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Failures and deficiencies in flight programs are reviewed and suggestions are made for avoiding them. The technology development problem areas considered are control configured vehicle design, gyros, solid state star sensors, control instrumentation, tolerant/accomodating control systems, large momentum exchange devices, and autonomous rendezvous and docking.
The general objectives of the NASA/UCF Automated Knowledge Generation Project were the development of an intelligent software system that could access CAD design data bases, interpret them, and generate a diagnostic knowledge base in the form of a system model. The initial area of concentration is in the diagnosis of the process control system using the Knowledge-based Autonomous Test Engineer (KATE) diagnostic system. A secondary objective was the study of general problems of automated knowledge generation. A prototype was developed, based on object-oriented language (Flavors).
The Orbital Maneuvering Vehicle (OMV) and concepts of teleoperation and video data compression as applied to OMV design and operation are described. The OMV provides spacecraft delivery, retrieval, reboost, deboost and viewing services, with ground-control or Space Station operation, through autonomous navigation and pilot controlled maneuvers. Communications systems are comprised of S-band RF command, telemetry, and compressed video data links through the TDRSS and GSTDN networks. The control console video monitors display a monochrome image at an update rate of five frames per second. Depending upon the mode of operation selected by the pilot, the video resolution is either 255 x 244 pixels, or 510 x 244 pixels. Since practically all video image redundancy is removed by the compression process, the video reconstruction is particularly sensitive to data transmission bit errors. Concatenated Reed-Solomon and convolution coding are used with helical data interleaving for error detection and correction, and an error-containment process minimizes the propagation of error effects throughout the video image. Video sub-frame replacement is used, in the case of a non-correctable error or error burst, to minimize the visual impact to the pilot.
Prolonged exposure of humans to decreased gravitational forces during spaceflight results in a number of adverse cardiovascular consequences, often referred to as cardiovascular deconditioning. Prominent among these negative cardiovascular effects are orthostatic intolerance and decreased exercise capacity. Rat hindlimb unweighting is an animal model which simulates weightlessness, and results in similar cardiovascular consequences. Cardiovascular reflexes, including arterial and cardiopulmonary baroreflexes, are required for normal adjustment to both orthostatic challenges and exercise. Therefore, the orthostatic intolerance and decreased exercise capacity associated with exposure to microgravity may be due to cardiovascular reflex dysfunction. The proposed studies will test the general hypothesis that hindlimb unweighting in rats results in impaired autonomic reflex control of the sympathetic nervous system. Specifically, we hypothesize that the ability to reflexly increase sympathetic nerve activity in response to decreases in arterial pressure or blood volume will be blunted due to hindlimb unweighting. There are 3 specific aims: (1) To evaluate arterial and cardiopulmonary baroreflex control of renal and lumbar sympathetic nerve activity in conscious rats subjected to 14 days of hindlimb unweighting; (2) To examine the interaction between arterial and cardiopulmonary baroreflex control of sympathetic nerve activity in conscious hindlimb unweighted rats; (3) to evaluate changes in afferent and/or central nervous system mechanisms in baroreflex regulation of the sympathetic nervous system. These experiments will provide information related to potential mechanisms for orthostatic and exercise intolerance due to microgravity.
For the first time, a suite of three lidar sensors have been used in flight to scan a lunar-like hazard field, identify a safe landing site, and, in concert with an experimental Guidance, Navigation, and Control (GN&C) system, guide the Morpheus autonomous, rocket-propelled, free-flying test bed to a safe landing on the hazard field. The lidar sensors and GN&C system are part of the Autonomous Precision Landing and Hazard Detection and Avoidance Technology (ALHAT) project which has been seeking to develop a system capable of enabling safe, precise crewed or robotic landings in challenging terrain on planetary bodies under any ambient lighting conditions. The 3-D imaging flash lidar is a second generation, compact, real-time, air-cooled instrument developed from a number of cutting-edge components from industry and NASA and is used as part of the ALHAT Hazard Detection System (HDS) to scan the hazard field and build a 3-D Digital Elevation Map (DEM) in near-real time for identifying safe sites. The flash lidar is capable of identifying a 30 cm hazard from a slant range of 1 km with its 8 cm range precision at 1 sigma. The flash lidar is also used in Hazard Relative Navigation (HRN) to provide position updates down to a 250m slant range to the ALHAT navigation filter as it guides Morpheus to the safe site. The Doppler Lidar system has been developed within NASA to provide velocity measurements with an accuracy of 0.2 cm/sec and range measurements with an accuracy of 17 cm both from a maximum range of 2,200 m to a minimum range of several meters above the ground. The Doppler Lidar's measurements are fed into the ALHAT navigation filter to provide lander guidance to the safe site. The Laser Altimeter, also developed within NASA, provides range measurements with an accuracy of 5 cm from a maximum operational range of 30 km down to 1 m and, being a separate sensor from the flash lidar, can provide range along a separate vector. The Laser Altimeter measurements are also fed into the ALHAT navigation filter to provide lander guidance to the safe site. The flight tests served as the culmination of the TRL 6 journey for the lidar suite and included launch from a pad situated at the NASA-Kennedy Space Center Shuttle Landing Facility (SLF) runway, a lunar-like descent trajectory from an altitude of 250m, and landing on a lunar-like hazard field of rocks, craters, hazardous slopes, and safe sites 400m down-range just off the North end of the runway. The tests both confirmed the expected performance and also revealed several challenges present in the flight-like environment which will feed into future TRL advancement of the sensors. The flash lidar identified hazards as small as 30 cm from the maximum slant range of 450 m which Morpheus could provide, however, it was occasionally susceptible to an increase in range noise due to heated air from the Morpheus rocket plume which entered its Field-of-View (FOV). The flash lidar was also susceptible to pre-triggering on dust during the HRN phase which was created during launch and transported by the wind. The Doppler Lidar provided velocity and range measurements to the expected accuracy levels yet it was also susceptible to signal degradation due to air heated by the rocket engine. The Laser Altimeter, operating with a degraded transmitter laser, also showed signal attenuation over a few seconds at a specific phase of the flight due to the heat plume generated by the rocket engine.
During the early years of human spaceflight, short duration missions allowed for monitoring of the spacecraft environment to be performed via archival sampling, in which samples were returned to Earth for analysis. With the construction of the International Space Station (ISS) and the accompanying extended mission durations, the need for enhanced, real-time monitors became apparent. The Volatile Organic Analyzer (VOA) operated on ISS for 7 years, where it assessed trace volatile organic compounds in the cabin air. The large and fixed-position VOA was eventually replaced with the smaller Air Quality Monitor (AQM). Since March 2013, the atmosphere of the U.S. Operating Segment (USOS) has been monitored in near real-time by a pair of AQMs. These devices consist of a gas chromatograph (GC) coupled with a differential mobility spectrometer (DMS) and currently target detection list of 22 compounds. These targets are of importance to both crew health and the Environmental Control and Life Support Systems (ECLSS) on ISS. Data is collected autonomously every 73 hours, though the units can be controlled remotely from mission control to collect data more frequently during contingency or troubleshooting operations. Due to a nominal three-year lifetime on-orbit, the initial units were replaced in February 2016. This paper will focus on the preparation and use of the AQMs over the past several years. A description of the technical aspects of the AQM will be followed by lessons learned from the deployment and operation of the first set of AQMs. These lessons were used to improve the already-excellent performance of the instruments prior to deployment of the replacement units. Data trending over the past several years of operation on ISS will also be discussed, including data obtained during a survey of the USOS modules. Finally, a description of AQM use for contingency and investigative studies will be presented.
Cost, performance and functionality concerns are driving a trend towards use of self-sufficient autonomous systems in place of human-controlled mechanisms.
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The Booster Operation Optimization Sequential Time-series for Regression (BOOSTR) dataset was created to provide a cycle-by-cycle time series of readings and settings from instruments and controllable devices of the Booster, Fermilab's Rapid-Cycling Synchrotron (RCS) operating at 15~Hz. BOOSTR provides a time series from 55 device readings and settings that pertain most directly to the high-precision regulation of the Booster's gradient magnet power supply (GMPS). To our knowledge, this is one of the first well-documented datasets of accelerator device parameters made publicly available. We are releasing it in the hopes that it can be used to demonstrate aspects of artificial intelligence for advanced control systems, such as reinforcement learning and autonomous anomaly detection.
The University of California at Berkeley's (UCB) Center for Extreme Ultraviolet Astrophysics (CEA), in conjunction with NASA's Ames Research Center (ARC), has implemented an autonomous monitoring system in the Extreme Ultraviolet Explorer (EUVE) science operations center (ESOC). The implementation was driven by a need to reduce operations costs and has allowed the ESOC to move from continuous, three-shift, human-tended monitoring of the science payload to a one-shift operation in which the off shifts are monitored by an autonomous anomaly detection system. This system includes Eworks, an artificial intelligence (AI) payload telemetry monitoring package based on RTworks, and Epage, an automatic paging system to notify ESOC personnel of detected anomalies. In this age of shrinking NASA budgets, the lessons learned on the EUVE project are useful to other NASA missions looking for ways to reduce their operations budgets. The process of knowledge capture, from the payload controllers for implementation in an expert system, is directly applicable to any mission considering a transition to autonomous monitoring in their control center. The collaboration with ARC demonstrates how a project with limited programming resources can expand the breadth of its goals without incurring the high cost of hiring additional, dedicated programmers. This dispersal of expertise across NASA centers allows future missions to easily access experts for collaborative efforts of their own. Even the criterion used to choose an expert system has widespread impacts on the implementation, including the completion time and the final cost. In this paper we discuss, from inception to completion, the areas where our experiences in moving from three shifts to one shift may offer insights for other NASA missions.
Detection of bad data from measurement sensors and bad commands from control centers need to be carried out to avoid instabilities within large power electronics systems. Towards the same, in this paper, model and data driven methods are proposed to identify anomalies in measured data and commands received by large power electronics systems. The large power electronics system considered in this paper is a multi-port autonomous reconfigurable solar power plant (MARS), which consists of photovoltaic (PV) and energy storage systems (ESSs) that connect to high-voltage direct current (HVdc) system and transmission ac power grid. The proposed algorithms in the MARS power plant to detect bad data from measurements and bad commands from control centers are evaluated in simulations and hardware-in-the-loop (HIL) tests. Furthermore, it has been observed that the proposed algorithms are able to detect bad measurements and commands in all the use cases evaluated.
The proposed supervisory control system (SCS) may provide considerable benefits to advanced small modular reactors, including reduced plant staffing, optimized maintenance activities, greater plant availability, and higher operating efficiency. The SCS makes risk-informed decisions based on (1) a probabilistic assessment of the likelihood of success given the status of the plant/systems and component health, and (2) a deterministic assessment between plant operating parameters and reactor protection parameters to prevent unnecessary trips and challenges to plant safety system—one measure of SCS success. The probabilistic portion of the decision-making engine of the SCS is based on the control actions associated with an advanced liquid-metal reactor (ALMR) Power Reactor, Innovative, Small Module (PRISM). Within the SCS, the probabilistic assessment provides a ranking of viable control actions; however, certain instructions generated by the probabilistic model only include an abstract notion of action without specifications. For instance, one instruction may be to reduce power without specifying how much reduction is needed. The prognostic/diagnostic models incorporate the health of components into the decision-making process. Once the control options are identified and ranked based on the likelihood of success, the SCS transmits the options to the deterministic portion of the platform. The performance-based system models assess and rank each of the probabilistically identified control actions by taking into account the physical behavior (current and projected) of the system. The performance-based decision making module receives inputs from the probabilistic decision-making module and the ERM module to generate a single solution. Interfaces to these modules are defined later in the section. A utility theory algorithm factors into the decision making by estimating the distance from and approach to a trip setpoint for each control option. If the magnitude of a negative utility value increases rapidly as the system approaches the trip setpoint, that option is not likely to be the preferred option. This can lead to a re-ranking of the control options. The SCS then transmits a control signal(s) to a component or system and informs the operator of actions taken based on the action chosen. The SCS successfully coupled probabilistic and performance-based system models to arrive at optimal control decisions based on the actual status of the plant and components. The automatic, autonomous, and real-time performance requirements for a control system were met by the SCS. The value of coupling probabilistic and performance-based system models was demonstrated by the re-ranking of control options based on the use of a utility algorithm. The use of ERM monitors provides added value to the SCS as demonstrated by the re-ranking of control options based on a components degraded state.
A high level robot command language is proposed for the autonomous mode of an advanced telerobotics system and a predictive display mechanism for the teleoperational model. It is believed that any such system will involve some mixture of these two modes, since, although artificial intelligence can facilitate significant autonomy, a system that can resort to teleoperation will always have the advantage. The high level command language will allow humans to give the robot instructions in a very natural manner. The robot will then analyze these instructions to infer meaning so that is can translate the task into lower level executable primitives. If, however, the robot is unable to perform the task autonomously, it will switch to the teleoperational mode. The time delay between control movement and actual robot movement has always been a problem in teleoperations. The remote operator may not actually see (via a monitor) the results of high actions for several seconds. A computer generated predictive display system is proposed whereby the operator can see a real-time model of the robot's environment and the delayed video picture on the monitor at the same time.
A test bed was fabricated to demonstrate hydrogen/oxygen propulsion technology readiness for the intital operating configuration (IOC) space station application. The test bed propulsion module and computer control system were delivered in December 1985, but activation was delayed until mid-1986 while the propulsion system baseline for the station was reexamined. A new baseline was selected with hydrogen/oxygen thruster modules supplied with gas produced by electrolysis of waste water from the space shuttle and space station. As a result, an electrolysis module was designed, fabricated, and added to the test bed to provide an end-to-end simulation of the baseline system. Subsequent testing of the test bed propulsion and electrolysis modules provided an end-to-end demonstration of the complete space station propulsion system, including thruster hot firings using the oxygen and hydrogen generated from electrolysis of water. Complete autonomous control and operation of all test bed components by the microprocessor control system designed and delivered during the program was demonstrated. The technical readiness of the system is now firmly established.
This paper presents an autonomous path-following control architecture for a tilt-wing, distributed electric propulsion, vertical take-off and landing unmanned aerial system in hover mode and presents indoor flight test results. The test-bed vehicle is a subscale model with the same configuration as the NASA GL-10 aircraft. The control architecture consists of an inner-loop attitude controller, outer-loop trajectory controller, and a trajectory generation scheme. The flight test results show that the vehicle can satisfactorily follow a path prescribed by a list of waypoints around the indoor flight room.
A closed-loop system for the demonstration of autonomous satellite formation flying technologies using hardware-in-the-loop has been developed. Making use of a GPS signal simulator with a dual radio frequency outlet, the system includes two GPS space receivers as well as a powerful onboard navigation processor dedicated to the GPS-based guidance, navigation, and control of a satellite formation in real-time. The closed-loop system allows realistic simulations of autonomous formation flying scenarios, enabling research in the fields of tracking and orbit control strategies for a wide range of applications. The autonomous closed-loop formation acquisition and keeping strategy is based on Lyapunov's direct control method as applied to the standard set of Keplerian elements. This approach not only assures global and asymptotic stability of the control but also maintains valuable physical insight into the applied control vectors. Furthermore, the approach can account for system uncertainties and effectively avoids a computationally expensive solution of the two point boundary problem, which renders the concept particularly attractive for implementation in onboard processors. A guidance law has been developed which strictly separates the relative from the absolute motion, thus avoiding the numerical integration of a target trajectory in the onboard processor. Moreover, upon using precise kinematic relative GPS solutions, a dynamical modeling or filtering is avoided which provides for an efficient implementation of the process on an onboard processor. A sample formation flying scenario has been created aiming at the autonomous transition of a Low Earth Orbit satellite formation from an initial along-track separation of 800 m to a target distance of 100 m. Assuming a low-thrust actuator which may be accommodated on a small satellite, a typical control accuracy of less than 5 m has been achieved which proves the applicability of autonomous formation flying techniques to formations of satellites as close as 50 m.
The NASA Johnson Space Center has developed a nanosatellite-class Free Flyer intended for future external inspection and remote viewing of human spacecraft. The Miniature Autonomous Extravehicular Robotic Camera (Mini AERCam) technology demonstration unit has been integrated into the approximate form and function of a flight system. The spherical Mini AERCam Free Flyer is 7.5 inches in diameter and weighs approximately 10 pounds, yet it incorporates significant additional capabilities compared to the 35-pound, 14-inch diameter AERCam Sprint that flew as a Shuttle flight experiment in 1997. Mini AERCam hosts a full suite of miniaturized avionics, instrumentation, communications, navigation, power, propulsion, and imaging subsystems, including digital video cameras and a high resolution still image camera. The vehicle is designed for either remotely piloted operations or supervised autonomous operations, including automatic stationkeeping, point-to-point maneuvering, and waypoint tracking. The Mini AERCam Free Flyer is accompanied by a sophisticated control station for command and control, as well as a docking system for automated deployment, docking, and recharge at a parent spacecraft. Free Flyer functional testing has been conducted successfully on both an airbearing table and in a six-degree-of-freedom closed-loop orbital simulation with avionics hardware in the loop. Mini AERCam aims to provide beneficial on-orbit views that cannot be obtained from fixed cameras, cameras on robotic manipulators, or cameras carried by crewmembers during extravehicular activities (EVA s). On Shuttle or International Space Station (ISS), for example, Mini AERCam could support external robotic operations by supplying orthogonal views to the intravehicular activity (IVA) robotic operator, supply views of EVA operations to IVA and/or ground crews monitoring the EVA, and carry out independent visual inspections of areas of interest around the spacecraft. To enable these future benefits with minimal impact on IVA operators and ground controllers, the Mini AERCam system architecture incorporates intelligent systems attributes that support various autonomous capabilities. 1) A robust command sequencer enables task-level command scripting. Command scripting is employed for operations such as automatic inspection scans over a region of interest, and operator-hands-off automated docking. 2) A system manager built on the same expert-system software as the command sequencer provides detection and smart-response capability for potential system-level anomalies, like loss of communications between the Free Flyer and control station. 3) An AERCam dynamics manager provides nominal and off-nominal management of guidance, navigation, and control (GN&C) functions. It is employed for safe trajectory monitoring, contingency maneuvering, and related roles. This paper will describe these architectural components of Mini AERCam autonomy, as well as the interaction of these elements with a human operator during supervised autonomous control.