A Cooperative UAV Swarm Localization Algorithm Based on Probabilistic Data Association for Visual Measurement

Wang, Dongjia; Lian, Baowang; Liu, Yangyang; Gao, Bo

doi:10.1109/jsen.2022.3202356

Cited by 5 publications

(3 citation statements)

References 32 publications

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“…During visual cooperative navigation in UAV swarms, UAVs capture feature information of other UAVs using visual sensors. This information is then processed through modeling, filtering, and other techniques to track the targets that fulfill certain requirements [75]. Estimating their motion state based on the target's position, UAVs adjust factors like flight attitude or speed to adhere to preset constraints and complete the UAV swarm's navigational flight.…”

Section: Visual Collaborative Navigation Modementioning

confidence: 99%

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Comparative Analysis of Different UAV Swarm Control Methods on Unmanned Farms

Ming,

Jiang,

Luo

et al. 2023

Agronomy

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Unmanned farms employ a variety of sensors, automated systems, and data analysis techniques to enable fully automated and intelligent management. This not only heightens agricultural production efficiency but also reduces the costs associated with human resources. As integral components of unmanned farms’ automation systems, agricultural UAVs have been widely adopted across various operational stages due to their precision, high efficiency, environmental sustainability, and simplicity of operation. However, present-day technological advancement levels and relevant policy regulations pose significant restrictions on UAVs in terms of payload and endurance, leading to diminished task efficiency when a single UAV is deployed over large areas. Accordingly, this paper aggregates and analyzes research pertaining to UAV swarms from databases such as Google Scholar, ScienceDirect, Scopus, IEEE Xplorer, and Wiley over the past decade. An initial overview presents the current control methods for UAV swarms, incorporating a summary and analysis of the features, merits, and drawbacks of diverse control techniques. Subsequently, drawing from the four main stages of agricultural production (cultivation, planting, management, and harvesting), we evaluate the application of UAV swarms in each stage and provide an overview of the most advanced UAV swarm technologies utilized therein. Finally, we scrutinize and analyze the challenges and concerns associated with UAV swarm applications on unmanned farms and provide forward-looking insights into the future developmental trajectory of UAV swarm technology in unmanned farming, with the objective of bolstering swarm performance, scalability, and adoption rates in such settings.

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Section: Visual Collaborative Navigation Modementioning

confidence: 99%

Section: Visually Guided Navigationmentioning

confidence: 99%

Comparative Analysis of Different UAV Swarm Control Methods on Unmanned Farms

Ming,

Jiang,

Luo

et al. 2023

Agronomy

View full text Add to dashboard Cite

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“…In [21], [22], map-matching positioning was proposed to reduce the requirements for infrastructure but increased the hardware cost and computational complexity, which is particularly unfavorable for small UAVs with short endurance. Different from the idea of map matching, authors in [23] proposed a vision-based UAV group relative angle measurement scheme, which makes full use of the advantages of multi-device collaboration, and achieves a better UAV positioning effect than EKF. Multi-UAV collaboration can improve the positioning accuracy of individual UAVs, but once some of them have positioning anomalies, it would drag down the performance of the entire system.…”

Section: Related Workmentioning

confidence: 99%

Multi-UAV Collaboration and IMU Fusion Localization Method in Partial GNSS-Denied Scenarios

Zheng,

Xie,

2023

IEEE Access

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When it comes to disaster rescue, quickly and accurately locating the trapped people will not miss the golden rescue time, thereby improving the success rate of rescue. The existing research has focused on the use of unmanned aerial vehicles (UAVs) to locate stationary user equipment (UE). However, the simultaneous movement of the UAVs and the UE brings about problems of UAV coordinate deviation and range interference, resulting in poor accuracy and stability of UE localization. To solve the mentioned issues above, a fusion localization method based on multi-UAV collaborative ranging and user-side inertial measurement unit (IMU) module was proposed for GNSS-denied scenarios. First, the initial coordinate deviations of the UAVs were corrected by multi-UAV collaboration; Then, a pre-processing scheme based on density peaks clustering (DPC) was proposed to eliminate the abnormal interference in the ranging data; Finally, the unscented Kalman filter (UKF) was used to realize the fusion localization based on multi-UAV ranging and user-side IMU module. The simulation results showed that the proposed scheme can maintain the accuracy and stability of UE localization in the presence of ranging interference, and the positioning error was reduced by 21.4% compared with the commonly used extended Kalman filter (EKF). Moreover, the proposed localization scheme had a better performance in low-altitude rather than high-altitude flight scenarios.INDEX TERMS Density peaks clustering (DPC), fusion localization, inertial measurement unit (IMU), unmanned aerial vehicle (UAV), unscented Kalman filter (UKF)

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