Factors That Influence Pilot Task Demand Load During Area Navigation Approaches

Heiligers, Monique; Holten, Theo van; Mulder, Max

doi:10.2514/1.c031188

Cited by 5 publications

(24 citation statements)

References 7 publications

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“…It can be seen that both plots compare very well and that modeling the pilot actions according to trigger events and reaction times [4] actually works. Figure 10 shows the predictions of the offline computer simulation with respect to meeting the constraints at all waypoints.…”

Section: B Validation Of Offline Computer Simulationmentioning

confidence: 69%

“…For an analysis of these flight data, see the Appendix. For a more detailed explanation of the basic principles of the offline computer simulation, the reader is referred to [4].…”

Section: A Offline Computer Simulationmentioning

confidence: 99%

“…2) instead of on the constraints of the pilot himself (like memory capacity, time delay, etc.) [4]. In this respect, our work is influenced by the principles of cognitive work analysis [5].…”

Section: Introductionmentioning

confidence: 99%

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Seven Guidlines for Limiting Pilot Task Demand Load During Area Navigation Approaches

Heiligers

Holten

Mulder

2011

Journal of Aircraft

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The goal of this research is to develop a method that can give an indication of pilot task demand load during approaches. This paper presents the results of a flight simulator experiment that aimed to identify the factors that influence pilot task demand load during an approach with a Boeing 747. Based on the results of this experiment, seven guidelines for the design of approaches are presented. When these guidelines are adhered to, pilot task demand load during the approach will be acceptable. Additionally, a nonlinear Monte Carlo computer simulation, as well as a computer simulation based on a point mass model, is presented. Both computer simulations can predict, for a given approach, whether the seven guidelines with respect to pilot task demand load are adhered to.

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Section: B Validation Of Offline Computer Simulationmentioning

confidence: 69%

“…For an analysis of these flight data, see the Appendix. For a more detailed explanation of the basic principles of the offline computer simulation, the reader is referred to [4].…”

Section: A Offline Computer Simulationmentioning

confidence: 99%

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Seven Guidlines for Limiting Pilot Task Demand Load During Area Navigation Approaches

Heiligers

Holten

Mulder

2011

Journal of Aircraft

Self Cite

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“…This approach deliberately deviates from the idea behind models such as the Procedure-Oriented Crew model (PROCRU) (5,6) or the Man-Machine Integrated Design and Analysis System (MIDAS) (7-9) that use human operator models which do focus on the constraints of the human operator. It is anticipated that by focusing on the environment of the pilot instead of on the limitations of the pilot himself much simpler models can be achieved to predict pilot TDL than by using human operator models.During previous research (10)(11)(12)(13)(14) , factors have been identified that influence pilot TDL for pilots flying an RNAV approach with a B747. These factors were identified based on flight simulator tests.…”

mentioning

confidence: 99%

“…During previous research (10)(11)(12)(13)(14) , factors have been identified that influence pilot TDL for pilots flying an RNAV approach with a B747. These factors were identified based on flight simulator tests.…”

mentioning

confidence: 99%

Pilot task demand load during RNAV approaches with a Cessna Citation

et al. 2011

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influenced by the principles of cognitive work analysis (4) . This approach deliberately deviates from the idea behind models such as the Procedure-Oriented Crew model (PROCRU) (5,6) or the Man-Machine Integrated Design and Analysis System (MIDAS) (7-9) that use human operator models which do focus on the constraints of the human operator. It is anticipated that by focusing on the environment of the pilot instead of on the limitations of the pilot himself much simpler models can be achieved to predict pilot TDL than by using human operator models.During previous research (10)(11)(12)(13)(14) , factors have been identified that influence pilot TDL for pilots flying an RNAV approach with a B747. These factors were identified based on flight simulator tests. In this paper it will be demonstrated that the same factors also influence pilot TDL when flying an approach with a Cessna Citation. This indicates that the set of factors that has been identified is a generally applicable set of factors, and not only valid for the B747. Additionally, it is investigated whether the same set of factors influences pilot TDL during flight simulator tests and during real flight. To this end, in this paper, both the results of a flight simulator experiment for a Cessna Citation and the results of real flight tests with a Cessna Citation aircraft are compared. It will be demonstrated that the same set of factors results from both experiments. Finally, the simulation tool that was developed for the B747 in order to analyse an approach with respect to the factors that were proven to influence pilot TDL is adjusted in order to include the Cessna Citation. It will be shown that the simulation tool also works and provides reliable results for the Cessna Citation.This paper is structured as follows. First, the basic principles of this research are introduced as well as the scope of the research. After that, the results of previous research (11)(12)(13) which focused on the B747 are briefly explained. Subsequently, the human in the loop experiments are presented, these experiments are conducted for the Cessna Citation aircraft both in a flight simulator and during real flight tests. To conclude, the simulation tool that is adjusted to include the Cessna Citation is explained, and its predictions are illustrated by a case study.

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Vision-based navigation using multi-rate feedback from optic flow and scene reconstruction

Arvai¹,

Kehoe²,

Lind

2011

Aeronaut. j.

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difficult for a human operator to observe and command. Some missions will require rapid decision making based on more targets than a human can process. Finally, missions may utilise swarms of UAV systems for which the workload would be too immense for a human to control.Vision-based feedback can be a critical component to autonomy. Inertial sensors, such as gyros and accelerometers, provide information about the state of the vehicle; however, they do not provide any information about the environment. Vision can be used to extract information, such as location of obstacles, about the flight space. As such, vision-based feedback provides a mechanism for autonomous navigation to maneuver through unknown or uncertain environments. Even urban environments can be considered given that appropriately sized vehicles, having wingspan less than 2ft and turn radius less than 20ft, can respond quickly to information obtained from cameras which often have field of view between 70 degrees and 140 degrees.The concept of visual-servo, or vision-based, control is appropriate for tasks such as guidance and navigation. Researchers in robotics have been particularly active in this area along with more recent applications in aerospace and manufacturing. Most techniques share some commonality; namely, a sequence of image processing and vision processing are performed to extract information which is then analysed to make an optimal control decision. The basic unit of information from an image is a feature point which indicates some pixel of particular interest due to, for example, colour or intensity gradient near that pixel. Among the techniques that utilise feature ABSTRACT Vision-based control is being aggressively pursued for autonomous systems. Such control is particularly valuable for path planning to achieve mission objectives like target tracking and obstacle avoidance. This paper presents a multi-rate strategy that utilises a fast-rate optic flow approach and a slow-rate scene reconstruction approach. The vehicle uses scene reconstruction to generate an accurate map for path planning; however, optic flow is used to avoid obstacles while the scene reconstruction is computed. A switch element is used in the feedback path to determine whether information relating to the reconstructed map or the optical flow should be used for navigation. The resulting controller is able to generate flight trajectories and perform obstacle avoidance within a computational cost which is reasonable given performance demands and computational resources on a wide range of aircraft. A simulation demonstrates the performance of an aircraft that uses the multi-rate controller to avoid an obstacle which is only observed after a turn. Essentially, the fast-rate optic flow indicates the presence of the obstacle during the time that slow-rate scene reconstruction is being performed. The resulting flight path is able to follow mission objectives and avoid a collision.

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Factors That Influence Pilot Task Demand Load During Area Navigation Approaches

Cited by 5 publications

References 7 publications

Seven Guidlines for Limiting Pilot Task Demand Load During Area Navigation Approaches

Seven Guidlines for Limiting Pilot Task Demand Load During Area Navigation Approaches

Pilot task demand load during RNAV approaches with a Cessna Citation

Vision-based navigation using multi-rate feedback from optic flow and scene reconstruction

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