Intelligent Unmanned Air Vehicle Flight Systems

Miller, Jodi A.; Minear, Paul D.; Niessner, Albert F.; DeLullo, Anthony M.; Geiger, Brian R.; Long, Lyle N.; Horn, Joseph F.

doi:10.2514/1.26553

Cited by 25 publications

(19 citation statements)

References 9 publications

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“…It is easily expandable to incorporate new functionality as desired. Using the UAV flight system developed by researchers at both ARL/PSU and the Penn State Department of Aerospace Engineering [13], successful flight tests have been conducted demonstrating current UAV IC capabilities.…”

Section: Discussionmentioning

confidence: 99%

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An Intelligent Controller for Collaborative Unmanned Air Vehicles

Sinsley¹,

Miller²,

Long³

et al. 2007

2007 IEEE Symposium on Computational Intelligence in Security and Defense Applications

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Section: Discussionmentioning

confidence: 99%

“…The IC is responsible for high level mission planning and coordination among UAVs. For complete details on our UAV testbed see [13]. …”

Section: Introductionmentioning

confidence: 99%

An Intelligent Controller for Collaborative Unmanned Air Vehicles

Sinsley¹,

Miller²,

Long³

et al. 2007

2007 IEEE Symposium on Computational Intelligence in Security and Defense Applications

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“…All image processing is done in real time using the onboard webcam and the onboard processor. More details on the UAV platform can be found in Miller et al 18 and in Sinsley et al …”

Section: Via Platformmentioning

confidence: 99%

“…2,18 The vision and estimation systems have been tested using hardware-in-the-loop simulations, and have been run in real time onboard the aircraft with a human pilot in the loop. The complete system, including the path planning program, has been tested in hardware-in-the-loop.…”

Section: Testingmentioning

confidence: 99%

Vision-Based Target Geolocation and Optimal Surveillance on an Unmanned Aerial Vehicle

Ross

Geiger

Sinsley

et al. 2008

AIAA Guidance, Navigation and Control Conference and Exhibit

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A real-time computer vision algorithm for the identification and geolocation of ground targets was developed and implemented on the Penn State University / Applied Research Laboratory Unmanned Aerial Vehicle (PSU/ARL UAV) system. The geolocation data is filtered using a linear Kalman filter, which provides a smoothed estimate of target location and target velocity. The vision processing routine and estimator are coupled with an onboard path planning algorithm that optimizes the vehicle trajectory to maximize surveillance coverage of the targets.The vision processing and estimation routines were flight tested onboard a UAV system with a human pilot-in-the-loop. It was found that GPS latency had a significant effect on the geolocation error, and performance was significantly improved when using latency compensation. The combined target geolocation and path planning system was tested on the ground using a hardware-in-the-loop simulation, and resulted successful tracking and observation of a fixed target. Timing results showed that is is feasible to implement total system in real time.

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“…The UAVs for sensing and decision making research often requires a higher level of customization for the avionic system. This can be achieved through either augmenting a COTS autopilot with a dedicated payload computer (Miller et al, 2005), or by having an entirely specialized avionics design (Evans et al, 2001). An alternative approach for smaller UAVs is to instrument an indoor testing environment (How et al, 2008) for measuring aircraft states so that a less complex avionic system could be used on-board the aircraft.…”

Section: Introductionmentioning

confidence: 99%