Technology, 2011-07) Drayer, Gregorio E. ; Howard, Ayanna M.

The automation of bioregenerative life support systems poses challenges for the development of model-based approaches given the varying characteristic of the biological processes that constitute them. Switching control paradigms offer an alternative for the management of such uncertainty by introducing flexibility into the control path and allowing for different control modes depending on the operational conditions of the system. This paper presents a perception-based switching control strategy that makes use of sensor information to define and act upon those conditions. Abundant sensor information gives rise to sensing spaces in which the operational conditions of the system are found. A decomposition of the sensing spaces into perceptual elements allows for automation and integration strategies, and for the implementation of fail-safe and fail-operational mechanisms. This paper proposes the use of agents based on fuzzy associative memories to decompose sensing spaces into granular structures composed of n-dimensional non-interactive fuzzy sets. The granular structures allow for the incremental development and automation of the system by associating a control task to each granule. The method presented in this paper is applied to the dynamic model of a reconfigurable aquatic habitat. The habitat serves as a small-scale bioregenerative test bed for life support control research. The method used in this paper may also enable cognitive resources to enhance human interaction with the system.
Modeling, Design and Simulation of a Reconfigurable Aquatic Habitat for Life Support Control Research

(Georgia Institute of Technology, 2011-07) Drayer, Gregorio E. ; Howard, Ayanna M.

This paper presents the design, modeling, and simulation of a reconfigurable aquatic habitat for experiments in regenerative life support automation; it supports the use of aquatic habitats as a small-scale approach to automation experiments relevant to larger- scale regenerative life support systems. The habitat consists of a ten-gallon tank with four compartments, containing animal and botanical elements. The water volume serves as the medium through which life-support compounds, like oxygen, are transferred between organisms. A motorized hatch allows reconfiguration of the system to allow or prevent the exchange of gases with the atmosphere, and enables the study of fail-safe automation mechanisms. Sensors and actuators measure and intervene to regulate life support variables in the water. The model serves as an analytical reference for future tests in hardware settings, and to test advanced control architectures and policies that enable the system to operate safely and with increasing levels of autonomy, allowing for human intervention if necessary. The goal of the aquatic habitat is to enable life support control concepts that may be challenging to test in larger-scale life support systems. The mathematical description of the dynamic model of the system is presented in this paper with results from simulations of a distributed control approach applied to the habitat.