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Questions and areas of study that need to be persued in order to develope a Controlled Ecological Life Support System are posed. Research topics needing attention are grouped under various leadings: ecology, genetics, plant pathology, cybernetics, chemistry, computer science, fluid dynamics, optics, and solid-state physics.
The automated and robotic systems requirements of the NASA Space Station are prompted by maintenance, repair, servicing and assembly requirements. Trend analyses, fault diagnoses, and subsystem status assessments for the Station's electrical power, guidance, navigation, control, data management and environmental control subsystems will be undertaken by cybernetic expert systems; this will reduce or eliminate on-board or ground facility activities that would otherwise be essential, enhancing system productivity. Additional capabilities may also be obtained through the incorporation of even a limited amount of artificial intelligence in the controllers of the various Space Station systems.
A bioregenerative life support system designed to address the fundamental requirements of a functioning independent lunar base is presented in full. Issues to be discussed are associated with CELSS weight, volume and cost of operation. The fundamental CELSS component is a small, highly automated module containing plants which photosynthesize and provide the crew with food, water and oxygen. Hydrogen, nitrogen and carbon dioxide will be initially brought in from earth, recycled and their waste products conserved. As the insufficiency of buffers necessitates stringent cybernetic control, a stable state will be maintained by computer control. Through genetic engineering and carbon dioxide, temperature, and nutrient manipulation, plant productivity can be increased, while the area necessary for growth and illumination energy decreased. In addition, photosynthetic efficiency can be enhanced through lamp design, fiber optics and the use of appropriate wavelengths. Crop maintenance will be performed by robotics, as a means of preventing plant ailments.
The PER-Force Handcontroller was originally developed for the International Space Station under a Johnson Space Center Small Business Innovation Research (SBIR) contract. Produced by Cybernet Systems Corporation, the unit is a force-reflecting system that manipulates robots or objects by "feel." The Handcontroller moves in six degrees of freedom, with real and virtual reality forces simulated by a 3-D molecular modeling software package. It is used in molecular modeling in metallurgy applications, satellite docking research, and in research on military unmanned ground vehicles.
The PER-Force robotic handcontroller provides a sense of touch or "feel" to an operator manipulating robots. The force simulation and wide range of motion greatly enhances the efficiency of robotic and computer operations. The handcontroller was developed for the Space Station by Cybernet Systems Corporation under a Small Business Innovation Research (SBIR) contract. Commercial applications include underwater use, underground excavations, research laboratories, hazardous waste handling and in manufacturing operations in which it is unsafe or impractical for humans to work.
Physiological self-regulation training is a behavioral medicine intervention that has demonstrated capability to improve psychophysiological coping responses to stressful experiences and to foster optimal behavioral and cognitive performance. Once developed, these psychophysiological skills require regular practice for maintenance. A concomitant benefit of these physiologically monitored practice sessions is the opportunity to track crew psychophysiological responses to the challenges of the practice task in order to detect shifts in adaptability that may foretell performance degradation. Long-duration missions will include crew recreation periods that will afford physiological self-regulation training opportunities. However, to promote adherence to the regimen, the practice experience that occupies their recreation time must be perceived by the crew as engaging and entertaining throughout repeated reinforcement sessions on long-duration missions. NASA biocybernetic technologies and publications have developed a closed-loop concept that involves adjusting or modulating (cybernetic, for governing) a person's task environment based upon a comparison of that person's physiological responses (bio-) with a training or performance criterion. This approach affords the opportunity to deliver physiological self-regulation training in an entertaining and motivating fashion and can also be employed to create a conditioned association between effective performance state and task execution behaviors, while enabling tracking of individuals psychophysiological status over time in the context of an interactive task challenge. This paper describes the aerospace spin-off technologies in this training application area as well as the current spin-back application of the technologies to long-duration missions - the Recreation Embedded State Tuning for Optimal Readiness and Effectiveness (RESTORE) concept. The RESTORE technology is designed to provide a physiological self-regulation training countermeasure for maintaining and reinforcing cognitive readiness, resilience under psychological stress, and effective mood states in long-duration crews. The technology consists of a system for delivering physiological self-regulation training and for tracking crew central and autonomic nervous system function; the system interface is designed to be experienced as engaging and entertaining throughout repeated training sessions on long-duration missions. Consequently, this self-management technology has threefold capability for recreation, behavioral health problem prophylaxis and remediation, and psychophysiological assay. The RESTORE concept aims to reduce the risk of future manned exploration missions by enhancing the capability of individual crewmembers to self-regulate cognitive states through recreation-embedded training protocols to effectively deal with the psychological toll of long-duration space flight.
The study described in this paper investigated the effects of two different hexapod motion configurations on the training and transfer of training of a simultaneous roll and pitch control task. Pilots were divided between two groups which trained either under a baseline hexapod motion condition, with motion typically provided by current training simulators, or an optimized hexapod motion condition, with increased fidelity of the motion cues most relevant for the task. All pilots transferred to the same full-motion condition, representing motion experienced in flight. A cybernetic approach was used that gave insights into the development of pilots use of visual and motion cues over the course of training and after transfer. Based on the current results, neither of the hexapod motion conditions can unambiguously be chosen as providing the best motion for training and transfer of training of the used multi-axis control task. However, the optimized hexapod motion condition did allow pilots to generate less visual lead, control with higher gains, and have better disturbance-rejection performance at the end of the training session compared to the baseline hexapod motion condition. Significant adaptations in control behavior still occurred in the transfer phase under the full-motion condition for both groups. Pilots behaved less linearly compared to previous single-axis control-task experiments; however, this did not result in smaller motion or learning effects. Motion and learning effects were more pronounced in pitch compared to roll. Finally, valuable lessons were learned that allow us to improve the adopted approach for future transfer-of-training studies.
In this presentation, I will first give an overview of some of the research being performed at the Human Systems Integration Division of NASAs Ames Research Center. In the second part of the presentation, I will discuss two studies that investigate the effects of simulator motion on pilot transfer of training. In a study funded by the Federal Aviation Administration, task performance is used to investigate the effects of motion on the training of 4 challenging tasks in a transport category aircraft. In addition, the effectiveness of recently proposed objective motion cueing criteria for training simulators are evaluated. The second study, part of the NASA Aeronautics Research Program, focuses on optimizing simulator motion for maximum transfer of stall recovery training and utilizes a cybernetic approach to measure transfer of training.
Human operators interacting with machines or computers continually adapt to the needs of the system ideally resulting in optimal performance. In some cases, however, deteriorated performance is an outcome. Adaptation to the situation is a strength expected of the human operator which is often accomplished by the human through self-regulation of mental state. Adaptation is at the core of the human operator's activity, and research has demonstrated that the implementation of a feedback loop can enhance this natural skill to improve training and human/machine interaction. Biocybernetic adaptation involves a “loop upon a loop,” which may be visualized as a superimposed loop which senses a physiological signal and influences the operator’s task at some point. Biocybernetic adaptation in, for example, physiologically adaptive automation employs the “steering” sense of “cybernetic,” and serves a transitory adaptive purpose – to better serve the human operator by more fully representing their responses to the sys- tem. The adaptation process usually makes use of an assessment of transient cog- nitive state to steer a functional aspect of a system that is external to the operator’s physiology from which the state assessment is derived. Therefore, the objective of this paper is to detail the structure of biocybernetic systems regarding the level of engagement of interest for adaptive systems, their processing pipeline, and the adaptation strategies employed for training purposes, in an effort to pave the way towards machine awareness of human state for self-regulation and improved operational performance.
In long-duration space missions, skill retention and generalizability become ever more important as mission length increases, for it is through these capabilities that astronaut crews achieve autonomy. A framework is proposed for future research based on three types of training commonly found in operational scenarios: initial training, refresher training, and just-in-time training. One of the key points of interest identified from the body of literature is the need for a structured and quantifiable approach to characterize skill decay. Such an approach would allow gaining a deeper understanding of the mechanisms through which skill decay operates. Because simulators are used extensively in all types of training, the effects of simulator fidelity on skill retention and generalizability are paramount to understand. Some common fidelity types are defined and their effects on the human operator are discussed. From a review of the literature on all these topics, several research gaps were identified that led to three key opportunities for future research: (1) developing skill decay functions provides theoretical insights into skill decay and allows for several practical applications, such as planning refresher training, (2) the effects of simulator fidelity on skill decay functions should be investigated empirically, and (3) investigating the generalizability of skills learned in initial training to other tasks could provide space crews with greater autonomy. A cybernetic approach might give new insights.
This paper presents the results of an experiment that was performed at NASA Ames Research Center using 18 participants in two different groups who trained a task for ten days, with the goal of identifying how skill generalization would occur between two similar tasks of varying difficulty. A cybernetic approach was used. The first group was trained in a simple one-dimensional tracking task and transferred to a difficult two-dimensional tracking task. For the second group, this was reversed. Training with a simple task before transferring to the difficult task resulted in a slower convergence to final performance. However, it did allow participants to start with a better initial performance in the difficult task. Furthermore, after training with a simple task, participants controlled with a higher gain and generated lower lead time constants. However, possibly due to the number of participants, this experiment did not find any statistical evidence to support the conclusion that training with a simple task version helps in learning a more complex task.
In long-duration space missions, skill retention and generalizability become ever more important as mission length increases, for it is through these capabilities that astronaut crews achieve autonomy. A framework is proposed for future research based on three types of training commonly found in operational scenarios: initial training, refresher training, and just-in-time training. One of the key points of interest identified from the body of literature is the need for a structured and quantifiable approach to characterize skill decay. Such an approach would allow gaining a deeper understanding of the mechanisms through which skill decay operates. Because simulators are used extensively in all types of training, the effects of simulator fidelity on skill retention and generalizability are paramount to understand. Some common fidelity types are defined and their effects on the human operator are discussed. From a review of the literature on all these topics, several research gaps were identified that led to three key opportunities for future research: (1) developing skill decay functions provides theoretical insights into skill decay and allows for several practical applications, such as planning refresher training, (2) the effects of simulator fidelity on skill decay functions should be investigated empirically, and (3) investigating the generalizability of skills learned in initial training to other tasks could provide space crews with greater autonomy. A cybernetic approach might give new insights.
This paper presents the results of an experiment that was performed at NASA Ames Research Center using 18 participants in two different groups who trained a task for ten days, with the goal of identifying how skill generalization would occur between two similar tasks of varying difficulty. A cybernetic approach was used. The first group was trained in a simple one-dimensional tracking task and transferred to a difficult two-dimensional tracking task. For the second group, this was reversed. Training with a simple task before transferring to the difficult task resulted in a slower convergence to final performance. However, it did allow participants to start with a better initial performance in the difficult task. Furthermore, after training with a simple task, participants controlled with a higher gain and generated lower lead time constants. However, possibly due to the number of participants, this experiment did not find any statistical evidence to support the conclusion that training with a simple task version helps in learning a more complex task.
The United Nations states that Earth’s population is expected to reach just under 10 billion people (9.7) by the year 2050. To meet the demands of 10 billion people, governments, multinational corporations and global leaders are relying on autonomy and technological advances to augment and/or accommodate human efforts to meet the required needs of daily living. Genetically modified organisms (GMOs), Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) gene-edited plants and cloning will be utilized to expand human food supply. Biomimetic implants are expected to improve life expectancy with 3D printed body parts. Human functioning will be extended with wearables and cybernetic implants continuing humanity’s path toward transhumanism. Families will be strengthened with 3 parent households. Disease will surely be eradicated using the CRISPR-CAS9 genetic engineering revolution to design out undesirable human traits and to design in new capabilities. With autonomous cars, trucks and buses on our roads and on-demand autonomous aircraft delivering pizzas, medical prescriptions and groceries in the air and multi-planet vehicles traversing space, utopia will finally arrive! Or will it? All of these powerful, man-made, technological systems will experience unintended consequences with certainty. Instead of over-reacting with hysteria and fear, we should be seeking answers to the following questions - “What skills are required to architect socially-healthy technological systems for 2050?” “What mindsets should we embody to ameliorate hubris syndrome and to build our future technological systems with deliberation, soberness and social responsibility?”
Cognitive state detection and its relationship to observable physiologically telemetry has been utilized for many human-machine and human-cybernetic applications. This paper aims at understanding and addressing if there are unique psychophysiological patterns over time, a ”physiological temporal fingerprint”, that is associated with specific cognitive states. This preliminary work involves commercial airline pilots completing experimental benchmark task inductions of three cognitive states: 1) Channelized Attention (CA); 2) High Workload (HW); and 3) Low Workload (LW). We approach this objective by modeling these "fingerprints" through the use of Hidden Markov Models and Entropy analysis to evaluate if the transitions over time are complex or rhythmic/predictable by nature. Our results indicate that cognitive states do have unique complexity of physiological sequences that are statistically different from other cognitive states. More specifically, CA has a significantly higher temporal psychophysiological complexity than HW and LW in EEG and ECG telemetry signals. With regards to respiration telemetry, CA has a lower temporal psychophysiological complexity than HW and LW. Through our preliminary work, addressing this unique underpinning can inform whether these underlying dynamics can be utilized to understand how humans transition between cognitive states and for improved detection of cognitive states.
Space exploration missions will require combining human and technical subsystems into overall "crew systems" capable of performing under the rigorous conditions of outer space. This report describes substantive and conceptual relationships among humans, intelligent machines, and communication systems, and explores how these components may be combined to complement and strengthen one another. We identify key research issues in the combination of humans and technology and examine the role of individual differences, group processes, and environmental conditions. We conclude that a crew system is, in effect, a social cyborg, a living system consisting of multiple individuals whose capabilities are extended by advanced technology.
Unmanned Aerial Systems (UAS) can only be deployed if they can effectively complete their mission and respond to failures and uncertain environmental conditions while maintaining safety with respect to other aircraft as well as humans and property on the ground. We propose to design a real-time, onboard system health management (SHM) capability to continuously monitor essential system components such as sensors, software, and hardware systems for detection and diagnosis of failures and violations of safety or performance rules during the ight of a UAS. Our approach to SHM is three-pronged, providing: (1) real-time monitoring of sensor and software signals; (2) signal analysis, preprocessing, and advanced on-the- y temporal and Bayesian probabilistic fault diagnosis; (3) an unobtrusive, lightweight, read-only, low-power hardware realization using Field Programmable Gate Arrays (FPGAs) in order to avoid overburdening limited computing resources or costly re-certi cation of ight software due to instrumentation. No currently available SHM capabilities (or combinations of currently existing SHM capabilities) come anywhere close to satisfying these three criteria yet NASA will require such intelligent, hardwareenabled sensor and software safety and health management for introducing autonomous UAS into the National Airspace System (NAS). We propose a novel approach of creating modular building blocks for combining responsive runtime monitoring of temporal logic system safety requirements with model-based diagnosis and Bayesian network-based probabilistic analysis. Our proposed research program includes both developing this novel approach and demonstrating its capabilities using the NASA Swift UAS as a demonstration platform.
This paper describes the concept of moral hazard as applied to technologies that incorporate automation and autonomy. Moral hazard is said to exist when a party to a transaction feels more comfortable taking undue risks because another party will bear the costs if things go badly. As opposed to regular physical hazards, a moral hazard comes from within a person. In this paper, we reveal two categories of moral hazards related to autonomy. The first category of moral hazard occurs when the owner of the autonomy introduces an autonomous system without accepting the full responsibility for improper operation thereby shifting the risks from one party to another party. This category of moral hazard is similar to moral hazards experienced in other industries and can often be addressed through appropriate policy and establishing liability for irresponsible behavior. The issue becomes more complicated in cases where the operator of the autonomy may not have a full understanding of the system behavior. In the second category of moral hazard, risks are shifted from people to autonomy. In this category, the humans in proximity to the autonomous system begin to trust its behavior. Their behavior may change in that they may believe they are more insulated from harm and subsequently exhibit more risky behavior towards increasingly autonomous technologies. Mitigating this type of moral hazard may require the autonomy to possess certain design features to discourage this type of harmful human behavior so that humans do not suffer needlessly in their interactions with autonomous systems by placing inappropriate trust where that trust is neither warranted nor deserved.