Analysis of the Energy Storage Efficiency of a UAV-Mounted Sensor Launcher Built on Traditional Crossbow Launch Mechanisms

Deploying sensors to target locations using UAV platforms can effectively address the issue of limited aerial endurance in micro-UAVs. This paper introduces a launch method based on the crossbow principle, which is capable of concealing the deployment of heavy sensors. Given that the size and mass of the launcher on the UAV should be minimized, optimizing the structural energy storage performance of the launcher is essential. Initially, a static energy storage model for the launcher was developed, and criteria for evaluating the energy storage coefficient were established. Using the control variable method, the impact of seven structural parameters on the energy storage performance was analyzed. Based on these findings, a particle swarm optimization algorithm was proposed to optimize six parameters, aiming to maximize the energy storage coefficient. Subsequently, the bending stiffness coefficient of the launcher’s bow limb can be adjusted according to the specific application scenario to manage the energy storage within a reasonable range. Calculations revealed that after optimization, the maximum draw force was only 0.675 times that of the original plan under the same energy storage conditions. With the same maximum draw force, the energy storage capacity was 1.5 times higher than the original plan, indicating a significant optimization effect. This optimization approach provides a theoretical foundation for the energy storage optimization analysis of UAV-launched crossbow-based systems.

Authors

Publication Details

Journal
Drones
Published
2024-11-28
DOI
https://doi.org/10.3390/drones8120712
Citations
1
Primary Topic
Guidance and Control Systems
Type
article
Field-Weighted Citation Impact
1.30
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article

Analysis of the Energy Storage Efficiency of a UAV-Mounted Sensor Launcher Built on Traditional Crossbow Launch Mechanisms

Lingsong Di, Liang Liu, Lei Wang, Huiqi Xu et al.
1 citations
Drones
Guidance and Control Systems
1.30
article

Analysis of the Energy Storage Efficiency of a UAV-Mounted Sensor Launcher Built on Traditional Crossbow Launch Mechanisms

Lingsong Di, Liang Liu, Lei Wang, Huiqi Xu, Weihua Liu, Yanan He
article en
1 citations

Abstract

Deploying sensors to target locations using UAV platforms can effectively address the issue of limited aerial endurance in micro-UAVs. This paper introduces a launch method based on the crossbow principle, which is capable of concealing the deployment of heavy sensors. Given that the size and mass of the launcher on the UAV should be minimized, optimizing the structural energy storage performance of the launcher is essential. Initially, a static energy storage model for the launcher was developed, and criteria for evaluating the energy storage coefficient were established. Using the control variable method, the impact of seven structural parameters on the energy storage performance was analyzed. Based on these findings, a particle swarm optimization algorithm was proposed to optimize six parameters, aiming to maximize the energy storage coefficient. Subsequently, the bending stiffness coefficient of the launcher’s bow limb can be adjusted according to the specific application scenario to manage the energy storage within a reasonable range. Calculations revealed that after optimization, the maximum draw force was only 0.675 times that of the original plan under the same energy storage conditions. With the same maximum draw force, the energy storage capacity was 1.5 times higher than the original plan, indicating a significant optimization effect. This optimization approach provides a theoretical foundation for the energy storage optimization analysis of UAV-launched crossbow-based systems.

DronesVol. 8(12)
Affordable and clean energy
Openalex Percentile: Top 12%
Guidance and Control Systems
1.30
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Analysis of the Energy Storage Efficiency of a UAV-Mounted Sensor Launcher Built on Traditional Crossbow Launch Mechanisms — Lingsong Di, Liang Liu, et al. · Drones (2024) | TGRS Research Map | TGRS