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NASA’s Distributed Spacecraft Autonomy (DSA) Project developed and demonstrated software to enhance multi-spacecraft mission adaptability, efficiently allocate tasks between spacecraft using ad-hoc networking, and enable human-swarm commanding of Distributed Space Missions.
The problem of multi-task attention allocation with special reference to aircraft piloting is discussed with the experimental paradigm used to characterize this situation and the experimental results obtained in the first phase of the research. A qualitative description of an approach to mathematical modeling, and some results obtained with it are also presented to indicate what aspects of the model are most promising. Two appendices are given which (1) discuss the model in relation to graph theory and optimization and (2) specify the optimization algorithm of the model.
An important consideration when designing the interface to an intelligent system concerns function allocation between the system and the user. The display of information could be held constant, or 'fixed', leaving the user with the task of searching through all of the available information, integrating it, and classifying the data into a known system state. On the other hand, the system, based on its own intelligent diagnosis, could display only relevant information in order to reduce the user's search set. The user would still be left the task of perceiving and integrating the data and classifying it into the appropriate system state. Finally, the system could display the patterns of data. In this scenario, the task of integrating the data is carried out by the system, and the user's information processing load is reduced, leaving only the tasks of perception and classification of the patterns of data. Humans are especially adept at this form of display processing. Although others have examined the relative effectiveness of alphanumeric and graphical display formats, it is interesting to reexamine this issue together with the function allocation problem. Currently, Johnson Space Center is the test site for an intelligent Thermal Control System (TCS), TEXSYS, being tested for use with Space Station Freedom. Expert TCS engineers, as well as novices, were asked to classify several displays of TEXSYS data into various system states (including nominal and anomalous states). Three different display formats were used: fixed, subset, and graphical. The hypothesis tested was that the graphical displays would provide for fewer errors and faster classification times by both experts and novices, regardless of the kind of system state represented within the display. The subset displays were hypothesized to be the second most effective display format/function allocation condition, based on the fact that the search set is reduced in these displays. Both the subset and the graphic display conditions were hypothesized to be processed more efficiently than the fixed display conditions.
Improvements and advances in the development of computer architecture now provide innovative technology for the recasting of traditional sequential solutions into high-performance, low-cost, parallel system to increase system performance. Research conducted in development of specialized computer architecture for the algorithmic execution of an avionics system, guidance and control problem in real time is described. A comprehensive treatment of both the hardware and software structures of a customized computer which performs real-time computation of guidance commands with updated estimates of target motion and time-to-go is presented. An optimal, real-time allocation algorithm was developed which maps the algorithmic tasks onto the processing elements. This allocation is based on the critical path analysis. The final stage is the design and development of the hardware structures suitable for the efficient execution of the allocated task graph. The processing element is designed for rapid execution of the allocated tasks. Fault tolerance is a key feature of the overall architecture. Parallel numerical integration techniques, tasks definitions, and allocation algorithms are discussed. The parallel implementation is analytically verified and the experimental results are presented. The design of the data-driven computer architecture, customized for the execution of the particular algorithm, is discussed.
The preliminary results of a research program investigating the use of added tasks to evaluate mental workload are reviewed. The focus of the first studies was a reappraisal of the traditional secondary task logic that encouraged the use of low-priority instructions for the added task. It was believed that such low-priority tasks would encourage subjects to split their available resources among the two tasks. The primary task would be assigned all the resources it needed, and any remaining reserve capacity would be assigned to the secondary task. If the model were correct, this approach was expected to combine sensitivity to primary task difficulty with unintrusiveness to primary task performance. The first studies of the current project demonstrated that a high-priority added task, although intrusive, could be more sensitive than the traditional low-priority secondary task. These results suggested that a more appropriate model of the attentional effects associated with added task performance might be based on capacity switching, rather than the traditional optimal allocation model.
The human factors issues associated with the use of voice technology in the cockpit are summarized. The formulation of the LHX avionics suite is described and the allocation of tasks to voice in the cockpit is discussed. State-of-the-art speech recognition technology is reviewed. Finally, a questionnaire designed to tap pilot opinions concerning the allocation of tasks to voice input and output in the cockpit is presented. This questionnaire was designed to be administered to operational AH-1G Cobra gunship pilots. Half of the questionnaire deals specifically with the AH-1G cockpit and the types of tasks pilots would like to have performed by voice in this existing rotorcraft. The remaining portion of the questionnaire deals with an undefined rotorcraft of the future and is aimed at determining what types of tasks these pilots would like to have performed by voice technology if anything was possible, i.e. if there were no technological constraints.
Pilots were required to access information from a hierarchical aviation database by navigating under single-task conditions (Experiment 1) and when this task was time-shared with an altitude-monitoring task of varying bandwidth and priority (Experiment 2). In dual-task conditions, pilots had 2 viewports available, 1 always used for the information task and the other to be allocated to either task. Dual-task strategy, inferred from the decision of which task to allocate to the 2nd viewport, revealed that allocation was generally biased in favor of the monitoring task and was only partly sensitive to the difficulty of the 2 tasks and their relative priorities. Some dominant sources of navigational difficulties failed to adaptively influence selection strategy. The implications of the results are to provide tools for jumping to the top of the database, to provide 2 viewports into the common database, and to provide training as to the optimum viewport management strategy in a multitask environment.
A major problem in employing networks of microcomputers for the real-time simulation of complex systems is to allocate computational tasks to the various microcomputers in such a way that idle time and time lost in interprocess communication is minimized. The research reported in this paper is directed to the development of a software interface between a higher-level simulation language and a network of microcomputers. A CSSL-IV source program is translated to a data flow graph. This graph is then analyzed automatically so as to allocate computing tasks to the various processors.
A distinction was made between two aspects of time sharing performance: time sharing efficiency and attention allocation optimality. A secondary task technique was employed to evaluate the effects of the task structures of the component time shared tasks on both aspects of the time sharing performance. Five pairs of dual tasks differing in their structural configurations were investigated. The primary task was a visual/manual tracking task which requires spatial processing. The secondary task was either another tracking task or a verbal memory task with one of four different input/output configurations. Congruent to a common finding, time-sharing efficiency was observed to decrease with an increasing overlap of resources utilized by the time shared tasks. Research also tends to support the hypothesis that resource allocation is more optimal when the time shared tasks placed heavy demands on common processing resources than when they utilized separate resources.
With the recently increasing adoption of transactive energy markets, the role of a load aggregator is becoming increasingly important. Among the many tasks of an aggregator, the task of managing the electricity consumption among a group of grid-interactive efficient buildings according to the demand on the grid is taken up. Specifically, here, the task of allocating a certain amount of power among the participating customers while respecting their preferences and comfort is considered. Modeling this as an aggregate flexibility, the virtual battery conditions that are employed to verify the feasibility of a candidate power profile are derived. A feasible power profile, when allocated, satisfies specified comfort bands. To determine the ideal power requirement of the loads, the model-free control method is employed. The feasible power profile is allocated by embedding the customers’ preferences through weights, posing the power constraint as a hyperplane, and employing a weighted projection from the ideal power requirements onto the constraint plane to solve the considered task. The proposed method is computationally efficient and scalable to any number of heterogeneous thermostatically controlled loads in buildings.
A model for the human performance of approach and landing tasks that would provide a means for systematic exploration of questions concerning the impact of procedural and equipment design and the allocation of resources in the cockpit on performance and safety in approach-to-landing is discussed. A system model is needed that accounts for the interactions of crew, procedures, vehicle, approach geometry, and environment. The issues of interest revolve principally around allocation of tasks in the cockpit and crew performance with respect to the cognitive aspects of the tasks. The model must, therefore, deal effectively with information processing and decision-making aspects of human performance.
The current task of Resource Allocation Process includes the planning and apportionment of JPL's Ground Data System composed of the Deep Space Network and Mission Control and Computing Center facilities. The addition of the data driven, rule based planning system, RALPH, has expanded the planning horizon from 8 weeks to 10 years and has resulted in large labor savings. Use of the system has also resulted in important improvements in science return through enhanced resource utilization. In addition, RALPH has been instrumental in supporting rapid turn around for an increased volume of special what if studies. The status of RALPH is briefly reviewed and important lessons learned from the creation of an highly functional design team are focused on through an evolutionary design and implementation period in which an AI shell was selected, prototyped, and ultimately abandoned, and through the fundamental changes to the very process that spawned the tool kit. Principal topics include proper integration of software tools within the planning environment, transition from prototype to delivered to delivered software, changes in the planning methodology as a result of evolving software capabilities and creation of the ability to develop and process generic requirements to allow planning flexibility.
Quantative metrics were determined for system control performance, workload for control, monitoring performance, and workload for monitoring. Pilot tasks were allocated for navigation and guidance of automated commercial V/STOL aircraft in all weather conditions using an optimal control model of the human operator to determine display elements and design.
The NASA-Goddard Space Flight Center is responsible for the control and ground support for all of NASA's unmanned near-earth satellites. Traditionally, each satellite had its own dedicated mission operations room. In the mid-seventies, an integration of some of these dedicated facilities was begun with the primary objective to reduce costs. In this connection, the Multi-Satellite Operations Control Center (MSOCC) was designed. MSOCC represents currently a labor intensive operation. Recently, Goddard has become increasingly aware of human factors and human-machine interface issues. A summary is provided of some of the attempts to apply human factors considerations in the design of command and control environments. Current and future activities with respect to human factors and systems design are discussed, giving attention to the allocation of tasks between human and computer, and the interface for the human-computer dialogue.
Two hypotheses concerning the way in which short-term memory interacts with another task in a dual task situation are considered. It is noted that when two tasks are combined, the activity of controlling and organizing performance on both tasks simultaneously may compete with either task for a resource; this resource may be space in a central mechanism or general processing capacity or it may be some task-specific resource. If a special relationship exists between short-term memory and control, especially if there is an identity relationship between short-term and a central controlling mechanism, then short-term memory performance should show a decrement in a dual task situation. Even if short-term memory does not have any particular identity with a controlling mechanism, but both tasks draw on some common resource or resources, then a tradeoff between the two tasks in allocating resources is possible and could be reflected in performance. The persistent concurrence cost in memory performance in these experiments suggests that short-term memory may have a unique status in the information processing system.
The degree of autonomy of future long duration manned missions will emphasize interactions between human operators and automated systems aimed at the most effective allocations of tasks between humans and machines. Knowledge of crewmembers' physical status, encompassing both capabilities and limitations, will also be critical during EVA and planetary roving missions; psychological evaluation and support, with a view to both individual health and group cohesion and productivity, may become a critical consideration. Attention is here given to crewmembers' medical and psychological vulnerabilities.
Manned space flight can be viewed as an interaction of three general elements: the human crewmember, spacecraft systems, and the environment. While the human crewmember is a crucial element in the system, certain physiological, psychological, environ- mental and spacecraft systems factors can compromise human performance in space. These factors include atmospheric pressure, physiology, uncertainties associated with space radiation, the potential for exposure to toxic materials in the closed environment, and spacecraft habitability. Health protection in space, for current and future missions, relies on a philosophy of risk reduction, which in the space program is achieved in four ways-through health maintenance, health care, design criteria, an selection and training. Emphasis is place upon prevention, through selection criteria and careful screening. Spacecraft health care systems must be absolutely reliable, and they will be automated and computerized to the maximum extent possible, but still designed with the human crewmember's capabilities in mind. The autonomy and technological sophistication of future missions will require a greater emphasis on high-level interaction between the human operator and automated systems, with effective allocation of tasks between humans and machines. Performance in space will include complex tasks during extravehicular activity (EVA) and on planetary surfaces, and knowledge of crewmembers' capability and limitations during such operations will be critical to mission success. Psychological support will become increasingly important on space missions, as crews spend long periods in remote and potentially hazardous environments. The success of future missions will depend on both individual psychological health and group cohesion and productivity, particularly as crew profiles become more heterogeneous. Thus, further human factors are needed in the area of small-group dynamics and performance.
The lack of reach performance data obtained under space-flight conditions has led to questions regarding the operational impact of higher G loads on crew performance. This investigation studied the effect of increasing G loads on reach capability. Ten subjects were exposed in a stepwise fashion to increasing accelerations resulting in G loads of from +1 to +6 Gx in the Brooks AFB centrifuge. Four subjects wore the pre-Challenger Launch Entry Helmet (LEH) ensemble and six the current Launch Entry Suit (LES). The subjects performed standardized reach sweeps at each G level. These sweeps were recorded on videotape and subsequently analyzed using a 3-dimensional motion analysis system. Significant differences in forward and overhead reach were determined using the General Linear Models (GLM) procedure of the Statistical Analysis System (SAS) program. The results from this study suggest that purposeful movement can be realistically performed in the LEH at the 5 G level and in the LES up to the 4 G level.