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Relating Sensor Degradation to Vehicle Situational Awareness for Autonomous Air Vehicle Certification
Abstract
Pilots use situational awareness (SA) to make appropriate aeronautical decisions. Autonomous vehicles will not have a human pilot (or operator) in the loop when off-nominal conditions present themselves, and will rely on sensors to build SA on their environment to make sound aeronautical decisions. As their sensors degrade, it is hypothesized that a point exists where the SA those decisions are based off will be inadequate for sound aeronautical decisions. It will be shown that this point can be identified through modeling and simulation of a simple sensor network to complete a task currently reserved for qualified pilots. This research highlights the process of determining an objective measure for the subjective end and relates it to a possible safety of flight certification for an autonomous system to perform tasks currently reserved for qualified pilots.
... Certification of AI has many forms such as (1) model certification [36] in response to imbalanced, costconstrained, active, transfer, and quantified learning, among others; (2) robustness performance assessment over the sensors [37], environments, and applications [38], (e.g., structural health monitoring [39,40,41]) and (3) software assurance such as blockchain [42]. Also, there is a need to consider the data, such as balancing privacy and security in certification standards [43,44]). ...
... The first observation was that the statistics were not consistent across all the image fusion methods and hence we chose two candidates with generally good performance: ADF and CNN. The second observation was that the ADF was typically the fastest (e.g., 0.054 s for distance 1) while the CNN was the slowest (e.g., 40.783 s for distance 1). ...
This paper reviews and proposes concerns in adopting, fielding, and maintaining artificial intelligence (AI) systems. While the AI community has made rapid progress, there are challenges in certifying AI systems. Using procedures from design and operational test and evaluation, there are opportunities towards determining performance bounds to manage expectations of intended use. A notional use case is presented with image data fusion to support AI object recognition certifiability considering precision versus distance.
... However, when SA estimated by the UAS did not match reality, the system was unable to complete the task [7]. A relationship was then demonstrated between sensor degradation and the UAS's ability to make sound aeronautical decisions during autonomous operation [8]. The crux of this line of research was to exercise a certification methodology that may be used to certify an UAS to make a decision currently reserved for a fully qualified pilot [9]. ...
Uncrewed aircraft deployed on aircraft carriers will be expected to have ever-increasing levels of autonomous functionality in the near future. This work presents early progress toward the certification of an uncrewed system to exhibit autonomous behavior while acting as the receiver during probe and drogue aerial refueling. The paper focuses on using a computer vision-based approach for identifying a drogue deployed by a tanker aircraft and determining its relative position to the refueling probe tip of the receiver. This will be critical if a vision-based solution will eventually feed into the control loop for the uncrewed system. This research used a fleet representative refueling drogue and probe. A computer vision system installed at the base of the refueling probe was used to feed imagery to a deep neural network trained to identify the refueling drogue and determine the drogue’s position relative to the probe tip. Ground truth experiments validated via a motion capture system demonstrated that the error in relative position measurements was within tolerances to allow the deep neural network to be used in feedback control for a notional uncrewed system. Finally, the laboratory-trained deep neural network demonstrated its ability to identify the drogue outside of the laboratory with mission-representative backgrounds.
... An integral problem to solve with sensor fusion will be ensuring the time synchronization and update rates between all contributing sensors have the precision and frequency required to enhance the blended solution and not inadvertently degrade the solution [79]. Without fusion or with a suboptimal fusion solution, the UA will be operating with decreased awareness to the environment, which will be a critical consideration for airworthiness officials to consider when conducting the risk assessment of the UA [80]. ...
As technologies advance and applications for uncrewed aircraft increase, the capability to conduct automated air-to-air refueling becomes increasingly important. This paper provides a review of required sensors to enable automated air-to-air refueling for an uncrewed aircraft, as well as a review of published research on the topic. Automated air-to-air refueling of uncrewed aircraft eliminates the need for ground infrastructure for intermediate refueling, as well as the need for on-site personnel. Automated air-to-air refueling potentially supports civilian applications such as weather monitoring, surveillance for wildfires, search and rescue, and emergency response, especially when airfields are not available due to natural disasters. For military applications, to enable the Air Wing of the Future to strike at the ranges required for the mission, both crewed and uncrewed aircraft must be capable of air-to-air refueling. To cover the sensors required to complete automated air-to-air refueling, a brief history of air-to-air refueling is presented, followed by a concept of employment for uncrewed aircraft refueling, and finally, a review of the sensors required to complete the different phases of automated air-to-air refueling. To complete uncrewed aircraft refueling, the uncrewed receiver aircraft must have the sensors required to establish communication, determine relative position, decrease separation to astern position, transition to computer vision, position keep during refueling, and separate from the tanker aircraft upon completion of refueling. This paper provides a review of the twelve sensors that would enable the uncrewed aircraft to complete the seven tasks required for automated air-to-air refueling.
... Other work, particularly in the medical context, has suggested basic prerequisite steps to facilitate regulation of autonomous systems, such as common data definitions, standards, and security protocols [41], [42]. And still other researchers have focused on generating evidence that can be used by a certification authority to judge the validity of the autonomous system [43]- [45]. ...
Autonomous systems promise significant improvements in many fields. These systems will be subject to legal governance requirements. The literature has largely focused on “autonomous governance” as a framework that is broadly applicable to autonomous devices regardless of the type of system (
e.g
., aviation or motor vehicles) at issue. While there are regulatory principles applicable to autonomous systems generally, an “autonomy-focused” approach is an inadequate lens to consider the governance of these systems. Rather, because autonomous systems are improvements of currently regulated complex systems, the regulation of autonomous elements will occur within those systems’ preexisting regulatory framework. Accordingly, the nature of future autonomous regulation will likely depend on the preexisting features of that substantive system, rather than on an optimal approach divorced from that history, an attribute known in the social science literature as path dependency. In order to characterize diverse regulated systems with an eye toward assessing future autonomous developments, we develop a framework of regulatory approaches to identify specific features of the preexisting regulatory scheme for a given system. We then analyze that approach by examining three different regulatory regimes (aviation, motor vehicles, and medical devices), across two different continents, and consider how the same type of requirement,
e.g
., fail-safe systems, can lead to different types of regulations depending on the differing baseline framework.
... For certifiable AI, the issue is to determine where and what is being certified for bounds of performance (Fig. 9). Certification of AI has many forms such as (1) model certification [49] in response to imbalanced, costconstrained, and quantified learning, among others; (2) robustness performance assessment over the sensors [50], environments, and applications [51], (e.g., structural health monitoring [52,53]) and (3) software assurance such as blockchain [54]. Also, there is a need to consider the data use such as balancing privacy and security in certification standards [55,56]). ...
... .; C n , the corresponding uncertainty probability is p 1 ; p 2 ; . . .; p n , and these source symbols are independent of each other [42]. Then, the information entropy of these information source symbols can be defined as follows: ...
Underground pipelines are an indispensable part of urban public facilities. However, the frequent occurrence of pipeline accidents in recent years has not only brought great inconvenience to people’s lives, but also affected people’s lives and property safety to a certain extent. Therefore, timely treatment and treatment are very important. Preventing sudden underground pipeline accidents plays an important role in improving urban livability. This article studies pipeline risk big data intelligent decision-making systems based on machine learning and situational awareness. In this paper, by analyzing the application scope of gas leakage and diffusion models under different modes, leakage, diffusion, fire and explosion models are determined, and a combined model framework of leakage accident consequence system analysis is formed. The system uses the pipeline failure probability model and the pipeline failure consequence analysis model to determine the pipeline failure probability, the probability and the consequences of each accident; it uses the spatial analysis ability of GIS technology to determine the accident impact area and displays the impact area in graphics form. Through the effect verification of the test set, the prediction result of the SVR model based on the grid search parameter, the relative percentage error of the predicted value of each sample and the true value fluctuate is in the range of 4%-36%, and the amplitude is not very large. Most of the error values are approximately 13.56% of the MAPE value. The results show that the optimization method using grid search parameters can have better prediction performances.
Pilots use their senses and training to generate situational awareness (SA). They then use this SA to make sound aeronautical decisions. Autonomous vehicles, by contrast, cannot rely on pilot expertise in off‐nominal situations. They must rely on their onboard sensors to build SA of the environment. As these sensors degrade, it is hypothesized that a point exists where the SA generated by these sensors is inadequate to allow the autonomous vehicle to make sound aeronautical decisions. In previous work, a point was defined based on broad assumptions within a modeling and simulation environment (i.e., the error within each sensor was known and not random). This research used a larger data set that contained random errors within the sensors. The data was then used to build predictive equations through a Monte Carlo simulation in the same simulation environment as previous work. While the data showed there was a statistically significant relationship between the error values in each sensor and the fused distance value, the resulting predictive equations were not able to provide adequate SA to make sound aeronautical decisions. This research highlights multiple issues the test and evaluation community will face when trying to develop new techniques for the verification and validation of autonomous systems.
This paper investigates the use of a heterogeneous stereo-vision system to mitigate the effects of time delays in a drone-based visual interface presented to a human operator. Time delays in the display for a telerobotic interface refer to the time difference between the operator’s input action and the corresponding visible outcome. In human/machine interfaces, time delays can arise due to computation, telecommunication, and mechanical limitations. These delays can degrade the performance of the human/machine system. A heterogeneous stereo-vision predictive algorithm is presented that can reduce the negative effects of time delays in the operator’s display. The heterogeneous stereo-vision system consists of an omnidirectional camera and a pan/tilt/zoom camera. Two predictive display setups were developed that modify the delayed video imagery that would otherwise be presented to the operator in a way that provides an almost immediate visual response to the operator’s control actions. The usability of the system is determined through human performance testing with and without the predictive algorithms. The results indicate that the predictive algorithm allows more efficient, accurate, and user-friendly operation.
The methods and procedures within United States naval aviation to certify an aircraft safe for flight are well established. However, these methods and procedures are based on clearing a system that is operated or monitored by a human. A fully autonomous system will not have a human in or on the loop and will therefore require a new method for certifying it safe for flight. This paper details how to use run time assurance as the framework for a safety of flight certification of autonomous behavior within United States naval aviation. We present an aerial refueling task with run time assurance as use case for the framework for certification. Within the use case we then give more details on the mechanics of using RTA to enable autonomous functionality within naval aviation.
Cooperative autonomous air combat of multiple unmanned aerial vehicles (UAVs) is one of the main combat modes in future air warfare, which becomes even more complicated with highly changeable situation and uncertain information of the opponents. As such, this paper presents a cooperative decision-making method based on incomplete information dynamic game to generate maneuver strategies for multiple UAVs in air combat. Firstly, a cooperative situation assessment model is presented to measure the overall combat situation. Secondly, an incomplete information dynamic game model is proposed to model the dynamic process of air combat, and a dynamic Bayesian network is designed to infer the tactical intention of the opponent. Then a reinforcement learning framework based on multi-agent deep deterministic policy gradient is established to obtain the perfect Bayes-Nash equilibrium solution of the air combat game model. Finally, a series of simulations are conducted to verify the effectiveness of the proposed method, and the simulation results show effective synergies and cooperative tactics.
This paper aims to find a reliable, collision-free path in a dynamic environment for highly maneuverable unmanned combat air vehicles (UCAVs). Given the real-time nature of the operational scenario, quick and adaptable reactions of UCAVs are necessary for updates in situational awareness. Therefore, we propose a three dimensional (3D) path planning approach based on the situational space to provide the tactical requirements of UCAVs for tracking targets and avoiding collisions. First, to ensure reliable nonlinear measurements, the interacting multiple model (IMM) algorithm based on a cubature Kalman filter (CKF) is chosen for the tracking and prediction algorithm. A constraint reference frame combining the kinematic model of constant acceleration (CA) is developed to solve the problem of arrival point generation. Second, by analyzing the relative motion between the UCAV and the moving objects, we define the situation space and give the corresponding calculation method. In tracking the moving target, the guidance vector contains the fusion information of displacement and velocity. At the same time, taking advantage of the one-step situation space as the judgment of the threat, we further plan the collision avoidance strategy. Third, as the safety in a practically reachable trajectory of the UCAV possesses the absolute priority, the collision avoidance acceleration accounts for this dominant factor in path planning. Simulations and experimental results prove that the proposed approach can plan a smooth and flyable path in 0.008 s under the premise of soft-landing target tracking.
It is expected that soon there will be a significant number of unmanned aerial vehicles (UAVs) operating side-by-side with manned civil aircraft in national airspace systems. To be able to integrate UAVs safely with civil traffic, a number of challenges must be overcome first. This study investigates situational awareness of UAVs' autonomous taxiing in an aerodrome environment. The research work is based on a real outdoor experimental data collected at the Walney Island Airport, the UK. It aims to further develop and test UAVs' autonomous taxiing in a challenging outdoor environment. To address various practical issues arising from the outdoor aerodrome such as camera vibration, taxiway feature extraction, and unknown obstacles, the authors develop an integrated approach that combines the Bayesian-network based semantic segmentation with a self-learning method to enhance situational awareness of UAVs. Detailed analysis of the outdoor experimental data shows that the integrated method developed in this study improves the robustness of situational awareness for autonomous taxiing.
Situation awareness (SA) is an important constituent in human information processing and essential in pilots' decision-making processes. Acquiring and maintaining appropriate levels of SA is critical in aviation environments as it affects all decisions and actions taking place in flights and air traffic control. This paper provides an overview of recent measurement models and approaches to establishing and enhancing SA in aviation environments. Many aspects of SA are examined including the classification of SA techniques into six categories, and different theoretical SA models from individual, to shared or team, and to distributed or system levels. Quantitative and qualitative perspectives pertaining to SA methods and issues of SA for unmanned vehicles are also addressed. Furthermore, future research directions regarding SA assessment approaches are raised to deal with shortcomings of the existing state-of-the-art methods in the literature.
Unmanned Aerial Vehicles (UAVs) are being extensively used nowadays. Therefore, pilots of traditional aerial platforms should adapt their skills to operate them from a Ground Control Station (GCS). Common GCSs provide information in separate screens: one presents the video stream while the other displays information about the mission plan and information coming from other sensors. To avoid the burden of fusing information displayed in the two screens, an Augmented Reality (AR) tool is proposed in this paper. The AR system has two functionalities for Medium-Altitude Long-Endurance (MALE) UAVs: route orientation and target identification. Route orientation allows the operator to identify the upcoming waypoints and the path that the UAV is going to follow. Target identification allows a fast target localization, even in the presence of occlusions. The AR tool is implemented following the North Atlantic Treaty Organization (NATO) standards so that it can be used in different GCSs. The experiments show how the AR tool improves significantly the situational awareness of the UAV operators.
Objective: We systematically review recent empirical research on factors that influence trust in automation to present a three-layered trust model that synthesizes existing knowledge.
Background: Much of the existing research on factors that guide human-automation interaction is centered around trust, a variable that often determines the willingness of human operators to rely on automation. Studies have utilized a variety of different automated systems in diverse experimental paradigms to identify factors that impact operators’ trust.
Method: We performed a systematic review of empirical research on trust in automation from January 2002 to June 2013. Papers were deemed eligible only if they reported the results of a human-subjects experiment in which humans interacted with an automated system in order to achieve a goal. Additionally, a relationship between trust (or a trust-related behavior) and another variable had to be measured. All together, 101 total papers, containing 127 eligible studies, were included in the review.
Results: Our analysis revealed three layers of variability in human–automation trust (dispositional trust, situational trust, and learned trust), which we organize into a model. We propose design recommendations for creating trustworthy automation and identify environmental conditions that can affect the strength of the relationship between trust and reliance. Future research directions are also discussed for each layer of trust.
Conclusion: Our three-layered trust model provides a new lens for conceptualizing the variability of trust in automation. Its structure can be applied to help guide future research and develop training interventions and design procedures that encourage appropriate trust.
This paper reports an ongoing effort that supports NASA research in Airportal Transition and Integration Management as NASA prepares for the challenges anticipated with the Next Generation Air Transportation System (NextGen). The NASA Airportal Project is directed toward achieving increases in capacity and throughput in the terminal area, while maintaining the highest possible standards of safety. The requirements of NextGen demand the implementation of high levels of automation, and of automation functioning at a higher level of autonomy (LOA). The research described here seeks to mitigate the possibility of introducing automation that has a negative impact on the system by developing a predictive model of situation awareness (SA) that can be used to evaluate the impact of automation on operator SA early in the design cycle. In an effort to reduce negative impacts on operator SA, the research includes developing a predictive model of SA using a Fuzzy Cognitive Map (FCM), which can be used to evaluate tools intended to automate tasks for aviation personnel. SA theory was used as the foundation of the design of the FCM model. The report describes the methodology of designing the FCM that includes utilizing SA theory as the starting point. Cognitive tasks analyses were employed to further develop the model. Human factors and systems engineers will use the evaluation tool to assess the impact of their automated systems on operator SA by defining the work environment, operational parameters, tool characteristics, and environmental variables. The FCM model may be applied to aerospace automation design to evaluate similar human factors related issues.
As mini-UAVs become more capable and reliable, it is important to start looking at the factors differentiating them from other classes of unmanned vehicles. One such factor is the physical proximity of operators to the vehicle during deployment. Operators of these UAVs are often within sight of their vehicle, and share many environmental cues such as visual landmarks. However, operating in the field also entails additional environmental stresses, such as less optimal use of computer equipment, variations in weather, and the physical demands of the terrain. In this paper, a pilot study is conducted to determine if any of these factors significantly impact situation awareness, by comparing operator performance in a visual identification task in a live field test with operators performing an identical task in a lab environment. Metric results suggest that performance is similar across the two conditions, but qualitative responses from participants suggest that the underlying strategies employed differ in the two conditions.
Purpose: We present partial least squares (PLS) as an evolving approach to structural equation modeling (SEM), highlight its advantages and limitations and provide an overview of recent research on the method across various fields.
Design/methodology/approach: In this review article we merge literatures from the marketing, management, and management information systems fields to present the state-of-the art of PLS-SEM research. Furthermore, we meta-analyze recent review studies to shed light on popular reasons for PLS-SEM usage.
Findings: PLS-SEM has experienced increasing dissemination in a variety of fields in recent years with non-normal data, small sample sizes and the use of formative indicators being the most prominent reasons for its application. Recent methodological research has extended PLS-SEM’s methodological toolbox to accommodate more complex model structures or handle data inadequacies such as heterogeneity.
Research limitations/implications: While research on the PLS-SEM method has gained momentum during the last decade, there are ample research opportunities on subjects such as mediation or multigroup analysis, which warrant further attention.
Originality/value: This article provides an introduction to PLS-SEM for researchers that have not yet been exposed to the method. The article is the first to meta-analyze reasons for PLS-SEM usage across the marketing, management, and management information systems fields. Our cross-disciplinary review of recent research on the PLS-SEM method also makes this article useful for researchers interested in advanced concepts.
This paper presents a theoretical model of situation awareness based on its role in dynamic human decision making in a variety of domains. Situation awareness is presented as a predominant concern in system operation, based on a descriptive view of decision making. The relationship between situation awareness and numerous individual and environmental factors is explored. Among these factors, attention and working memory are presented as critical factors limiting operators from acquiring and interpreting information from the environment to form situation awareness, and mental models and goal-directed behavior are hypothesized as important mechanisms for overcoming these limits. The impact of design features, workload, stress, system complexity, and automation on operator situation awareness is addressed, and a taxonomy of errors in situation awareness is introduced, based on the model presented. The model is used to generate design implications for enhancing operator situation awareness and future directions for situation awareness research.
Situation awareness (SA) is an important component of pilot/system performance in all types of aircraft. It is the role of the human factors engineer to develop aircraft cockpits which will enhance SA. Research in the area of situation awareness is is vitally needed if system designers are to meet the challenge of providing cockpits which enhance SA. This paper presents a discussion of the SA construct, important considerations facing designers of aircraft systems, and current research in the area of SA measurement.
In the first part of the reported research, 12 instrument-rated pilots flew a high-fidelity simulation, in which air traffic control presentation of auditory (voice) information regarding traffic and flight parameters was compared with advanced display technology presentation of equivalent information regarding traffic (cockpit display of traffic information) and flight parameters (data link display). Redundant combinations were also examined while pilots flew the aircraft simulation, monitored for outside traffic, and read back communications messages. The data suggested a modest cost for visual presentation over auditory presentation, a cost mediated by head-down visual scanning, and no benefit for redundant presentation. The effects in Part 1 were modeled by multiple-resource and preemption models of divided attention. In the second part of the research, visual scanning in all conditions was fit by an expected value model of selective attention derived from a previous experiment. This model accounted for 94% of the variance in the scanning data and 90% of the variance in a second validation experiment. Actual or potential applications of this research include guidance on choosing the appropriate modality for presenting in-cockpit information and understanding task strategies induced by introducing new aviation technology.
The last 15 years have seen a large uptick in the use of unmanned aircraft. However, the current safety of flight clearances for unmanned aircraft requires a qualified operator who can make decisions and ultimately bear the responsibly for the safe operations of the vehicle. The future of aviation is unmanned, and ultimately autonomous. Yet, a clear path for certifying an autonomous vehicle to make decisions currently reserved for qualified pilots does not exist. This paper presents a preliminary approach for certifying an autonomous controller to select an appropriate landing site for a large rotorcraft in an unprepared landing zone. In particular, this paper will decompose the steps currently used by qualified pilots to the basic requirements to define an envelope where the vehicle will be allowed to operate autonomously while landing. These requirements are the basis for a specification that we examine to ensure it met the requirements. A protocol is developed based on the analyzed specification that will ensure what the vehicle “will not do” while operating autonomously. Finally, we describe how this protocol can be used as the safety of flight evidence, and eventually for clearing an autonomous controller to complete a task reserved for qualified pilots.
As autonomous and semiautonomous systems are developed for automotive, aviation, cyber, robotics and other applications, the ability of human operators to effectively oversee and interact with them when needed poses a significant challenge. An automation conundrum exists in which as more autonomy is added to a system, and its reliability and robustness increase, the lower the situation awareness of human operators and the less likely that they will be able to take over manual control when needed. The human-autonomy systems oversight model integrates several decades of relevant autonomy research on operator situation awareness, out-of-the-loop performance problems, monitoring, and trust, which are all major challenges underlying the automation conundrum. Key design interventions for improving human performance in interacting with autonomous systems are integrated in the model, including human-automation interface features and central automation interaction paradigms comprising levels of automation, adaptive automation, and granularity of control approaches. Recommendations for the design of human-autonomy interfaces are presented and directions for future research discussed.
The European Data Relay System (EDRS) will provide a high speed data link between ground stations and satellites in low earth orbit. Up to 400 links per day are foreseen to be commanded by the groundsystems established at DLR's German Space Operations Center. The high command load is beyond the capabilities of a classical operational concept with manual operations. Therefore an automated system is established at the Devolved Payload Control Center with human interaction only necessary after a contingency, either in the ground processing or the space segment. One of the biggest challenges in this operational concept will be to become aware of the complex situation after any not-nominal situation. A reporting system will inform the on-call personnel of the current state of all components. This paper presents the operational concept established at the DPCC.
The main goal of this research effort is to find a flyable collision-free path for an unmanned aerial vehicle (UAV) in a dynamic environment. Given that the UAV path planning needs to adapt in near real-time to the dynamic nature of the operational scenario, and to react rapidly to updates in the situational awareness, a modified artificial potential field (MAPF) approach is utilized to provide collision avoidance in view of pop-up threats and a random set of moving obstacles. To ensure a practically reachable trajectory, this paper proposes a constraint reference frame to develop MAPF so that the decomposed forces from MAPF can be matched with the physical constraints of the UAV for online adjustment. Simulations and experimental results provide promising validation in terms of the efficiency and scalability of the proposed approach.
Situation awareness (SA) has become a widely used construct within the human factors community, the focus of considerable research over the past 25 years. This research has been used to drive the development of advanced information displays, the design of automated systems, information fusion algorithms, and new training approaches for improving SA in individuals and teams. In recent years, a number of papers criticized the Endsley model of SA on various grounds. I review those criticisms here and show them to be based on misunderstandings of the model. I also review several new models of SA, including situated SA, distributed SA, and sensemaking, in light of this discussion and show how they compare to existing models of SA in individuals and teams.
The role of situation awareness (SA) in aircrew safety and survivability continues to be of considerable interest to the aviation community. Various methods of measuring SA have been proposed. Among these are SAGAT and SA-SWORD. The former is an objective measure of SA, while the latter is based on operators' subjective comparisons. This paper reports the findings of a study that compared these two metrics in a simulated low-level ingress scenario. It was found that the type of SA metric used did not affect performance measures collected. While independent variables manipulated had large and statistically significant effects upon SA-SWORD data, this was not true for SAGAT data and the two SA measures were not significantly correlated. Advantages and disadvantages of the two techniques are discussed, and lessons learned from their application in this study are reported.
This paper presents an interface system display which is conceived to improve pilot situation awareness with respect to a flight envelope protection system developed for a mid-sized transport aircraft. The new display is designed to complement existing cockpit displays, and to augment them with information that relates to both aircraft state and the control automation itself. In particular, the proposed display provides cues about the state of automation directly in terms of pilot control actions, in addition to flight parameters. The paper also describes a forthcoming evaluation test plan that is intended to validate the developed interface by assessing the relevance of the displayed information, as well as the adequacy of the display layout.
This article uses the case of the production of jet engines during the Second World War to challenge how historians think about the Third Reich's relationship to new weapons. Far from wonder weapons, Germany's jet engines, first deployed in July 1944, were engines of desperation, unreliable but well fitted to the conditions of production in the National Socialist war economy. Despite the chaos of late war weapons production in Germany, Albert Speer's Armament's Ministry oversaw the manufacture of large numbers of jet engines, exploiting in particular the Mittelwerk weapons factory. The jet did not allow Germany to regain air superiority, but jet engines optimized for production enabled the regime to produce aero-engines as efficiently as possible with its remaining resources. Great Britain also deployed jet aircraft in mid-1944, but its government de-emphasized short-term engine production in favour of a broad development programme. Comparison of the different paths followed by the two nations to jet engines highlights how the design and production of jet engines in the Third Reich reflected a compromise typical of the regime's last years: manufacturing masses of inferior weapons, whose virtue lay in the fact that they were easy to build. © The Author(s) 2013 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.
We present a hierarchical flight control system with a situation-aware capability using the model-predictive control (MPC) approach. The optimization process computes the optimal control input sequence over a finite horizon for a conflict-free trajectory with an explicit consideration on the vehicle’s maneuverability and the behavioral constraints such as obstacle avoidance. The obtained trajectory and control input pair is sent to a two degreeof-freedom control loop, which can be chosen to run in feedforward, feedback, or both. The MPC module also serves as a safeguard interface between the vehicle control and the highlevel path generator, which may request a waypoint sequence not plausible with the kinodynamic constraints of the servoing vehicle. The proposed algorithm is integrated into the Berkeley UAV flight control software. The behavior of the overall software is examined for reliable and accurate in-flight operations with emphasis on the execution time and process synchronization. The implemented approach was applied to a complex flight scenario involving itinerary changes on-the-fly tested successfully in a series of flights.
An approach to cooperative sensor management is proposed to coordinate track discovery, refinement, and maintenance that balances the workload across the available flight members, reduces the redundant data that is shared across the data link, and increases track capacity and system responsiveness to new objects in the environment. This method provides a distributed, but cooperative sensor management framework that allows optimization locally at each aircraft, but permits cooperation across a flight group (or larger community) autonomously to improve the operational picture of the flight group. The approach supports cooperative search schemes that focus different platforms on complementary subsets of the environment while still providing complete coverage as a group. Predictions for the expected improvement in flight group capacity and reductions in data link loading are shown to be proportional to the volume of the overlapping coverage.
An Autonomous Flight Manager (AFM) is an integral and necessary component to achieve NASA’s goal of safe and precise landing to extend exploration beyond Low Earth Orbit. This goal includes reduced interaction with mission operations unlike those present during the Apollo era. To that end, the cr ew’s situation awareness of the vehicle’s performance with regard to executing a predefined descent trajectory is critical. The Multi State Excursion Assessment (MSEA) algorithm is a central component in providing this situation awareness by monitoring designated trajectory parameters as defined in a mission profile, which is outside the scope of traditional guidance algorithm functionality . During the mission, the MSEA algorithm compares real-time updates of the current trajectory state with parameter specific thresholds dictating nominal performance. The algorithm fuses this information into a composite assessment of the traversed trajectory versus the predefined nominal trajectory. The assessment includes a projection of emergent trends leading to imminent violation of the prescribed trajectory. Through the use of the MSEA algorithm, the AFM significantly reduces the crew’s monitoring responsibilities by providing them with an assessment of current and future mission performance, which alleviates the n eed to monitor and assess the individual performance parameters manually.
Satellite operators manually fuse large amounts of data and information to develop space situational awareness. As systems increase in complexity and autonomy, this process will become prohibitively expensive due to human cognitive ability and limited operator training. To address this problem, we performed preliminary research into the application of intelligent agents for automated data and information fusion for groundbased satellite operations. In particular, we focused on demonstrating the feasibility of using intelligent agents that employ fuzzy logic-based Information Processors to generate semantic characterizations of the data, Bayesian belief network-based Situation Assessors to generate a high-level interpretation of the operational situation as a function of perceived events and situational memory, and rule-based Decision-Makers to adaptively configure user interfaces and alert operators. This paper describes research results in developing and verifying (via simulated data) the situation assessor component for a geosynchronous communications satellite subjected to space weather and satellite anomalies. © 2003 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved.
During the development of an autonomous mini helicopter UAV at the Delft University of Technology, the objective is to improve the capabilities, the safety and the degree of autonomy at the same time within the severe weight limitations imposed by the vehicle. The weight limitation implies a minimal amount of sensors while autonomy, redundancy and extra capabilities require a lot of information. Motivated by the increasing computer power, the improving quality of digital imaging and the increasing performance of digital image analysis, this paper proposes "camera images" as extra source of information for the guidance and situation awareness of a gps position and inertial attitude stabilized UAV helicopter. The analysis of video together with the other onboard sensor data can greatly enhance the systems overall situation awareness. Simulation and real flight, tests show that intelligent analysis of the steaming images is capable of providing a huge amount of mission-relevant knowledge. Moreover, since images contain a lot of redundant information, the onboard video can partially replace other sensors and thus cope with a variety of failures of the other sensors onboard the UAV; in occurrence the magnetometer, gps and imu. However, since analysis of images only provides statistical information about just parts of the surroundings, visual observations should be used carefully. Based on the idea that as long as the system knows the certainty of its observations it can react in accordance, the uncertainties are reduced where possible using active vision and biologically inspired algorithms. The vision-based awareness algorithms can detect and track landing pads, provide guidance towards objects of interest and reconstruct 3D terrain information. They can even provide answers to survive magnetic, gps and some inertial sensor failures using only a single camera and they can provide collision avoidance reflexes to avoid fatal collisions. The proposed system features a significant increase in situation awareness for a very modest weight and cost. Combining additional capabilities with additional safety through situation awareness in a single low-cost sensor is particularly important as weight, cost and safety are dominant factors of UAV development. Copyright © 2005 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved.
A pilot faces special challenges imposed by the need to control a multivariate lagged system in a heterogeneous multitask environment. The time lags between critical variables require prediction in an uncertain world. The interrelated concepts of situation awareness and workload are central to aviation psychology. Three components of situation awareness are spatial awareness, system awareness, and task awareness. Each of these components has real–world implications, spatial awareness for instrument displays, system awareness for keeping the operator informed about actions that have been taken by automated systems, and task awareness for attention and task management. Task management is directly related to mental workload, as the competing demands of tasks for attention exceed the operator's limited resources.
During World War II, the American and British governments sponsored collaborative research in radar technology. Beginning in September 1940, scientists from the two countries increasingly shared information and worked together to develop radar systems. The radar technology developed through this cooperation had a significant impact on the outcome of the war. There is more to the story, however, than white‐coated scientists joining forces in the name of the common good. The process of wartime radar development influenced and was influenced by the ever‐changing character and priorities of the Anglo‐American diplomatic and military alliance. Social and political factors also played an important role within the research and development process itself. This article will examine radar research during World War II in the context of the collaborative efforts of the British and Americans to develop the technology, and will attempt to uncover the relationships between radar development efforts and the politics of the Anglo‐American alliance.
CVN Precision Approach and Landing Systems (PALS) Certification Tests
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Government Laboratory Technology Transfer: Process and Impact Assessment
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The Use of Pilot Rating in the Evaluation of Aircraft Handling Qualities
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