Designing Unmanned Aircraft Systems

Gundlach, Jay

doi:10.2514/4.868443

Cited by 135 publications

(10 citation statements)

References 12 publications

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“…where V f uel is the net volume of fuel consumed (expressed in gallons). The performance of the UAV is estimated using a series of analytical and empirical expressions [35,36]. The detailed formulation of the UAV performance and constraints is however not within the scope of this paper, and is in part proprietary [33].…”

Section: Twin-boom Uav Designmentioning

confidence: 99%

Comprehensive Product Platform Planning (CP3) for a Modular Family of Unmanned Aerial Vehicles

Chowdhury

Maldonado

Tong

et al. 2013

Volume 3B: 39th Design Automation Conference

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The development of products with a modular structure, where the constituent modules could be derived from a set of common platforms to suit different market niches, provides unique engineering and economic advantages. However, the quantitative design of such modular product platforms could become significantly challenging for complex products. The Comprehensive Product Platform Planning (CP 3 ) method facilitates effective design of such product platforms. The original CP 3 method is however typically suitable for scale-based product family design. In this paper, we perform important modifications to the commonality matrix and the commonality constraint formulation in CP 3 to advance its applicability to modular product family design. A commonality index (CI), defined in terms of the number of unique modules in a family, is used to quantify the commonality objective. The new CP 3 method is applied to design a family of reconfigurable Unmanned Aerial Vehicles (UAVs) for civilian applications. CP 3 enables the design of an optimum set of distinct modules, different groups of which could be assembled to configure twin-boom UAVs that provide three different combinations of payload capacity and endurance. The six key modules that participate in the platform planning are: (i) the fuselage/pod, (ii) the wing, (iii) the booms, (iv) the vertical tails, (v) the horizontal tail, and (vi) the fuel tank. The performance of each UAV is defined in terms of its range per unit fuel consumption. Among the best tradeoff UAV families obtained by mixed-discrete Particle Swarm Optimization, the family with the maximum commonality (CI=0.5) required a 66% compromise of the UAVs' range/fuel-consumption performance. The platform configuration corresponding to the maximumcommonality UAV family involved sharing of the horizontal tail and fuel tank among all three UAVs and sharing of the fuselage and booms among two UAVs.

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Section: Twin-boom Uav Designmentioning

confidence: 99%

Comprehensive Product Platform Planning (CP3) for a Modular Family of Unmanned Aerial Vehicles

Chowdhury

Maldonado

Tong

et al. 2013

Volume 3B: 39th Design Automation Conference

View full text Add to dashboard Cite

show abstract

“…Unmanned aerial systems (UAS) can be traced back at least as early as 1896 with Samuel Langley's unmanned 'aerodome' which flew for almost a mile over the Potomac River [1]. Since then, UAS' have made significant progress and are now a rapidly expanding area of technological research and development.…”

Section: Introductionmentioning

confidence: 99%

System and mathematical modeling of quadrotor dynamics

et al. 2015

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Unmanned aerial systems (UAS) are becoming increasingly visible in our daily lives; and range in operation from search and rescue, monitoring hazardous environments, and to the delivery of goods. One of the most popular UAS are based on a quad-rotor design. These are typically small devices that rely on four propellers for lift and movement. Quad-rotors are inherently unstable, and rely on advanced control methodologies to keep them operating safely and behaving in a predictable and desirable manner. The control of these devices can be enhanced and improved by making use of an accurate dynamic model. In this paper, we examine a simple quadrotor model, and note some of the additional dynamic considerations that were left out. We then compare simulation results of the simple model with that of another comprehensive model.

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“…Lithium-based batteries present advantageous characteristics, such as high charge and discharge rates, longevity, high energy density, and affordable cost accompanied with a lightweight structure [1,2,3,4]. Nonetheless, only small improvements in lithium-based batteries have occurred over the past ten years with the biggest improvement being the reduction of price.…”

Section: A Brief Introductionmentioning

confidence: 99%

LiPo battery energy studies for improved flight performance of unmanned aerial systems

et al. 2016

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Energy storage is one of the most important determinants of how long and far a small electric powered unmanned aerial system (UAS) can fly. For years, most hobby and experimentalists used heavy fuels to power small drone-like systems. Electric motors and battery storage prior to the turn of the century were either too heavy or too inefficient for flight times of any usable duration. However, with the availability of brushless electric motors and lithium-based batteries everything has changed. Systems like the Dragon Eye, Pointer, and Raven are in service performing reconnaissance, intelligence, surveillance, and target acquisition (RISTA) for more than an hour at a time. More recently, multi-rotor vehicles have expanded small UAS capabilities to include activities with hovering and persistent surveillance. Moreover, these systems coupled with the surge of small, low-cost electronics can perform autonomous and semi-autonomous missions not possible just ten years ago. This paper addresses flight time limitation issues by proposing an experimental method with procedures for system identification that may lead to modeling of energy storage in electric UAS'. Consequently, this will allow for energy storage to be used more effectively in planning autonomous missions. To achieve this, a set of baseline experiments were designed to measure the energy consumption of a mid-size UAS multi-rotor. Several different flight maneuvers were considered to include different lateral velocities, climbing, and hovering. Therefore, the goal of this paper is to create baseline flight data for each maneuver to be characterized with a certain rate of energy usage. Experimental results demonstrate the feasibility and robustness of the proposed approach. Future work will include the development of mission planning algorithms that provide realistic estimates of possible mission flight times and distances given specific mission parameters.Keywords: Battery energy management, flight performance, modeling, unmanned aerial system A BRIEF INTRODUCTIONLithium-based batteries present advantageous characteristics, such as high charge and discharge rates, longevity, high energy density, and affordable cost accompanied with a lightweight structure [1,2,3,4]. Nonetheless, only small improvements in lithium-based batteries have occurred over the past ten years with the biggest improvement being the reduction of price. Even with the most advanced lithium-based batteries, electric multi-rotor UAS mission plans are often dictated by the flight time (and consequently path) of the vehicle [5]. In most scenarios, this is generally limited to approximately thirty minutes before battery protection measures activate and the craft must land. However, lithium-based batteries are still affected by charge and discharge rates, life cycles, temperature, and their capacity rating [1]. These issues need to be considered when operating battery powered unmanned air systems. Currently, lithium-based battery characteristics offer the best power supply for small UAS' where r...

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Designing Unmanned Aircraft Systems

Cited by 135 publications

References 12 publications

Comprehensive Product Platform Planning (CP3) for a Modular Family of Unmanned Aerial Vehicles

Comprehensive Product Platform Planning (CP3) for a Modular Family of Unmanned Aerial Vehicles

System and mathematical modeling of quadrotor dynamics

LiPo battery energy studies for improved flight performance of unmanned aerial systems

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