Human Factors Program for the Cooperative Pilot Warning Indicator System Final Report
Display devices for aircraft pilot warning systems
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Display devices for aircraft pilot warning systems
This final report covers work performed under Contract NCC2-220 between NASA Ames Research Center and the Knowledge Systems Laboratory, Stanford University. The period of research was from March 1, 1987 to February 29, 1988. Topics covered were as follows: (1) concurrent architectures for knowledge-based systems; (2) methods for the solution of geometric constraint satisfaction problems, and (3) reasoning under uncertainty. The research in concurrent architectures was co-funded by DARPA, as part of that agency's Strategic Computing Program. The research has been in progress since 1985, under DARPA and NASA sponsorship. The research in geometric constraint satisfaction has been done in the context of a particular application, that of determining the 3-D structure of complex protein molecules, using the constraints inferred from NMR measurements.
In a previous report, an empirical study of 30 pilots using the Flight Planning Testbed was reported. An identical experiment using the Flight Planning Testbed (FPT), except that 27 airline dispatchers were studied, is described. Five general questions were addressed in this study: (1) under what circumstances do the introduction of computer-generated suggestions (flight plans) influence the planning behavior of dispatchers (either in a beneficial or adverse manner); (2) what is the nature of such influences (i.e., how are the person's cognitive processes changed); (3) how beneficial are the general design concepts underlying FPT (use of a graphical interface, embedding graphics in a spreadsheet, etc.); (4) how effective are the specific implementation decisions made in realizing these general design concepts; and (5) how effectively do dispatchers evaluate situations requiring replanning, and how effectively do they identify appropriate solutions to these situations.
A prototype optoelectronic system measures the three-dimensional relative coordinates of objects of interest or of targets affixed to objects of interest in a workspace. The system includes a charge-coupled-device video camera mounted in a known position and orientation in the workspace, a frame grabber, and a personal computer running image-data-processing software. Relative to conventional optical surveying equipment, this system can be built and operated at much lower cost; however, it is less accurate. It is also much easier to operate than are conventional instrumentation systems. In addition, there is no need to establish a coordinate system through cooperative action by a team of surveyors. The system operates in real time at around 30 frames per second (limited mostly by the frame rate of the camera). It continuously tracks targets as long as they remain in the field of the camera. In this respect, it emulates more expensive, elaborate laser tracking equipment that costs of the order of 100 times as much. Unlike laser tracking equipment, this system does not pose a hazard of laser exposure. Images acquired by the camera are digitized and processed to extract all valid targets in the field of view. The three-dimensional coordinates (x, y, and z) of each target are computed from the pixel coordinates of the targets in the images to accuracy of the order of millimeters over distances of the orders of meters. The system was originally intended specifically for real-time position measurement of payload transfers from payload canisters into the payload bay of the Space Shuttle Orbiters (see Figure 1). The system may be easily adapted to other applications that involve similar coordinate-measuring requirements. Examples of such applications include manufacturing, construction, preliminary approximate land surveying, and aerial surveying. For some applications with rectangular symmetry, it is feasible and desirable to attach a target composed of black and white squares to an object of interest (see Figure 2). For other situations, where circular symmetry is more desirable, circular targets also can be created. Such a target can readily be generated and modified by use of commercially available software and printed by use of a standard office printer. All three relative coordinates (x, y, and z) of each target can be determined by processing the video image of the target. Because of the unique design of corresponding image-processing filters and targets, the vision-based position- measurement system is extremely robust and tolerant of widely varying fields of view, lighting conditions, and varying background imagery.
In order to solve a complicated problem one must use the knowledge from different domains. Therefore, if one wants to automatize the solution of these problems, one has to help the knowledge-based systems that correspond to these domains cooperate, that is, communicate facts and conclusions to each other in the process of decision making. One of the main obstacles to such cooperation is the fact that different intelligent systems use different methods of knowledge acquisition and different methods and formalisms for uncertainty representation. So an interface f is needed, 'translating' the values x, y, which represent uncertainty of the experts' knowledge in one system, into the values f(x), f(y) appropriate for another one. The problem of designing such an interface as a mathematical problem is formulated and solved. It is shown that the interface must be fractionally linear: f(x) = (ax + b)/(cx + d).
The future of space exploration lies in cooperative autonomous systems. Ensuring their high integrity remains a challenge. The Robust Software Engineering group at NASA Ames Research Center has been developing the Troupe project to explore the challenges with developing and assuring high integrity of cooperative autonomous robotic systems. In particular, Troupe aims to develop a swarm of autonomous rovers capable of mapping unknown terrain and assure their high integrity using the advanced V&V tools developed in the group. In this paper, we present the evolution of the design of Troupe. We focus on the lessons learned in developing and assuring the rover swarm using core Flight System (cFS). In particular, we discuss the benefits and challenges in applying model-based development to develop the rover swarm.
The future of space exploration lies in cooperative autonomous systems. Ensuring their high integrity remains a challenge. The Robust Software Engineering group at NASA Ames Research Center has been developing the Troupe project to explore the challenges with developing and assuring high integrity of cooperative autonomous robotic systems. In particular, Troupe aims to develop a swarm of autonomous rovers capable of mapping unknown terrain and assure their high integrity using the advanced V&V tools developed in the group. In this paper, we present the evolution of the design of Troupe. We focus on the lessons learned in developing and assuring the rover swarm using core Flight System (cFS). In particular, we discuss the benefits and challenges in applying model-based development to develop the rover swarm.
Connected and automated vehicles (CAVs) can plan and actuate control that explicitly considers performance, system safety, and actuation constraints in a manner more efficient than their human-driven counterparts. In particular, eco-driving is enabled through connected exchange of information from signalized corridors that share their upcoming signal phase and timing (SPaT). This is accomplished in the proposed control approach, which follows first principles to plan a free-flow acceleration-optimal trajectory through green traffic light intervals by Pontryagin's Minimum Principle in a feedback manner. Urban conditions are then imposed from exogeneous traffic comprised of a mixture of human-driven vehicles (HVs) - as well as other CAVs. As such, safe disturbance compensation is achieved by implementing a model predictive controller (MPC) to anticipate and avoid collisions by issuing braking commands as necessary. The control strategy is experimentally vetted through vehicle-in-the-loop (VIL) of a prototype CAV that is embedded into a virtual traffic corridor realized through microsimulation. Up to 36% fuel savings are measured with the proposed control approach over a human-modelled driver, and it was found connectivity in the automation approach improved fuel economy by up to 26% over automation without. Additionally, the passive energy benefits realizable for human drivers when driving behind downstream CAVs are measured, showing up to 22% fuel savings in a HV when driving behind a small penetration of connectivity-enabled automated vehicles.
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Various applications of artificial intelligence to space electrical power systems are discussed. An overview is given of completed, on-going, and planned knowledge-based system activities. These applications include the Nickel-Cadmium Battery Expert System (NICBES) (the expert system interfaced with the Hubble Space Telescope electrical power system test bed); the early work with the Space Station Experiment Scheduler (SSES); the three expert systems under development in the space station advanced development effort in the core module power management and distribution system test bed; planned cooperation of expert systems in the Core Module Power Management and Distribution (CM/PMAD) system breadboard with expert systems for the space station at other research centers; and the intelligent data reduction expert system under development.
The kinematic control of a redundant arm based on multiple tasks assigned to different locations of the manipulator is presented. This is equivalent to decomposing a redundant arm into two (or more) nonredundant local arms, the basearm and the forearm, at an intermediate arm location or task point called the elbow. A redundant arm is transformed into a serially cooperating dual-arm system, with the cooperation between the local arms being carried out at the elbow. Then, a manipulator end-effector motion specified by a given task is decomposed into motions of individual local arms as well as an end-effector free motion based on achieving maximum efficiency in task execution. To consider global optimization, elbow control is applied to reshaping and reorientation of a manipulability ellipsoid at the end effector during task execution. The resolved rate control of elbow in the end-effector null and free motion space is used to maximize arm homogeneity and to match the forearm motion space with the task space. Simulation results are shown.
The Robotic Air Vehicle (RAV) system is described. The program's objectives were to design, implement, and demonstrate cooperating expert systems for piloting robotic air vehicles. The development of this system merges conventional programming used in passive navigation with Artificial Intelligence techniques such as voice recognition, spatial reasoning, and expert systems. The individual components of the RAV system are discussed as well as their interactions with each other and how they operate as a system.
Robotic Construction Crew (RCC) is a heterogeneous multi-robot system for autonomous acquisition, transport, and precision mating of components in construction tasks. RCC minimizes resources constrained in a space environment such as computation, power, communication and, sensing. A behavior-based architecture provides adaptability and robustness despite low computational requirements. RCC successfully performs several construction related tasks in an emulated outdoor environment despite high levels of uncertainty in motions and sensing. Quantitative results are provided for formation keeping in component transport, precision instrument placement, and construction tasks.
An error reporting system utilizes a parity checker to receive data results from execution of an original instruction and a parity bit for the data. A decoder receives an error correcting code (ECC) for data resulting from execution of a shadow instruction of the original instruction, and data error correction is initiated on the original instruction result on condition of a mismatch between the parity bit and the original instruction result, and the decoder asserting a correctable error in the original instruction result.
One of the earliest applications for radar was to search for and find maritime vessels on the open sea. Proper design and operation of an airborne Maritime Wide Area Search (MWAS) radar requires an understanding of system performance characteristics and limitations, and furthermore understanding the trades amongst a large number of interdependent system parameters. This report identifies and explores those characteristics and limits, and how they depend on hardware system parameters and environmental conditions. Ultimately, this leads to a characterization of parameters that offer optimum performance for the overall MWAS radar system. While the information herein is not new to the literature, its collection into a single report hopes to offer some value in reducing the 'seek time'. Acknowledgements This report was funded by General Atomics Aeronautical Systems, Inc. (GA-ASI) Mission Systems under Cooperative Research and Development Agreement (CRADA) SC08/01749 between Sandia National Laboratories and GA-ASI. General Atomics Aeronautical Systems, Inc. (GA-ASI), an affiliate of privately-held General Atomics, is a leading manufacturer of Remotely Piloted Aircraft (RPA) systems, radars, and electro-optic and related mission systems, including the Predator/Gray Eagle-series and Lynx Multi-mode Radar. -
This project consists of several tasks which are unified toward experimentally demonstrating the operation of a highly autonomous, user-supportive power management and distribution system for Space Station Freedom (SSF) habitation/laboratory modules. This goal will be extended to a demonstration of autonomous, cooperative power system operation for the whole SSF power system through a joint effort with NASA's Lewis Research Center, using their Autonomous Power System. Short term goals for the space station module power management and distribution include having an operational breadboard reflecting current plans for SSF, improving performance of the system communications, and improving the organization and mutability of the artificial intelligence (AI) systems. In the middle term, intermediate levels of autonomy will be added, user interfaces will be modified, and enhanced modeling capabilities will be integrated in the system. Long term goals involve conversion of all software into Ada, vigorous verification and validation efforts and, finally, seeing an impact of this research on the operation of SSF. Conversion of the system to a DC Star configuration is now in progress, and should be completed by the end of October, 1989. This configuration reflects the latest SSF module architecture. Hardware is now being procured which will improve system communications significantly. The Knowledge-Based Management System (KBMS) is initially developed and the rules from FRAMES have been implemented in the KBMS. Rules in the other two AI systems are also being grouped modularly, making them more tractable, and easier to eventually move into the KBMS. Adding an intermediate level of autonomy will require development of a planning utility, which will also be built using the KBMS. These changes will require having the user interface for the whole system available from one interface. An Enhanced Model will be developed, which will allow exercise of the system through the interface without requiring all of the power hardware to be operational. The functionality of the AI systems will continue to be advanced, including incipient failure detection. Ada conversion will begin with the lowest level processor (LLP) code. Then selected pieces of the higher level functionality will be recorded in Ada and, where possible, moved to the LLP level. Validation and verification will be done on the Ada code, and will complete sometimes after completion of the Ada conversion.
There is an increasing demand for access to the national airspace system (NAS) by new entrants such as unmanned aircraft systems (UASs) and space vehicles. The need is driving the research into the development of a ubiquitous surveillance framework. A framework where all means of aircraft position tracking systems, both cooperative and noncooperative, would be correlated and made available to all NAS users. The architecture of an envisioned surveillance system was the focus of a recent Volpe National Transportation Systems Center research activity. In this work, the term ''Framework'' is used to characterize an operational environment that forms the context for future UAS operations. Within this framework, the use of all existing and future surveillance technologies is envisioned. Included would be all airspace locations; including coverage for airspace not under surveillance today. While these surveillance means and methods of air traffic control may currently constrain new entrant operations, they also provide the opportunity for the plethora of enabling technological capabilities with associated policies and procedures that can result in the safe, orderly and efficient operation. The focus of this paper is to survey and identify surveillance technologies to support the integration of new entrants in the NAS, and how those technologies can be aggregated.
Whenever mixing priorities of manually controlled resources with those of autonomously controlled resources, the space station module power management and distribution (SSM/PMAD) environment requires cooperating expert system interaction between the planning function and the priority manager. The elements and interactions of the SSM/PMAD planning and priority management functions are presented. Their adherence to cooperating for common achievement are described. In the SSM/PMAD testbed these actions are guided by having a system planning function, KANT, which has insight to the executing system and its automated database. First, the user must be given access to all information which may have an effect on the desired outcome. Second, the fault manager element, FRAMES, must be informed as to the change so that correct diagnoses and operations take place if and when faults occur. Third, some element must engage as mediator for selection of resources and actions to be added or removed at the user's request. This is performed by the priority manager, LPLMS. Lastly, the scheduling mechanism, MAESTRO, must provide future schedules adhering to the user modified resource base.