Planning sensing actions for UAVs in urban domains

Peot, Mark A.; Altshuler, Thomas W.; Breiholz, Arlen; Bueker, Richard A.; Fertig, Kenneth W.; Hawkins, Aaron T.; Reddy, Sudhakar Y.

doi:10.1117/12.634899

Cited by 13 publications

(6 citation statements)

References 14 publications

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“…These applications range from border patrol [1] and mapping buildings and facilities [2] to forest fire monitoring [3]. Flight control is one of the main differentiation factors between manned and unmanned aircraft.…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis and recovery from structural failures (icing) in unmanned aerial vehicles

Tousi

Khorasani

2009

2009 3rd Annual IEEE Systems Conference

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This paper tackles the problem of fault diagnosis and recovery of unmanned arial vehicles (UAVs) resulting from structural failures (icing). The proposed system consists of two units one for fault diagnosis and another for fault recovery. The goal of the fault diagnosis unit is to detect and estimate the severity of a fault. The recovery unit utilizes information on the estimated fault and adjusts the controller parameters to recover the system from the faulty condition. This methodology is useful mostly for small UAVs, where the available methods for structural fault detection and recovery are too expensive to be applied. Icing problem, which is a well-known structural problem in aerospace industry is considered as a demonstration of this method. Our proposed approach takes advantage of control theory to detect and recover faults within the flight control unit. Simulation results are provided to demonstrate the effectiveness and practical use of our proposed approach.

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Section: Introductionmentioning

confidence: 99%

Fault diagnosis and recovery from structural failures (icing) in unmanned aerial vehicles

Tousi

Khorasani

2009

2009 3rd Annual IEEE Systems Conference

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“…Areas of interest include cooperative control of a team of UAVs conducting operations such as border patrol [1], forest fire monitoring [2], and mapping buildings and facilities [3]. A cooperative decision and control theoretic framework is of particular interest to address robust dynamic control of distributed UAVs executing multiple, strongly coupled tasks with a high degree of decentralization [4].…”

Section: Introductionmentioning

confidence: 99%

A hybrid fault diagnosis and recovery for a team of unmanned vehicles

Tousi

Aghdam

Khorasani

2008

2008 IEEE International Conference on System of Systems Engineering

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In this paper, a hybrid fault detection, isolation, and recovery (FDIR) methodology is developed for a team of unmanned vehicles which takes advantage of the cooperative nature of the system to accomplish the desired mission in presence of failures. The proposed methodology is hybrid and consists of a low level (agent level) and a high level (team level) FDIR. The high level FDIR is formulated in the discrete-event system (DES) supervisory control framework, whereas the low level FDIR uses the classical control techniques. By properly integrating the two FDIR components, a larger class of faults can be detected and isolated when compared to results in the literature. A reconfiguration strategy is also designed so that the team is recovered from faults. Simulation results are provided to elucidate the efficacy of the proposed approach.

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“…Such research has been motivated by numerous potential applications such as reconnaissance and surveillance (Hegazy et al [1]), human risk-reduction and military operations (Mullens et al [2] and Peot et al [3]) and law enforcement (Murphy and Cycon [4]). With the low weight of such aircraft, acceptable flight performance is a concern due to the turbulent wind generated around urban structures.…”

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confidence: 99%

Modeling of Urban Wind Field Effects on Unmanned Rotorcraft Flight

Galway

Etele

Fusina³

2011

Journal of Aircraft

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Surveillance applications of unmanned aerial vehicles within urban areas is made difficult by turbulent winds generated by buildings. A methodology is proposed by which urban wind data is evaluated, selected, and applied during flight simulation. The urban environment is represented by a combination of discrete single buildings and canyons each easily amenable to computational fluid dynamics. These simulations are used to generate a database of building wakes for various wind conditions and building configurations typical to the North American urban environment. A selection algorithm is used that determines if the current aircraft position is influenced by the building wake and, if so, calculates the resulting effect on the aircraft. Results are presented for a Yamaha R-50 R/C helicopter in both forward and vertical flight. Variations in aircraft attitude by as much as 10 are observed when considering building wake effects generated by a 8 kt wind. It is also demonstrated that this approach is able to identify locations within a given wake at which aircraft flight is most affected. Nomenclature

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Planning sensing actions for UAVs in urban domains

Cited by 13 publications

References 14 publications

Fault diagnosis and recovery from structural failures (icing) in unmanned aerial vehicles

Fault diagnosis and recovery from structural failures (icing) in unmanned aerial vehicles

A hybrid fault diagnosis and recovery for a team of unmanned vehicles

Modeling of Urban Wind Field Effects on Unmanned Rotorcraft Flight

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