Autonomous flight control for a Titan exploration aerobot
In this paper, we discuss steps towards the development of an autonomy architecture, and concentrate on the autonomous flight control subsystem.
robotic airship↗
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Concepts for autonomous flight control for a balloon on Mars
Balloons operating as airborne rovers have been suggested as ideal candidates for early exploration of the Martian surface. An international study team composed of scientists from the U.S.S.R., France, and the U.S.A. is planning the launching in 1994 of a balloon system to fly on Mars. The current likely design is a dual thermal/gas balloon that consists of a gas balloon suspended above a solar-heated thermal balloon. At night, the thermal balloon provides no lift, and the balloon system drifts just above the Martian surface; the lift of the gas balloon is just sufficient to prevent the science payload from hitting the ground. During the day, the balloon system flies at an altitude of 4 to 5 kilometers, rising due to the added lift provided by the thermal balloon. Over the course of a single Martian day, there may be winds in several directions, and in fact it can be expected that there will be winds simultaneously in different directions at different altitudes. Therefore, a balloon system capable of controlling its own altitude, via an autonomous flight control system, can take advantage of these different winds to control its direction, thereby greatly increasing both its mission utility and its longevity.
Heinsheimer, Thomas F.↗
Autonomous flight control for a Titan exploration aerobot
Robotic lighter-than-air vehicles, or aerobots, provide strategic platform for the exploration of planets and moons with an atmosphere, such as Venus, Mars, Titan and the gas giants. In this paper, we discuss steps towards the development of an autonomy architecture, and concentrate on the autonomous fight control subsystem.
robotic airship↗
Autonomous flight control for a planetary exploration aerobot
Robotic lighter-than-air vehicles, or aerobots, provide a strategic platform for the exploration of planets and moons with an atmosphere, such as Venus, Mars, Titan and the gas giants.
robotic airships↗
Conjunction Assessment and Deconfliction Paradigm for Co-located Satellite Constellations with On-Spacecraft “Autonomous” Flight Dynamics Control
Constellations that employ highly-automated satellite flight dynamics can operate safely from a collision avoidance (CA) perspective using current CA screening technology and communication paradigms—except when in proximity to other highly-automated constellations: there is no existing arrangement and concept of operations to allow the low-latency CA screenings, exchange of information, and assignment/acceptance of mitigation responsibility that co-location of highly-automated constellations requires. However, the serendipitous co-location of the NASA Starling mission—an autonomously-controlled technology demonstration constellation—and the SpaceX Starlink constellation has presented an opportunity (indeed, a requirement) to develop and build out a solution for safe operations of two (or more) co-located, highly-automated satellite constellations. Such a solution has been designed and constructed, including the required ground node; and it will be exercised as a dedicated, in-flight safety experiment between the two constellations from January to September 2024. Lessons learned from this experiment are expected to feed the design of the US Department of Commerce’s Tracking Control System for Space (TraCSS) space traffic coordination solution.
conjunction assessment↗
Candidate Performance Metrics for Generalized Control for Autonomous Flight
Contingency management is the most challenging aspect of autonomous flight. In order to accommodate the most flexible response to unpredicted events and unexpected circumstances, i.e. contingencies, a new integrated path planning, trajectory following, flight control architecture is required that would maximize the safe operating envelope. For this highly integrated generalized control architecture, a new set of performance metrics that extends beyond traditional stability and performance is required. This paper proposes a candidate set of new performance metrics relevant to urban air mobility mission scenarios.
Control metrics↗
Candidate Performance Metrics for Generalized Control for Autonomous Flight
Contingency management is the most challenging aspect of autonomous flight. In order to accommodate the most flexible response to unpredicted events and unexpected circumstances, i.e. contingencies, a new integrated path planning, trajectory following, flight control architecture is required that would maximize the safe operating envelope. For this highly integrated generalized control architecture, a new set of performance metrics that extends beyond traditional stability and performance is required. This paper proposes a candidate set of new performance metrics relevant to urban air mobility mission scenarios.
Control metrics↗
Conjunction Assessment and Deconfliction Paradigm for Co-Located Satellite Constellations With on-Spacecraft “Autonomous” Flight Dynamics Control
Satellite constellations that employ on-spacecraft flight dynamics and conjunction assessment (CA) approaches face particular challenges. First, they must arrange to obtain and upload to the constellation satellite close approach information, such as Conjunction Data Messages, so that their on-board systems will have the data they need to make informed CA decisions. Second, given current latencies in obtaining CA screening information, such systems often cannot obtain dedicated screenings of their proposed maneuvers before they are executed. Third, they usually cannot engage in real time with owner/operators (O/Os) with whom they may come into conjunction, relying instead on pre-arranged courses of action that may or may not fit a given situation well. Despite these difficulties, it is nonetheless possible, as operators such as SpaceX have demonstrated, to operate safely using on-board CA calculations, in the presence of a largely collaborative operating environment. The situation breaks down, however, when two constellations that operate with such a paradigm are placed in the same orbital region and will experience conjunctions with each other. Each may have a vague idea of where the other’s satellites are, or perhaps even a somewhat precise idea if they exchange O/O ephemerides, but neither will know the other’s more immediate maneuver intentions, especially for situations in which they are in a high-risk conjunction with each other and a mitigation maneuver is required. As there is no formalized way for these automated systems to ascertain each other’s intentions for this type of conjunction, it is quite possible that each spacecraft may choose a maneuver that results in the two objects’ colliding. An opportunity to develop a solution to this problem was presented by the NASA “Starling” project, a small experimental constellation developed to demonstrate autonomous satellite control and constellation reconfiguration and for which a launch was obtained that, serendipitously, will place this constellation close to the SpaceX Starlink constellation. Because the NASA constellation is experimental, an extended mission part of the mission has been negotiated to implement and test mechanisms for resolving the problem of co-located constellations with on-board flight dynamics control. For the last eighteen months, a consortium among NASA Ames (the Starling satellite providers), NASA CARA (the Agency organization responsible for conjunction assessment), Space X, and the Department of Commerce (in observer status) has met regularly to assemble a concept of operations for a robust solution to this problem and has developed a prototype ground node that will serve as a needed CA screening and information dispensary. The approach and methods, as well as the active employment of the developed ground node, will be tested both in simulation before actual use and then in actual spacecraft operations, which are anticipated to begin in January 2024. This paper will describe the co-located autonomously-controlled constellation problem and the developed solution in detail, outlining what ground node (i.e., government) infrastructure and features are required, what particular capabilities are needed by the constellations themselves in order to make use of this deconfliction paradigm, and the migration path forward to allow such a capability set to be incorporated by the Department of Commerce’s space traffic coordination system.
Conjunction Assessment↗
Conjunction Assessment and Deconfliction Paradigm for Co-located Satellite Constellations with On-Spacecraft “Autonomous” Flight Dynamics Control
Explore the source record for details and available documents.
conjunction assessment↗
Case Study: Test Results of a Tool and Method for In-Flight, Adaptive Control System Verification on a NASA F-15 Flight Research Aircraft
Adaptive control technologies that incorporate learning algorithms have been proposed to enable autonomous flight control and to maintain vehicle performance in the face of unknown, changing, or poorly defined operating environments [1-2]. At the present time, however, it is unknown how adaptive algorithms can be routinely verified, validated, and certified for use in safety-critical applications. Rigorous methods for adaptive software verification end validation must be developed to ensure that. the control software functions as required and is highly safe and reliable. A large gap appears to exist between the point at which control system designers feel the verification process is complete, and when FAA certification officials agree it is complete. Certification of adaptive flight control software verification is complicated by the use of learning algorithms (e.g., neural networks) and degrees of system non-determinism. Of course, analytical efforts must be made in the verification process to place guarantees on learning algorithm stability, rate of convergence, and convergence accuracy. However, to satisfy FAA certification requirements, it must be demonstrated that the adaptive flight control system is also able to fail and still allow the aircraft to be flown safely or to land, while at the same time providing a means of crew notification of the (impending) failure. It was for this purpose that the NASA Ames Confidence Tool was developed [3]. This paper presents the Confidence Tool as a means of providing in-flight software assurance monitoring of an adaptive flight control system. The paper will present the data obtained from flight testing the tool on a specially modified F-15 aircraft designed to simulate loss of flight control faces.
Jacklin, Stephen A.↗
Verification and Validation Challenges for Adaptive Flight Control of Complex Autonomous Systems
Autonomy of aerospace systems requires the ability for flight control systems to be able to adapt to complex uncertain dynamic environment. In spite of the five decades of research in adaptive control, the fact still remains that currently no adaptive control system has ever been deployed on any safety-critical or human-rated production systems such as passenger transport aircraft. The problem lies in the difficulty with the certification of adaptive control systems since existing certification methods cannot readily be used for nonlinear adaptive control systems. Research to address the notion of metrics for adaptive control began to appear in the recent years. These metrics, if accepted, could pave a path towards certification that would potentially lead to the adoption of adaptive control as a future control technology for safety-critical and human-rated production systems. Development of certifiable adaptive control systems represents a major challenge to overcome. Adaptive control systems with learning algorithms will never become part of the future unless it can be proven that they are highly safe and reliable. Rigorous methods for adaptive control software verification and validation must therefore be developed to ensure that adaptive control system software failures will not occur, to verify that the adaptive control system functions as required, to eliminate unintended functionality, and to demonstrate that certification requirements imposed by regulatory bodies such as the Federal Aviation Administration (FAA) can be satisfied. This presentation will discuss some of the technical issues with adaptive flight control and related V&V challenges.
Nguyen, Nhan T.↗
Aerobot Autonomy Architecture
An architecture for autonomous operation of an aerobot (i.e., a robotic blimp) to be used in scientific exploration of planets and moons in the Solar system with an atmosphere (such as Titan and Venus) is undergoing development. This architecture is also applicable to autonomous airships that could be flown in the terrestrial atmosphere for scientific exploration, military reconnaissance and surveillance, and as radio-communication relay stations in disaster areas. The architecture was conceived to satisfy requirements to perform the following functions: a) Vehicle safing, that is, ensuring the integrity of the aerobot during its entire mission, including during extended communication blackouts. b) Accurate and robust autonomous flight control during operation in diverse modes, including launch, deployment of scientific instruments, long traverses, hovering or station-keeping, and maneuvers for touch-and-go surface sampling. c) Mapping and self-localization in the absence of a global positioning system. d) Advanced recognition of hazards and targets in conjunction with tracking of, and visual servoing toward, targets, all to enable the aerobot to detect and avoid atmospheric and topographic hazards and to identify, home in on, and hover over predefined terrain features or other targets of scientific interest. The architecture is an integrated combination of systems for accurate and robust vehicle and flight trajectory control; estimation of the state of the aerobot; perception-based detection and avoidance of hazards; monitoring of the integrity and functionality ("health") of the aerobot; reflexive safing actions; multi-modal localization and mapping; autonomous planning and execution of scientific observations; and long-range planning and monitoring of the mission of the aerobot. The prototype JPL aerobot (see figure) has been tested extensively in various areas in the California Mojave desert.
Elfes, Alberto↗
Dryden Flight Research Highlights; August 2006 - July 2007
A DVD highlights the research activities of the Dryden Flight Research Center. The video includes coverage of space-based communication, intelligent flight controls, autonomous refueling demonstration, QuietSpike, sonic boom tests, G-III radar pod, X-48B blended wing body, Altair fire mission, Ikhana UAV, STS-117 Atlantis, and SOFIA Telescope research efforts.
Wright, David A.↗
Project Morpheus: Morpheus 1.5A Lander Failure Investigation Results
On August 9, 2012 the Morpheus 1.5A vehicle crashed shortly after lift off from the Kennedy Space Center. The loss was limited to the vehicle itself which was pre-declared to be a test failure and not a mishap. The Morpheus project is demonstrating advanced technologies for in space and planetary surface vehicles including: autonomous flight control, landing site hazard identification and safe site selection, relative surface and hazard navigation, precision landing, modular reusable flight software, and high performance, non-toxic, cryogenic liquid Oxygen and liquid Methane integrated main engine and attitude control propulsion system. A comprehensive failure investigation isolated the fault to the Inertial Measurement Unit (IMU) data path to the flight computer. Several improvements have been identified and implemented for the 1.5B and 1.5C vehicles.
Devolites, Jennifer L.↗
An intelligent training system for space shuttle flight controllers
An autonomous intelligent training system which integrates expert system technology with training/teaching methodologies is described. The system was designed to train Mission Control Center (MCC) Flight Dynamics Officers (FDOs) to deploy a certain type of satellite from the Space Shuttle. The Payload-assist module Deploys/Intelligent Computer-Aided Training (PD/ICAT) system consists of five components: a user interface, a domain expert, a training session manager, a trainee model, and a training scenario generator. The interface provides the trainee with information of the characteristics of the current training session and with on-line help. The domain expert (DeplEx for Deploy Expert) contains the rules and procedural knowledge needed by the FDO to carry out the satellite deploy. The DeplEx also contains mal-rules which permit the identification and diagnosis of common errors made by the trainee. The training session manager (TSM) examines the actions of the trainee and compares them with the actions of DeplEx in order to determine appropriate responses. A trainee model is developed for each individual using the system. The model includes a history of the trainee's interactions with the training system and provides evaluative data on the trainee's current skill level. A training scenario generator (TSG) designs appropriate training exercises for each trainee based on the trainee model and the training goals. All of the expert system components of PD/ICAT communicate via a common blackboard. The PD/ICAT is currently being tested. Ultimately, this project will serve as a vehicle for developing a general architecture for intelligent training systems together with a software environment for creating such systems.
Loftin, R. Bowen↗
An intelligent training system for space shuttle flight controllers
An autonomous intelligent training system which integrates expert system technology with training/teaching methodologies is described. The system was designed to train Mission Control Center (MCC) Flight Dynamics Officers (FDOs) to deploy a certain type of satellite from the Space Shuttle. The Payload-assist module Deploys/Intelligent Computer-Aided Training (PD/ICAT) system consists of five components: a user interface, a domain expert, a training session manager, a trainee model, and a training scenario generator. The interface provides the trainee with information of the characteristics of the current training session and with on-line help. The domain expert (Dep1Ex for Deploy Expert) contains the rules and procedural knowledge needed by the FDO to carry out the satellite deploy. The Dep1Ex also contains mal-rules which permit the identification and diagnosis of common errors made by the trainee. The training session manager (TSM) examines the actions of the trainee and compares them with the actions of Dep1Ex in order to determine appropriate responses. A trainee model is developed for each individual using the system. The model includes a history of the trainee's interactions with the training system and provides evaluative data on the trainee's current skill level. A training scenario generator (TSG) designs appropriate training exercises for each trainee based on the trainee model and the training goals. All of the expert system components of PD/ICAT communicate via a common blackboard. The PD/ICAT is currently being tested. Ultimately, this project will serve as a vehicle for developing a general architecture for intelligent training systems together with a software environment for creating such systems.
Loftin, R. Bowen↗
Semiautonomous Avionics-and-Sensors System for a UAV
Unmanned Aerial Vehicles (UAVs) autonomous or remotely controlled pilotless aircraft have been recently thrust into the spotlight for military applications, for homeland security, and as test beds for research. In addition to these functions, there are many space applications in which lightweight, inexpensive, small UAVS can be used e.g., to determine the chemical composition and other qualities of the atmospheres of remote planets. Moreover, on Earth, such UAVs can be used to obtain information about weather in various regions; in particular, they can be used to analyze wide-band acoustic signals to aid in determining the complex dynamics of movement of hurricanes. The Advanced Sensors and Electronics group at Langley Research Center has developed an inexpensive, small, integrated avionics-and-sensors system to be installed in a UAV that serves two purposes. The first purpose is to provide flight data to an AI (Artificial Intelligence) controller as part of an autonomous flight-control system. The second purpose is to store data from a subsystem of distributed MEMS (microelectromechanical systems) sensors. Examples of these MEMS sensors include humidity, temperature, and acoustic sensors, plus chemical sensors for detecting various vapors and other gases in the environment. The critical sensors used for flight control are a differential- pressure sensor that is part of an apparatus for determining airspeed, an absolute-pressure sensor for determining altitude, three orthogonal accelerometers for determining tilt and acceleration, and three orthogonal angular-rate detectors (gyroscopes). By using these eight sensors, it is possible to determine the orientation, height, speed, and rates of roll, pitch, and yaw of the UAV. This avionics-and-sensors system is shown in the figure. During the last few years, there has been rapid growth and advancement in the technological disciplines of MEMS, of onboard artificial-intelligence systems, and of smaller, faster, and smarter wireless telemetry systems. The major attraction of MEMS lies in orders-of-magnitude reductions of power requirements relative to traditional electronic components that perform equivalent functions. In addition, the compactness of MEMS, relative to functionally equivalent traditional electronics systems, makes MEMS attractive for UAV applications. Recent advances in MEMS have made it possible to produce pressure, acceleration, humidity, and temperature sensors having masses in subgram range and possessing sensitivities and accuracies comparable to those of larger devices.
Shams, Qamar↗
Fielding An Amphibious UAV: Development, Results, and Lessons Learned
This report summarizes the work completed on the design and flight-testing of a small, unmanned, amphibious demonstrator aircraft that flies autonomously. The aircraft named ACAT (Autonomous Cargo Amphibious Transport) is intended to be a large cargo carrying unmanned aircraft that operates from water to avoid airspace and airfield conflict issues between manned and unmanned aircraft. To demonstrate the feasibility of this concept, a demonstrator ACAT was designed, built, and flown that has a six-foot wingspan and can fly autonomously from land or water airfield. The demonstrator was designed for a 1-hour duration and 1-mile telemetry range. A sizing code was used to design the smallest demonstrator UAV to achieve these goals. The final design was a six-foot wingspan, twin hull configuration that distributes the cargo weight across the span, reducing the wing structural weight. The demonstrator airframe was constructed from balsa wood, fiberglass, and plywood. A 4-stroke model airplane engine powered by methanol fuel was mounted in a pylon above the wing and powers the ACAT UAV. Initial flight tests from land and water were conducted under manual radio control and confirmed the amphibious capability of the design. Flight avionics that were developed by MLB for production UAVs were installed in the ACAT demonstrator. The flight software was also enhanced to permit autonomous takeoff and landing from water. A complete autonomous flight from ahard runway was successfully completed on July 5, 2001 and consisted of a take-off, rectangular flight pattern, and landing under complete computer control. A completely autonomous flight that featured a water takeoff and landing was completed on October 4, 2001. This report describes these activities in detail and highlights the challenges encountered and solved during the development of the ACAT demonstrator. hard runway was successfully completed on July 5, 2001 and consisted of a take-off, rectangular flight pattern, and landing under complete computer control. A completely autonomous flight that featured a water takeoff and landing was completed on October 4, 2001. This report describes these activities in detail and highlights the challenges encountered and solved during the development of the ACAT demonstrator.
Pisanich, Greg↗