UAV Formation Trajectory Planning Algorithms: A Review

Yang, Yunhong; Xiong, Xingzhong; Yan, Yonghong

doi:10.3390/drones7010062

Cited by 56 publications

(18 citation statements)

References 157 publications

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“…To validate the effectiveness of the proposed two-step strategy for UAV swarm formation adjustment, we compare it with a geometric optimization-based approach ( [7]). This approach stems from classical geometry problems and adjusts the UAV position by measuring and optimizing the path angle error through iterative calculations.…”

Section: Compared To the Representative Adjustment Strategymentioning

confidence: 99%

“…UAV swarm formation flight involves multiple UAVs flying in a specific formation to achieve cooperative operation, task division, and information sharing. The technology has wide-ranging applications in military, civil, scientific research, and entertainment fields [1][2][3][4][5][6][7][8][9][10]. To control multiple UAVs in a specific formation, it is primum necessary to localize them [7].…”

Section: Introductionmentioning

confidence: 99%

“…The technology has wide-ranging applications in military, civil, scientific research, and entertainment fields [1][2][3][4][5][6][7][8][9][10]. To control multiple UAVs in a specific formation, it is primum necessary to localize them [7]. In UAV localization studies, early approaches can be categorized as active localization methods.…”

Section: Introductionmentioning

confidence: 99%

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A New Method of UAV Swarm Formation Flight Based on AOA Azimuth-Only Passive Positioning

Kang,

Deng,

Yan

et al. 2024

Drones

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UAV swarm passive positioning technology only requires the reception of electromagnetic signals to achieve the positioning and tracking of radiation sources. It avoids the active positioning strategy that requires active emission of signals and has the advantages of good concealment, long acting distance, and strong anti-interference ability, which has received more and more attention. In this paper, we propose a new UAV swarm formation flight method based on pure azimuth passive positioning. Specifically, we propose a two-circle positioning model, which describes the positional deviation of the receiving UAV using trigonometric functions relative to the target in polar coordinates. Furthermore, we design a two-step adjustment strategy that enables the receiving UAV to reach the target position efficiently. Based on the above design, we constructed an optimized UAV swarm formation scheme. In experiments with UAV numbers of 8 and 20, compared to the representative comparison strategy, the proposed UAV formation scheme reduces the total length of the UAV formation by 34.76% and 55.34%, respectively. It demonstrates the effectiveness of the proposed method in the application of assigning target positions to UAV swarms.

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Section: Compared To the Representative Adjustment Strategymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A New Method of UAV Swarm Formation Flight Based on AOA Azimuth-Only Passive Positioning

Kang,

Deng,

Yan

et al. 2024

Drones

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“…A formation composed of multiple UAVs can effectively exert the performance of UAV formation through information interaction and cooperative interoperability technologies among the carriers, in which high-precision positioning information is one of the prerequisites for UAV formation to perform their tasks [4]. UAV formation currently mainly use Global Positioning System, BeiDou Navigation Satellite System and other satellite navigation systems to provide absolute/relative navigation information for UAVs, which has high positioning accuracy [5,6]. But UAVs at low altitude are highly susceptible to environmental occlusion and human interference, and incorrect positioning information can cause the collapse of the entire UAV formation configuration at low altitude [7].…”

Section: Introductionmentioning

confidence: 99%

Cooperative navigation of unmanned aerial vehicle formation with delayed measurement

Shi,

Xiong,

Chen

et al. 2024

Meas. Sci. Technol.

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This paper focused on the problem of positioning accuracy degradation caused by delayed measurement information in Unmanned Aerial Vehicle (UAV) formation cooperative navigation under complex environments such as cities and hills, and presented a non-synchronous compensation algorithm based on kinematic constraints and constructed a distributed cooperative navigation filter based on the analysis of the basic operating characteristics of inertial devices, satellite receivers, and ranging sensors. In the UAV formation, the leader-UAV is equipped with Real-Time Kinematic (RTK) differential equipment and airborne data link to construct the airborne reference beacons and provide cooperative navigation services for the wingmen-UAV. Firstly, the navigation filtering framework with inertial sensors as the core is established. Secondly, the non-synchronous compensation filter is constructed by using the kinematic constraint model, which compensates and corrects the non-synchronous air-based position of the leader-UAV, and reduces the effect of delayed measurement on the positioning error of the system. Then the fault diagnosis algorithm is utilized to complete the identification and rejection of abnormal range values in the case of Non-Line-of-Sight (NOLS). Finally, the navigation parameters are solved by the Kalman filter. Simulation results show that the non-synchronous compensated filtering proposed in this paper can improve the absolute positioning accuracy by 55%, which effectively improves the cooperative navigation performance and robustness under the presence of random time delay in the measurement information.

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“…In the case of a quadcopter, the main issue to be solved is also to design control that will guide a holonomic vehicle according to the velocity vector from the potential field, keeping its speed, position, and course as close to those resulting from the free movement of the reference position. Only then is it possible to track a random unknown trajectory in real time, not only along a predefined planned path, and this is the main significance of the work cited above in contrast to those based on path planning [ 13 ] (even if a short time horizon of the ground target movement prediction is available [ 14 ]) and on path optimization: particle swarm optimization and pigeon-inspired optimization. The present work presents a method that applies an artificial-potential-field approach and modified flight control for quadcopters to achieve the tracking of a freely moving point with unknown dynamics.…”

Section: Introductionmentioning

confidence: 99%

Artificial Potential Field Based Trajectory Tracking for Quadcopter UAV Moving Targets

Kownacki

2024

Sensors

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The trajectory or moving-target tracking feature is desirable, because it can be used in various applications where the usefulness of UAVs is already proven. Tracking moving targets can also be applied in scenarios of cooperation between mobile ground-based and flying robots, where mobile ground-based robots could play the role of mobile landing pads. This article presents a novel proposition of an approach to position-tracking problems utilizing artificial potential fields (APF) for quadcopter UAVs, which, in contrast to well-known APF-based path planning methods, is a dynamic problem and must be carried out online while keeping the tracking error as low as possible. Also, a new flight control is proposed, which uses roll, pitch, and yaw angle control based on the velocity vector. This method not only allows the UAV to track a point where the potential function reaches its minimum but also enables the alignment of the course and velocity to the direction and speed given by the velocity vector from the APF. Simulation results present the possibilities of applying the APF method to holonomic UAVs such as quadcopters and show that such UAVs controlled on the basis of an APF behave as non-holonomic UAVs during 90° turns. This allows them and the onboard camera to be oriented toward the tracked target. In simulations, the AR Drone 2.0 model of the Parrot quadcopter is used, which will make it possible to easily verify the method in real flights in future research.

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UAV Formation Trajectory Planning Algorithms: A Review

Cited by 56 publications

References 157 publications

A New Method of UAV Swarm Formation Flight Based on AOA Azimuth-Only Passive Positioning

A New Method of UAV Swarm Formation Flight Based on AOA Azimuth-Only Passive Positioning

Cooperative navigation of unmanned aerial vehicle formation with delayed measurement

Artificial Potential Field Based Trajectory Tracking for Quadcopter UAV Moving Targets

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