Airborne Delivery of Unmanned Aerial Vehicles via Joint Precision Airdrop Systems

Klinkmueller, Kurt M.; Wieck, Anthony J.; Holt, Joshua K.; Valentine, Andrew W.; Bluman, James E.; Kopeikin, Andrew; Prosser, Ekaterina M.

doi:10.2514/6.2019-2285

Cited by 12 publications

(7 citation statements)

References 6 publications

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“…At this point, the quadrotor would be deployed via dispenser to complete the rest of the descent and mission. The system successfully demonstrated the potential to deliver payloads to within 16 feet of target coordinates at distances over 1/2 mile from release point [9]. The deficiencies of the ADDAS lie in the extensive measures necessary to eliminate prolonged quadrotor descent, which result in a complex system with many moving parts and consequently, potential failures.…”

Section: Precision Aerial Deliverymentioning

confidence: 99%

Development and Viability of an Inverted Descent Quadrotor for Precision Aerial Delivery

Ward

Fields

2022

AIAA SCITECH 2022 Forum

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The field of Precision Aerial Delivery strives for the highest standard of accuracy, with sights set on delivering payloads from high altitudes to mere feet from goal locations.Quadrotors, due to high maneuverability and precision, are a logical choice to achieve this objective, however current inefficiencies of descent prevent quadrotors from use in Precision Aerial Delivery.Described herein are the development, experimentation and implementation of a near passive, Inverted Descent Quadrotor system. The Inverted Descent Quadrotor allows for high altitude deployment of a quadrotor fitted with standard hardware with the ability to descend more rapidly and with less energy requirement than previously possible. The decreased energy requirement of the Inverted Descent Quadrotor enables quadrotors to be used in high altitude scenarios, for missions that have previously been reserved for guided parachute or parafoil systems. Quadrotor systems thrive in such missions after descent, iii as they accommodate the potential for complex, highly precise activities to be completed once reaching ground.The development process consisted of wind tunnel thrust testing, a proof of concept single axis prototype, a functional Inverted Descent Quadrotor build, real world performance testing and mission simulation. Each stage of development provided valuable insight into the governing dynamics of rapid descent environments and informed the subsequent phases of development.Wind tunnel thrust testing helped to identify a potentially optimal baseline throttle to apply when facing high speed descent to maintain control authority, as well as quantify potential power savings. The single axis prototype proved control potential in an artificial descent environment, further quantified power usage, characterized potential glide performance and suggested control gains for the rapid descent environment. A functional Inverted Descent Quadrotor was then built and tested to prove initial functionality, and verify the results from single axis testing. The system was then tested to quantify control, power and glide performance against various configurations of normal quadrotor descent, as well as current comparable Precision Aerial Delivery Systems. Finally, the Inverted Descent Quadrotor s performance results were extrapolated to simulation to display the increased performance of the Inverted Descent Quadrotor in a mission setting.The Inverted Descent Quadrotor proved to be successful in reducing descent energy, which endowed high precision quadrotor systems the capability for descents far iv higher than previously possible. With far less sensitivity to wind prediction, coupled with the potential for payload capacity, the Inverted Descent Quadrotor showed promise worthy of future development and research.

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Section: Precision Aerial Deliverymentioning

confidence: 99%

Development and Viability of an Inverted Descent Quadrotor for Precision Aerial Delivery

Ward

Fields

2022

AIAA SCITECH 2022 Forum

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“…Methods for stabilization of sling loads and hoists include studies of the dynamics of container cranes and are an active area of research in academia [15][16][17][18][19][30][31][32][33][34][35][36][37] and industry [20,[38][39][40]. Currently, for MEDEVAC rescues, the common solution to solving this oscillation is the crew chief of the aircraft grabbing the sling and attempting to stabilize it themselves.…”

Section: Table 1 Cpt Medeiros Discusses Medevac Rescues [13]mentioning

confidence: 99%

Stabilization Environment for Swing Stabilization and MEDEVAC Hoists

Morock

Aldhizer

Lanzerotti

et al. 2021

Aiaa Aviation 2021 Forum

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This paper presents data related to helicopter sling load stabilization and MEDEVAC (Medical Evacuation) rescues collected by cadets performing research in the field at the United States Military Academy (West Point, NY) and Sapienza University of Rome (Rome, Italy) since 2018. The aim of this paper is to identify engineering constraints in MEDEVAC rescues. Constraints in two typical scenarios are presented. This information can then be included in simulations and models of swing stabilization and hoist control methods. Information is obtained through a literature review and interviews with U.S. Army helicopter pilots and crew chiefs who perform MEDEVAC rescues.

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“…Various types of research have been carried out to explore the potential of using drones to deliver goods to hard-to-reach locations, such as mountainous areas that are difficult to access by conventional land vehicles. (1,2) In addition, the role of cargo drones in emergency situations, such as delivering goods to traffic accident sites quickly and efficiently, has also attracted the attention of researchers. The ability of drones to reach difficult locations has quickly become a promising solution to support logistical assistance in emergency situations.…”

Section: Introductionmentioning

confidence: 99%

Design of AI-based 3.84 kW Battery Package Using Backpropagation Artificial Neural Network Algorithm for Cargo Drones

Hartono,

Oh,

Lim

et al. 2024

Sensors and Materials

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Despite limitations in payload and range, cargo drones have promising applications in emergency logistics and remote delivery. In this study, we tackle these challenges by developing a high-capacity 3.84 kW battery specifically designed for a 50-kg-payload cargo drone operating in demanding terrains. Focusing on the transport of emergency goods, we investigate key drone design aspects and details of the battery pack development, including cell selection, internal configuration, and critical circuits for cell balancing, charging/discharging, and advanced battery management. A key innovation is the integration of a backpropagation artificial neural network (BPANN) algorithm to predict the depth of discharge (DoD) and the state of charge (SoC). Research results show that BPANN offers highly accurate predictions, with error percentages as low as 0.12% for DoD and 0.02% for SoC, ensuring optimized and safe battery operation. Comprehensive field testing is carried out to evaluate the effectiveness of the proposed cell balancing strategy, robust battery management system (BMS), and BPANN implementation. We investigate the drone's performance in terms of DoD, SoC, and overall field operation with the designed battery pack and demonstrate its feasibility and potential for realworld applications.

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Airborne Delivery of Unmanned Aerial Vehicles via Joint Precision Airdrop Systems

Cited by 12 publications

References 6 publications

Development and Viability of an Inverted Descent Quadrotor for Precision Aerial Delivery

Development and Viability of an Inverted Descent Quadrotor for Precision Aerial Delivery

Stabilization Environment for Swing Stabilization and MEDEVAC Hoists

Design of AI-based 3.84 kW Battery Package Using Backpropagation Artificial Neural Network Algorithm for Cargo Drones

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