Vision-aware air-ground cooperative target localization for UAV and UGV

Liu, Daqian; Bao, Weidong; Zhu, Xiaomin; Fei, Bowen; Xiao, Zhenliang; Men, Tong

doi:10.1016/j.ast.2022.107525

Cited by 18 publications

(5 citation statements)

References 27 publications

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“…Zuo Yujia [18] optimized the dual-copter angular rendezvous algorithm in which the visual axis vector obtained by solving due to the influence of measurement errors such as optoelectronic side-angle code discs has a deviation from the actual visual axis. Liu [19] proposed a two-way feedback verification algorithm to ensure that each UV locates the target at the optimal position. Zhang [20] proposed a cooperative autonomous localization scheme for aerial-Ground unmanned systems for outdoor industrial environments, which effectively overcomes the problem of matching between vastly different aerial-Ground viewpoints.…”

Section: Related Workmentioning

confidence: 99%

Aerial-ground cooperative target localization based on trajectory matching

Tang,

Ma,

et al. 2024

Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024)

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Aerial-ground unmanned systems are the mainstream of research in recent years, target localization is a key step in rescue or strike missions, and the use of aerial-ground unmanned systems for target localization has great research value. However, aerial-ground systems have large differences in manoeuvrability and large sensor parallax. In this paper, an aerial-ground cooperative target localization method based on trajectory matching is proposed. First, the algorithm obtains the traveling trajectory of the unmanned vehicle in the UAV pixels. Then, the pixel trajectory and the UAV local coordinate trajectory are utilized to solve the transformation matrix from the pixel coordinate system to the geographic coordinate system. Finally, the geographic coordinates of other targets using the transformation matrix. The algorithm uses very little sensor data and a small number of platforms, and the experimental results show that the algorithm has an average localization accuracy of 1.55m at a UAV flight altitude of 100m, which is better than other target localization algorithms.

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Section: Related Workmentioning

confidence: 99%

Aerial-ground cooperative target localization based on trajectory matching

Tang,

Ma,

et al. 2024

Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024)

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“…Then, the core of air-ground cooperative control is trajectory tracking control. [7][8][9][10][11][12][13] In Reference 7, a scalable distributed network architecture for UAHs was designed to study the self-organized aggregation control for the large-scale UAHs, in which a large number of UAHs were composed of several interconnected global coverage layers, but lack the communication with ground station. In Reference 8, an air-ground collaborative positioning architecture was presented to address the limitations of single UAH or UGV positioning.…”

Section: Introductionmentioning

confidence: 99%

“…The UGV provides the track reference and supplies for the UAH, which needs communication network to interact with the UGV. Then, the core of air‐ground cooperative control is trajectory tracking control 7–13 . In Reference 7, a scalable distributed network architecture for UAHs was designed to study the self‐organized aggregation control for the large‐scale UAHs, in which a large number of UAHs were composed of several interconnected global coverage layers, but lack the communication with ground station.…”

Section: Introductionmentioning

confidence: 99%

“…In Reference 7, a scalable distributed network architecture for UAHs was designed to study the self‐organized aggregation control for the large‐scale UAHs, in which a large number of UAHs were composed of several interconnected global coverage layers, but lack the communication with ground station. In Reference 8, an air‐ground collaborative positioning architecture was presented to address the limitations of single UAH or UGV positioning. In Reference 9, the cooperative control for the UAH/UGV systems was studied with the constraints of the UGV speed, the load power of the UAH, and the communication restrictions between the UAH and the UGV.…”

Section: Introductionmentioning

confidence: 99%

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Switching‐like event‐triggered tracking control for UAH/UGV systems under external disturbance and DoS attacks

Zhang,

Pan,

et al. 2024

Intl J Robust & Nonlinear

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In this article, by considering the existence of unavailable state, external disturbance, and denial of service (DoS) attacks, an anti‐disturbance trajectory tracking controller is proposed for the air‐ground system composed of unmanned autonomous helicopter (UAH) and unmanned ground vehicle (UGV). Initially, by combining the advantages of conventional event‐triggered scheme (ETS) and memory ETS, a switching‐like event‐triggered mechanism is put forward, which can induce less data transmissions without reducing control performance, and effectively restrain the DoS attacks. Second, by dividing the DoS attacks into active intervals and sleep ones, the concept of acknowledgement character technology (ACK) is presented to define the suitable types of time intervals. Third, a switching‐like dynamic ETS is introduced for the sleep intervals of DoS attacks while the ETS with fixed triggered‐threshold is exploited for the active intervals. Fourth, based on the occurrence of DoS attacks, a switching‐like observer and a normal one are respectively proposed to estimate the UGV state, the UAH state, the reference input of the UGV, and the disturbance of the UAH, which are further utilized to design the tracking controller. Fifth, an augmented closed‐loop system consisting of observation errors and tracking error is established and its stability is analyzed by using Lyapunov stability theory. Then, a sufficient condition on co‐designing the parameters of switching‐like ETS, observers, and tracking controller is presented in terms of linear matrix inequalities (LMI). Finally, the validity and superiority of the proposed control scheme are verified by resorting to simulations and comparisons.

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“…The positioning results obtained by a single measurement are greatly affected by random errors. Two mainstream methods for solving this issue include multi-machine intersection positioning and a single aerial camera that accomplish multiple measurements [28][29][30][31][32][33][34][35]. Another related theory proposed to reduce the influence of random errors is filtering algorithms [36][37][38][39][40][41][42][43][44][45][46][47][48][49].…”

Section: Introductionmentioning

confidence: 99%

Review of Target Geo-Location Algorithms for Aerial Remote Sensing Cameras without Control Points

et al. 2022

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Aerial cameras are one of the main devices for obtaining ground images in the air. Since the industrial community sets higher requirements of aerial cameras’ self-locating performance yearly using aerial cameras to locate ground targets has become a research hotspot in recent years. Based on the situation that no ground control point exists in target areas, the calculation principle of the aerial remote sensing image positioning algorithm has been analyzed by establishing different positioning models. Several error analysis models of the positioning algorithm based on the total differential method and the Monte Carlo method are established, and relevant factors that cause the positioning error are summarized. The last section proposes the optimization direction of aerial camera positioning algorithms in the future, which are verified by related simulation experiments. This paper provides a certain degree of guidelines in this area for researchers, who can quickly understand the current development and optimization direction of target geo-location algorithms of aerial remote sensing imagery.

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Vision-aware air-ground cooperative target localization for UAV and UGV

Cited by 18 publications

References 27 publications

Aerial-ground cooperative target localization based on trajectory matching

Aerial-ground cooperative target localization based on trajectory matching

Switching‐like event‐triggered tracking control for UAH/UGV systems under external disturbance and DoS attacks

Review of Target Geo-Location Algorithms for Aerial Remote Sensing Cameras without Control Points

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