Pub Date : 2016-09-01DOI: 10.1109/DASC.2016.7778101
Yue Zhao, J. J. Zhu
An autonomous integrated Loss-of-Control (LOC) Prevention and Recovery (iLOCPR) system for UAVs is proposed with four operation modes: a nominal mode designed for 6 degree-of-freedom (DOF) trajectory tracking by trajectory linearization control; a LOC prevention mode designed by bandwidth adaptation augmentation to the baseline nominal controller for increasing the stability margin in the presence of LOC-prone flight conditions; a LOC arrest mode by reconfiguring the controller to recover and maintain healthy flight aerodynamic angles while temporarily giving up the trajectory tracking mission; a restoration mode to guide the vehicle back to the mission trajectory after successful LOC arrest. A supervisory discrete-event-driven automatic flight management system (AFMS) is designed to autonomously reconfigure the flight controller by coordinating and switching the control modes according to the real-time sensed flight conditions. A full comprehensive simulation entailing the nominal trajectory tracking, LOC prevention, LOC arrest and mission restoration is provided to demonstrate the effectiveness of modes switching and the performance of the iLOCPR system. The proposed framework can be further augmented for autonomous fault tolerance and collision avoidance in future development.
{"title":"An autonomous flight management system for prevention and recovery of unmanned aerial vehicle loss-of-control","authors":"Yue Zhao, J. J. Zhu","doi":"10.1109/DASC.2016.7778101","DOIUrl":"https://doi.org/10.1109/DASC.2016.7778101","url":null,"abstract":"An autonomous integrated Loss-of-Control (LOC) Prevention and Recovery (iLOCPR) system for UAVs is proposed with four operation modes: a nominal mode designed for 6 degree-of-freedom (DOF) trajectory tracking by trajectory linearization control; a LOC prevention mode designed by bandwidth adaptation augmentation to the baseline nominal controller for increasing the stability margin in the presence of LOC-prone flight conditions; a LOC arrest mode by reconfiguring the controller to recover and maintain healthy flight aerodynamic angles while temporarily giving up the trajectory tracking mission; a restoration mode to guide the vehicle back to the mission trajectory after successful LOC arrest. A supervisory discrete-event-driven automatic flight management system (AFMS) is designed to autonomously reconfigure the flight controller by coordinating and switching the control modes according to the real-time sensed flight conditions. A full comprehensive simulation entailing the nominal trajectory tracking, LOC prevention, LOC arrest and mission restoration is provided to demonstrate the effectiveness of modes switching and the performance of the iLOCPR system. The proposed framework can be further augmented for autonomous fault tolerance and collision avoidance in future development.","PeriodicalId":340472,"journal":{"name":"2016 IEEE/AIAA 35th Digital Avionics Systems Conference (DASC)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130019659","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-09-01DOI: 10.1109/DASC.2016.7777946
Cesar A. Nava-Gaxiola, C. Barrado
As part of the Single European Sky Airspace Research program (SESAR) a new operational instrument is being developed: the Free Route Airspace (FRA). FRA defines airspace areas where user can decide about the best performance routes, not subjected to airways or mandatory crossing points. Currently, 11 FRA projects are been deployed specially in low density areas and low density time periods. Long-term benefits for one single FRA can account for saving up to 32,000 nautical miles per day, which may represent around 100,000 Euros savings per days. In this paper we assess the benefit figures with the opinions of the involved air traffic controllers. They point to the challenges to be overtaken before extending the future FRA. An important issue, raised by the air traffic controllers, was the importance of the support tools. Also important are the previous training and a full FRA deployment.
{"title":"Free route airspace and the need of new air traffic control tools","authors":"Cesar A. Nava-Gaxiola, C. Barrado","doi":"10.1109/DASC.2016.7777946","DOIUrl":"https://doi.org/10.1109/DASC.2016.7777946","url":null,"abstract":"As part of the Single European Sky Airspace Research program (SESAR) a new operational instrument is being developed: the Free Route Airspace (FRA). FRA defines airspace areas where user can decide about the best performance routes, not subjected to airways or mandatory crossing points. Currently, 11 FRA projects are been deployed specially in low density areas and low density time periods. Long-term benefits for one single FRA can account for saving up to 32,000 nautical miles per day, which may represent around 100,000 Euros savings per days. In this paper we assess the benefit figures with the opinions of the involved air traffic controllers. They point to the challenges to be overtaken before extending the future FRA. An important issue, raised by the air traffic controllers, was the importance of the support tools. Also important are the previous training and a full FRA deployment.","PeriodicalId":340472,"journal":{"name":"2016 IEEE/AIAA 35th Digital Avionics Systems Conference (DASC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125909237","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-09-01DOI: 10.1109/DASC.2016.7778026
Laureano Fernandez-Olmos, F. Burrull, P. Pavón-Mariño
In this paper we present the AFDX (Avionics Full-DupleX switched Ethernet) extension for the open-source networking tool Net2Plan [1][2]. The Net2Plan-AFDX extension is also open-source, with no cost. This software will provide integrators, researchers and students with a set of tools to calculate, given an AFDX network design, an exhaustive set of AFDX parameters and performance merits. Net2Plan-AFDX can also be used for developing additional tools to evaluate and research alternatives related to AFDX. The implemented AFDX modules offer a Network Calculus and a Trajectory Approach algorithms' implementation for the theoretical worst-case delay calculation, given a configuration table. It also implements a simulation tool that produces realistic end-to-end latency values expected in an operational environment, as well as other performance merits. Avionics System Integrators can improve their designs using the obtained results.
{"title":"Net2Plan-AFDX: An open-source tool for optimization and performance evaluation of AFDX networks","authors":"Laureano Fernandez-Olmos, F. Burrull, P. Pavón-Mariño","doi":"10.1109/DASC.2016.7778026","DOIUrl":"https://doi.org/10.1109/DASC.2016.7778026","url":null,"abstract":"In this paper we present the AFDX (Avionics Full-DupleX switched Ethernet) extension for the open-source networking tool Net2Plan [1][2]. The Net2Plan-AFDX extension is also open-source, with no cost. This software will provide integrators, researchers and students with a set of tools to calculate, given an AFDX network design, an exhaustive set of AFDX parameters and performance merits. Net2Plan-AFDX can also be used for developing additional tools to evaluate and research alternatives related to AFDX. The implemented AFDX modules offer a Network Calculus and a Trajectory Approach algorithms' implementation for the theoretical worst-case delay calculation, given a configuration table. It also implements a simulation tool that produces realistic end-to-end latency values expected in an operational environment, as well as other performance merits. Avionics System Integrators can improve their designs using the obtained results.","PeriodicalId":340472,"journal":{"name":"2016 IEEE/AIAA 35th Digital Avionics Systems Conference (DASC)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125913110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-09-01DOI: 10.1109/DASC.2016.7778103
K. N. Maleki, K. Ashenayi, L. Hook, Justin G. Fuller, N. Hutchins
Despite the tremendous attention Unmanned Aerial Vehicles (UAVs) have received in recent years for applications in transportation, surveillance, agriculture, and search and rescue, as well as their possible enormous economic impact, UAVs are still banned from fully autonomous commercial flights. One of the main reasons for this is the safety of the flight. Traditionally, pilots control the aircraft when complex situations emerge that even advanced autopilots are not able to manage. Artificial Intelligence based methods and Adaptive Controllers have proven themselves to be efficient in scenarios with uncertainties; however, they also introduce another concern: nondeterminism. This research endeavors to find a solution on how such algorithms can be utilized with higher reliability. Our method is based on using an adaptive model to verify the performance of a control parameter - proposed by a nondeterministic adaptive controller or AI-based optimizer - before it is deployed on the physical platform. Furthermore, a backup mechanism is engaged to recover the drone in case of failure. A Neural Network is employed to model the aircraft, and a Genetic Algorithm is utilized to optimize the PID controller of a quadcopter. The initial experimental results from test flights indicate the feasibility of this method.
{"title":"A reliable system design for nondeterministic adaptive controllers in small UAV autopilots","authors":"K. N. Maleki, K. Ashenayi, L. Hook, Justin G. Fuller, N. Hutchins","doi":"10.1109/DASC.2016.7778103","DOIUrl":"https://doi.org/10.1109/DASC.2016.7778103","url":null,"abstract":"Despite the tremendous attention Unmanned Aerial Vehicles (UAVs) have received in recent years for applications in transportation, surveillance, agriculture, and search and rescue, as well as their possible enormous economic impact, UAVs are still banned from fully autonomous commercial flights. One of the main reasons for this is the safety of the flight. Traditionally, pilots control the aircraft when complex situations emerge that even advanced autopilots are not able to manage. Artificial Intelligence based methods and Adaptive Controllers have proven themselves to be efficient in scenarios with uncertainties; however, they also introduce another concern: nondeterminism. This research endeavors to find a solution on how such algorithms can be utilized with higher reliability. Our method is based on using an adaptive model to verify the performance of a control parameter - proposed by a nondeterministic adaptive controller or AI-based optimizer - before it is deployed on the physical platform. Furthermore, a backup mechanism is engaged to recover the drone in case of failure. A Neural Network is employed to model the aircraft, and a Genetic Algorithm is utilized to optimize the PID controller of a quadcopter. The initial experimental results from test flights indicate the feasibility of this method.","PeriodicalId":340472,"journal":{"name":"2016 IEEE/AIAA 35th Digital Avionics Systems Conference (DASC)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121661218","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-09-01DOI: 10.1109/DASC.2016.7778056
T. Driessen, B. Bauer
By now, Model-Driven Development is a well-known approach in many domains. By (re)using standardized domain-specific models, productivity is increased and common errors are simultanously avoided. The Architecture Analysis and Design Language is a domain-specific modeling language for embedded, real-time and safety-critical systems. In our approach we utilize this modeling language, with its well-defined semantics, as source language for a mapping into real-time Java. The chosen subset of model elements enables system designers to create a system and subsequently generate a code framework that complies to the model in terms of structure, timing and communicational restrictions. In order to demonstrate the benefits of our approach, we model and generate the code framework for an existing autopilot and compare our results with the original software.
{"title":"Shifting temporal and communicational aspects into design phase via AADL and RTSJ","authors":"T. Driessen, B. Bauer","doi":"10.1109/DASC.2016.7778056","DOIUrl":"https://doi.org/10.1109/DASC.2016.7778056","url":null,"abstract":"By now, Model-Driven Development is a well-known approach in many domains. By (re)using standardized domain-specific models, productivity is increased and common errors are simultanously avoided. The Architecture Analysis and Design Language is a domain-specific modeling language for embedded, real-time and safety-critical systems. In our approach we utilize this modeling language, with its well-defined semantics, as source language for a mapping into real-time Java. The chosen subset of model elements enables system designers to create a system and subsequently generate a code framework that complies to the model in terms of structure, timing and communicational restrictions. In order to demonstrate the benefits of our approach, we model and generate the code framework for an existing autopilot and compare our results with the original software.","PeriodicalId":340472,"journal":{"name":"2016 IEEE/AIAA 35th Digital Avionics Systems Conference (DASC)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122734823","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-09-01DOI: 10.1109/DASC.2016.7777997
Soyeon Jung, Keumjin Lee
Sequencing arrival flights is a major task of air traffic management, and there exist various optimization tools to support the air traffic controllers. It is, however, difficult to employ these tools in the actual operational environments since they lack consideration on the human cognitive process. This paper proposes a new framework to predict the arrival sequences based on a preference learning approach, where we learn the sequence data operated by human controllers. The proposed algorithm works in two-stages: it first learns the pairwise preference functions between arrivals using binomial logistic regression, and then it induces the total sequence for a new set of arrivals by comparing the scores of each aircraft, which are the sums of pairwise preference probabilities. The proposed model is demonstrated with real traffic data at Incheon International Airport and its performance is assessed using the Spearman's rank correlation.
{"title":"Probabilistic prediction model of air traffic controllers' sequencing strategy based on pairwise comparisons","authors":"Soyeon Jung, Keumjin Lee","doi":"10.1109/DASC.2016.7777997","DOIUrl":"https://doi.org/10.1109/DASC.2016.7777997","url":null,"abstract":"Sequencing arrival flights is a major task of air traffic management, and there exist various optimization tools to support the air traffic controllers. It is, however, difficult to employ these tools in the actual operational environments since they lack consideration on the human cognitive process. This paper proposes a new framework to predict the arrival sequences based on a preference learning approach, where we learn the sequence data operated by human controllers. The proposed algorithm works in two-stages: it first learns the pairwise preference functions between arrivals using binomial logistic regression, and then it induces the total sequence for a new set of arrivals by comparing the scores of each aircraft, which are the sums of pairwise preference probabilities. The proposed model is demonstrated with real traffic data at Incheon International Airport and its performance is assessed using the Spearman's rank correlation.","PeriodicalId":340472,"journal":{"name":"2016 IEEE/AIAA 35th Digital Avionics Systems Conference (DASC)","volume":"158 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122980073","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-09-01DOI: 10.1109/DASC.2016.7777963
Ryan Gardner, D. Genin, Raymond McDowell, C. Rouff, Anshu Saksena, Aurora C. Schmidt
We present a probabilistic model checking approach for evaluating the safety and operational suitability of the Airborne Collision Avoidance System X (ACAS X). This system issues advisories to pilots when the risk of mid-air collision is imminent, and is expected to be equipped on all large, piloted aircraft in the future. We developed an approach to efficiently compute the probabilities of generically specified events and the most likely sequences of states leading to those events within a discrete-time Markov chain model of aircraft flight and ACAS X. The probabilities and sequences are computed for all states in the model. Events of interest include near mid-air collisions (NMACs) and undesirable sequences of advisories that affect operational suitability. We have validated numerous observations of the model with higher-fidelity simulations of the full system. This analysis has revealed several characteristics of ACAS X's behavior.
{"title":"Probabilistic model checking of the next-generation airborne collision avoidance system","authors":"Ryan Gardner, D. Genin, Raymond McDowell, C. Rouff, Anshu Saksena, Aurora C. Schmidt","doi":"10.1109/DASC.2016.7777963","DOIUrl":"https://doi.org/10.1109/DASC.2016.7777963","url":null,"abstract":"We present a probabilistic model checking approach for evaluating the safety and operational suitability of the Airborne Collision Avoidance System X (ACAS X). This system issues advisories to pilots when the risk of mid-air collision is imminent, and is expected to be equipped on all large, piloted aircraft in the future. We developed an approach to efficiently compute the probabilities of generically specified events and the most likely sequences of states leading to those events within a discrete-time Markov chain model of aircraft flight and ACAS X. The probabilities and sequences are computed for all states in the model. Events of interest include near mid-air collisions (NMACs) and undesirable sequences of advisories that affect operational suitability. We have validated numerous observations of the model with higher-fidelity simulations of the full system. This analysis has revealed several characteristics of ACAS X's behavior.","PeriodicalId":340472,"journal":{"name":"2016 IEEE/AIAA 35th Digital Avionics Systems Conference (DASC)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129380046","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-09-01DOI: 10.1109/DASC.2016.7777987
Sarah D'Souza, A. Ishihara, Ben E. Nikaido, Hashmatullah Hasseeb
Managing trajectory separation of unmanned aircraft is critical to ensuring accessibility, efficiency, and safety in low altitude airspace. The concept of a geo-fence has emerged as a way to manage trajectory separation. A geo-fence consists of distance buffers that enclose individual trajectories to identify a `keep-in' region and/or enclose areas that identify `keep-out' regions. The `keep-in' geo-fence size can be defined as a static number or calculated as a function of vehicle performance characteristics, state of the airspace, weather, and other unforeseen events such as emergency or disaster response. Given that the fleet of Unmanned Aircraft Systems (UAS) operating in low altitude airspace will be numerous and non-homogeneous, calculating a `keep-in' geo-fence will need to balance operational safety and efficiency. A recently tested UAS Traffic Management (UTM) prototype used a geo-fence size of 30 meters, horizontally and vertically, for every operation submitted. The goal of this work is to determine the feasibility of a generalized, simple algorithm that calculates geo-fence sizes as a function of vehicle performance and potential wind disturbances. The resulting geo-fence size could be smaller or larger because the vehicle performance in the presence of wind is considered, thus leading to trajectory separation that is safe and efficient. In this paper, two simplified methods were developed to determine the feasibility of calculating a geo-fence as a function of vehicle parameters and wind information. The first method calculates the geo-fence using basic vehicle parameters and wind sensor data in a set of algebraic-geometric equations. The second method models a generic PID control system that uses a simplified set of equations of motion for the plant and uses gain scheduling to account for wind disturbances. It was found that the Algebraic-Geometric Geo-fence Algorithm provides geo-fence sizes of approximately 15 meters horizontally and 5 meters vertically, which is much smaller than the UTM static value of 30 meters. In the PID Controller Geo-fence Algorithm it was found that the geo-fence size is further reduced to less than 5 meters, horizontally and vertically. These results reveal that implementing geo-fence calculations provide UTM with the ability to schedule and separate operations based on geofences that are dynamic to vehicle capability and environment, which is more efficient than using a single static geo-fence.
{"title":"Feasibility of varying geo-fence around an unmanned aircraft operation based on vehicle performance and wind","authors":"Sarah D'Souza, A. Ishihara, Ben E. Nikaido, Hashmatullah Hasseeb","doi":"10.1109/DASC.2016.7777987","DOIUrl":"https://doi.org/10.1109/DASC.2016.7777987","url":null,"abstract":"Managing trajectory separation of unmanned aircraft is critical to ensuring accessibility, efficiency, and safety in low altitude airspace. The concept of a geo-fence has emerged as a way to manage trajectory separation. A geo-fence consists of distance buffers that enclose individual trajectories to identify a `keep-in' region and/or enclose areas that identify `keep-out' regions. The `keep-in' geo-fence size can be defined as a static number or calculated as a function of vehicle performance characteristics, state of the airspace, weather, and other unforeseen events such as emergency or disaster response. Given that the fleet of Unmanned Aircraft Systems (UAS) operating in low altitude airspace will be numerous and non-homogeneous, calculating a `keep-in' geo-fence will need to balance operational safety and efficiency. A recently tested UAS Traffic Management (UTM) prototype used a geo-fence size of 30 meters, horizontally and vertically, for every operation submitted. The goal of this work is to determine the feasibility of a generalized, simple algorithm that calculates geo-fence sizes as a function of vehicle performance and potential wind disturbances. The resulting geo-fence size could be smaller or larger because the vehicle performance in the presence of wind is considered, thus leading to trajectory separation that is safe and efficient. In this paper, two simplified methods were developed to determine the feasibility of calculating a geo-fence as a function of vehicle parameters and wind information. The first method calculates the geo-fence using basic vehicle parameters and wind sensor data in a set of algebraic-geometric equations. The second method models a generic PID control system that uses a simplified set of equations of motion for the plant and uses gain scheduling to account for wind disturbances. It was found that the Algebraic-Geometric Geo-fence Algorithm provides geo-fence sizes of approximately 15 meters horizontally and 5 meters vertically, which is much smaller than the UTM static value of 30 meters. In the PID Controller Geo-fence Algorithm it was found that the geo-fence size is further reduced to less than 5 meters, horizontally and vertically. These results reveal that implementing geo-fence calculations provide UTM with the ability to schedule and separate operations based on geofences that are dynamic to vehicle capability and environment, which is more efficient than using a single static geo-fence.","PeriodicalId":340472,"journal":{"name":"2016 IEEE/AIAA 35th Digital Avionics Systems Conference (DASC)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115426085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-09-01DOI: 10.1109/DASC.2016.7778094
T. Etherington, L. Kramer, R. Bailey, Kellie D. Kennedy, C. Stephens
Accident statistics cite the flight crew as a causal factor in over 60% of accidents involving transport category airplanes. Yet, a well-trained and well-qualified pilot is acknowledged as the critical center point of aircraft systems safety and an integral safety component of the entire commercial aviation system. No data currently exists that quantifies the contribution of the flight crew in this role. Neither does data exist for how often the flight crew handles non-normal procedures or system failures on a daily basis in the National Airspace System. A pilot-in-the-loop high fidelity motion simulation study was conducted by the NASA Langley Research Center in partnership with the Federal Aviation Administration (FAA) to evaluate the pilot's contribution to flight safety during normal flight and in response to aircraft system failures. Eighteen crews flew various normal and non-normal procedures over a two-day period and their actions were recorded in response to failures. To quantify the human's contribution, crew complement was used as the experiment independent variable in a between-subjects design. Pilot actions and performance when one of the flight crew was impaired were also recorded for comparison against the nominal two-crew operations. This paper details a portion of the results of this study.
{"title":"Quantifying pilot contribution to flight safety for normal and non-normal airline operations","authors":"T. Etherington, L. Kramer, R. Bailey, Kellie D. Kennedy, C. Stephens","doi":"10.1109/DASC.2016.7778094","DOIUrl":"https://doi.org/10.1109/DASC.2016.7778094","url":null,"abstract":"Accident statistics cite the flight crew as a causal factor in over 60% of accidents involving transport category airplanes. Yet, a well-trained and well-qualified pilot is acknowledged as the critical center point of aircraft systems safety and an integral safety component of the entire commercial aviation system. No data currently exists that quantifies the contribution of the flight crew in this role. Neither does data exist for how often the flight crew handles non-normal procedures or system failures on a daily basis in the National Airspace System. A pilot-in-the-loop high fidelity motion simulation study was conducted by the NASA Langley Research Center in partnership with the Federal Aviation Administration (FAA) to evaluate the pilot's contribution to flight safety during normal flight and in response to aircraft system failures. Eighteen crews flew various normal and non-normal procedures over a two-day period and their actions were recorded in response to failures. To quantify the human's contribution, crew complement was used as the experiment independent variable in a between-subjects design. Pilot actions and performance when one of the flight crew was impaired were also recorded for comparison against the nominal two-crew operations. This paper details a portion of the results of this study.","PeriodicalId":340472,"journal":{"name":"2016 IEEE/AIAA 35th Digital Avionics Systems Conference (DASC)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114174664","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-09-01DOI: 10.1109/DASC.2016.7778031
S. Cheon, S. Ha, Y. Moon
In this paper, a hardware-in-the loop simulation (HILS) platform is presented for verifying the image-based object tracking method adopted in the small Unmanned Aerial Vehicle (sUAV). The platform is constructed by image processing module, scene generation module, and flight control module. In the image processing module, the motion of target object is measured by using the speeded-up robust features (SURF) algorithm and the feature matching technique. And then, control command is provided to allow the target object to be tracked by sUAV automatically. The JMAVSIM software developed by PX4 dev-team is used in the proposed platform to simulate the flight of sUAV and provide virtual scene and flight data. Pixhawk based on PX4 firmware which is a popular flight control computer is used as flight control module in the proposed platform. Experimental results show that the object tracking method based on sUAV is effectively tested and evaluated in the proposed HILS platform.
{"title":"Hardware-in-the-loop simulation platform for image-based object tracking method using small UAV","authors":"S. Cheon, S. Ha, Y. Moon","doi":"10.1109/DASC.2016.7778031","DOIUrl":"https://doi.org/10.1109/DASC.2016.7778031","url":null,"abstract":"In this paper, a hardware-in-the loop simulation (HILS) platform is presented for verifying the image-based object tracking method adopted in the small Unmanned Aerial Vehicle (sUAV). The platform is constructed by image processing module, scene generation module, and flight control module. In the image processing module, the motion of target object is measured by using the speeded-up robust features (SURF) algorithm and the feature matching technique. And then, control command is provided to allow the target object to be tracked by sUAV automatically. The JMAVSIM software developed by PX4 dev-team is used in the proposed platform to simulate the flight of sUAV and provide virtual scene and flight data. Pixhawk based on PX4 firmware which is a popular flight control computer is used as flight control module in the proposed platform. Experimental results show that the object tracking method based on sUAV is effectively tested and evaluated in the proposed HILS platform.","PeriodicalId":340472,"journal":{"name":"2016 IEEE/AIAA 35th Digital Avionics Systems Conference (DASC)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116280693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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