Partial camera automation in an unmanned air vehicle

Korteling, J.E.; Borg, W. van der

doi:10.1109/3468.554688

Cited by 9 publications

(4 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Lefas [21] proposed a ground-based wind estimation algorithm from UAV transponder downlinked airspeed and heading data. Korteling and Borg [22] proposed an intelligent, semiautonomous interface for a payload operator, in which the payload joystick movement is synchronized with onboard payload movement.…”

Section: Level 1 -Remotely Guided Vehiclementioning

confidence: 99%

Role of information and communication in redefining unmanned aerial vehicle autonomous control levels

Suresh

Ghose

2010

Proceedings of the Institution of Mechanical Engineers, Part G:

View full text Add to dashboard Cite

Autonomous mission control, unlike automatic mission control which is generally pre-programmed to execute an intended mission, is guided by the philosophy of carrying out a complete mission on its own through online sensing, information processing, and control reconfiguration. A crucial cornerstone of this philosophy is the capability of intelligence and of information sharing between unmanned aerial vehicles (UAVs) or with a central controller through secured communication links. Though several mission control algorithms, for single and multiple UAVs, have been discussed in the literature, they lack a clear definition of the various autonomous mission control levels. In the conventional system, the ground pilot issues the flight and mission control command to a UAV through a command data link and the UAV transmits intelligence information, back to the ground pilot through a communication link. Thus, the success of the mission depends entirely on the information flow through a secured communication link between ground pilot and the UAV. In the past, mission success depended on the continuous interaction of ground pilot with a single UAV, while present day applications are attempting to define mission success through efficient interaction of ground pilot with multiple UAVs. However, the current trend in UAV applications is expected to lead to a futuristic scenario where mission success would depend only on interaction among UAV groups with no interaction with any ground entity. However, to reach this capability level, it is necessary to first understand the various levels of autonomy and the crucial role that information and communication plays in making these autonomy levels possible. This article presents a detailed framework of UAV autonomous mission control levels in the context of information flow and communication between UAVs and UAV groups for each level of autonomy.

show abstract

Section: Level 1 -Remotely Guided Vehiclementioning

confidence: 99%

Role of information and communication in redefining unmanned aerial vehicle autonomous control levels

Suresh

Ghose

2010

Proceedings of the Institution of Mechanical Engineers, Part G:

View full text Add to dashboard Cite

show abstract

“…We assume that the task intelligence cost and interface complexity are highly correlated with the mental workload since we can say that the higher degree of task intelligence cost or interface complexity takes more human information processing capacity. Many techniques to assess mental workload have been developed and some of them have been used for the practical estimation of designing aircraft cockpits [19], [20], main control boards [21], and user interface [22]. The six methods to measure mental workload are as follows: 1) parameters from behavioral signals; 2) dual task methods; 3) information measures; 4) eye scanning movements; 5) subjective measurement; 6) physiological variables.…”

Section: Procedures Of Miq Measurementmentioning

confidence: 99%

Measuring the machine intelligence quotient (MIQ) of human-machine cooperative systems

Park

Kim

Lim³

2001

IEEE Trans. Syst., Man, Cybern. A

View full text Add to dashboard Cite

“…The added kinesthetic information improves vehicle handling, improves lane following both in compensatory and in pursuit control, and reduces the mental and physical load of the driver. A force feedback joystick was used to give information about the motion of an unmanned air vehicle (UAV) to its pilot operating from a remote site in the work presented by Korteling and Borg [7]. The force-feedback system improved the performance of the pilot and reduced the mental workload associated with maneuvering a simulated UAV.…”

Section: Introductionmentioning

confidence: 99%

An Investigation of Vibration Feedthrough and Feedthrough Cancellation in Joystick Controlled Vehicles

́nyi

Gillespie

2003

Dynamic Systems and Control, Volumes 1 and 2

View full text Add to dashboard Cite

The inertial and applied forces acting on operators of joystick controlled machinery in moving vehicles can produce unintentional control signals through the joystick. These forces tend to deteriorate continuous tracking performance and further, when the machinery in control is the vehicle itself, they may lead to unstable oscillations that jeopardize that vehicle’s safe operation. In this paper, we propose the use of a force-reflecting joystick and a model-based controller to cancel the effects of inertia forces. Using a simple physical model of human biomechanics, we experimentally investigate the effectiveness of a cancellation controller in stabilizing a driving task. A second experiment involving a human subject on board a motion base investigates the ability of the cancellation controller to improve performance in a continuous tracking task. Results indicate that the cancellation controller enhances stability and improves tracking.

show abstract

Partial camera automation in an unmanned air vehicle

Cited by 9 publications

References 4 publications

Role of information and communication in redefining unmanned aerial vehicle autonomous control levels

Role of information and communication in redefining unmanned aerial vehicle autonomous control levels

Measuring the machine intelligence quotient (MIQ) of human-machine cooperative systems

An Investigation of Vibration Feedthrough and Feedthrough Cancellation in Joystick Controlled Vehicles

Contact Info

Product

Resources

About