Large-Scale Fixed-Wing UAV Swarm System Control With Collision Avoidance and Formation Maneuver

Li, Jiacheng; Fang, Yangwang; Cheng, Haoyun; Wang, Zhikai; Wu, Zihao; Zeng, Mengjie

doi:10.1109/jsyst.2022.3212068

Cited by 15 publications

(4 citation statements)

References 35 publications

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“…To calculate the aggregation index, we first calculate the virtual centroid of the whole swarm by using Eq. (19).…”

Section: Aggregation Of Individualsmentioning

confidence: 99%

“…The wind field model is decoupled into time-varying disturbances in three directions of space. Pyke and Stark [19] proposed a UAV swarm control model called p-drones, which is a combination of Particle Swarm Optimization (PSO) and virtual-physical-law based control model. This model has better performance in navigation and collision avoidance compared with existing algorithms.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Objective Rule System Based Control Model with Tunable Parameters for Swarm Robotic Control in Confined Environment

Wang,

Xing,

Wang

et al. 2024

Complex Syst. Model. Simul.

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Enhancing the adaptability of Unmanned Aerial Vehicle (UAV) swarm control models to cope with different complex working scenarios is an important issue in this research field. To achieve this goal, control model with tunable parameters is a widely adopted approach. In this article, an improved UAV swarm control model with tunable parameters namely Multi-Objective O-Flocking (MO O-Flocking) is proposed. The MO O-Flocking model is a combination of a multi rule control system and a virtual-physical-law based control model with tunable parameters. To achieve multi-objective parameter tuning, a multi-objective parameter tuning method namely Improved Strength Pareto Evolutionary Algorithm 2 (ISPEA2) is designed. Simulation experiment scenarios include six target orientation scenarios with different kinds of objectives. Experimental results show that both the ISPEA2 algorithm and MO O-Flocking control model have good performance in their experiment scenarios.

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“…To calculate the aggregation index, we first calculate the virtual centroid of the whole swarm by using Eq. (19).…”

Section: Aggregation Of Individualsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-Objective Rule System Based Control Model with Tunable Parameters for Swarm Robotic Control in Confined Environment

Wang,

Xing,

Wang

et al. 2024

Complex Syst. Model. Simul.

View full text Add to dashboard Cite

show abstract

“…In [18], an improved APF method for a multi-UAV system was presented to solve the path planning and formation control in 3D constrained space. In [19], a collision avoidance and formation control algorithm for a large-scale fixed-wing UAV swarm system was proposed based on the improved APF function and new swarm structure. By using the distributed control architecture of the swarm system, this algorithm can be applied in a large-scale fixed-wing UAV swarm system.…”

Section: Introductionmentioning

confidence: 99%

An Effective Strategy for Collision Avoidance of Multiple UAVs With Unknown Acceleration

Tao,

Guo,

Shang

2023

IEEE Access

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In this article, an effective strategy using mixed 3-D geometric and the miss distance approach is proposed for the collision avoidance between multiple UAVs with unknown acceleration. At each time sampling, conflict detection of multiple UAVs is first performed. If the conflict exists, the expected heading angle or/and expected elevation angle of UAV are computed. Then, UAV begins to perform the avoidance maneuvers. The heading angle or/and elevation angle of UAV are uniformly adjusted to the expected value. The condition required for successful collision avoidance is derived for the proposed strategy. To enhance safety during execution of avoidance maneuver, a simple and feasible modify measure is presented. In the case of multiple UAVs, a logic to allocate the avoidance prioritization of each UAV is proposed. The trajectory recovery algorithm is presented to ensure UAV to return to the planning flight paths. For the proposed strategy, it is not assumed that all UAVs have constant ground speeds and direction of the velocity. Thus, it is very suitable in reality application. Numerical simulation results show the efficiency and superiority of the proposed strategy.

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“…Large-scale networked systems (LSNSs) have attracted a great deal of attention becase they are widely used in unmanned aircraft formation flights [1], smart grids [2,3], automated highway systems [4], and industrial process systems [5,6]. LSNSs contain a great many of subsystems, which have various dynamics [7].…”

Section: Introductionmentioning

confidence: 99%

Robust H∞ Performance Analysis of Uncertain Large-Scale Networked Systems

Guan

Liu

2023

Mathematics

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This paper considers the robust H∞ performance problem of continuous-time uncertain large-scale networked systems (LSNSs). The systems consist of numerous arbitrarily connected subsystems, each of which has different dynamics. Currently it is computationally difficult to manage systems with the existing lumped analysis method; therefore, exploiting the structural properties of the systems, sufficient conditions are derived for robust H∞ performance. Based on these results, an analysis condition that depends only on the parameters of each single subsystem is further obtained. Some numerical simulations are proposed to verify the validity and superiority of the developed conditions.

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Large-Scale Fixed-Wing UAV Swarm System Control With Collision Avoidance and Formation Maneuver

Cited by 15 publications

References 35 publications

Multi-Objective Rule System Based Control Model with Tunable Parameters for Swarm Robotic Control in Confined Environment

Multi-Objective Rule System Based Control Model with Tunable Parameters for Swarm Robotic Control in Confined Environment

An Effective Strategy for Collision Avoidance of Multiple UAVs With Unknown Acceleration

Robust H∞ Performance Analysis of Uncertain Large-Scale Networked Systems

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