Swarming Coordination of Multiple Unmanned Aerial Vehicles in Three-Dimensional Space

2016 ◽  
Author(s):  
Yongnan Jia
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Aerial Vehicles ◽  
Multiple Unmanned Aerial Vehicles ◽  
Three Dimensional Space

Three-dimensional multiple unmanned aerial vehicles formation control strategy based on second-order consensus

2016 ◽  
Vol 232 (3) ◽  
pp. 481-491 ◽  
Author(s):  
Xu Zhu ◽  
Xun-Xun Zhang ◽  
Mao-De Yan ◽  
Yao-Hong Qu ◽  
Hai Lin
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Cooperative Control ◽  
Formation Control ◽  
Three Dimensional ◽  
Second Order ◽  
Guidance Law ◽  
Aerial Vehicles ◽  
Cooperative Guidance ◽  
Consensus Strategy ◽  
Multiple Unmanned Aerial Vehicles

Considering three-dimensional formation control for multiple unmanned aerial vehicles, this paper proposes a second-order consensus strategy by utilizing the position and velocity coordinate variables. To maintain the specified geometric configuration, a cooperative guidance algorithm and a cooperative control algorithm are proposed together to manage the position and attitude, respectively. The cooperative guidance law, which is designed as a second-order consensus algorithm, provides the desired pitch rate, heading rate and acceleration. In addition, a synchronization technology is put forward to reduce the influence of the measurement errors for the cooperative guidance law. The cooperative control law, regarding the output of the cooperative guidance law as its input, is designed by deducing the state-space expression of both the longitudinal and lateral motions. The formation stability is analyzed to give a sufficient and necessary condition. Finally, the simulations for the three-dimensional formation control demonstrate the feasibility and effectiveness of the second-order consensus strategy.

scholarly journals Influence of the Sun Position and Platform Orientation on the Quality of Imagery Obtained from Unmanned Aerial Vehicles

2020 ◽  
Vol 12 (6) ◽  
pp. 1040 ◽  
Author(s):  
Aleksandra Sekrecka ◽  
Damian Wierzbicki ◽  
Michal Kedzierski
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Correction Method ◽  
Accurate Information ◽  
The Sun ◽  
Aerial Vehicles ◽  
The Difference ◽  
Third Dimension

Images acquired at a low altitude can be the source of accurate information about various environmental phenomena. Often, however, this information is distorted by various factors, so a correction of the images needs to be performed to recreate the actual reflective properties of the imaged area. Due to the low flight altitude, the correction of images from UAVs (unmanned aerial vehicles) is usually limited to noise reduction and detector errors. The article shows the influence of the Sun position and platform deviation angles on the quality of images obtained by UAVs. Tilting the camera placed on an unmanned platform leads to incorrect exposures of imagery, and the order of this distortion depends on the position of the Sun during imaging. An image can be considered in three-dimensional space, where the x and y coordinates determine the position of the pixel and the third dimension determines its exposure. This assumption is the basis for the proposed method of image exposure compensation. A three-dimensional transformation by rotation is used to determine the adjustment matrix to correct the image quality. The adjustments depend on the angles of the platform and the difference between the direction of flight and the position of the Sun. An additional factor regulates the value of the adjustment depending on the ratio of the pitch and roll angles. The experiments were carried out for two sets of data obtained with different unmanned systems. The correction method used can improve the block exposure by up to 60%. The method gives the best results for simple systems, not equipped with lighting compensation systems.

scholarly journals Equilibrium Optimizer with Generalized Opposition-based Learning for Multiple Unmanned Aerial Vehicles Path Planning

2021 ◽  
Author(s):  
Yang Chen ◽  
Dechang Pi ◽  
Bi Wang ◽  
Ali Wagdy Mohamed ◽  
Junfu Chen
Keyword(s):  
Path Planning ◽  
Unmanned Aerial Vehicles ◽  
Dimensional Space ◽  
Optimization Techniques ◽  
High Dimensional ◽  
Population Activity ◽  
Opposition Based Learning ◽  
Aerial Vehicles ◽  
Multiple Uavs ◽  
Multiple Unmanned Aerial Vehicles

Abstract Multiple Unmanned Aerial Vehicles (UAVs) path planning is the benchmark problem of multiple UAVs application, which belongs to the non-deterministic polynomial problem. Its objective is to require multiple UAVs flying safely to the goal position according to their specific start position in three-dimensional space. This issue can be defined as a high-dimensional optimization problem, the solution of which requires optimization techniques with global optimization capabilities. Equilibrium optimizer (EO) is a population-based meta-heuristic algorithm. In order to improve the optimization ability of EO to solve high dimensional problems, this paper proposes a modified equilibrium optimizer with generalized opposition-based learning (MGOEO), which improves the population activity by increasing the internal mutation and cross of the population. In addition, the generalized opposition-based learning is used to construct the population, which can effectively ensure that the algorithm has ability to jump out of the limitation of local optimal. Firstly, numerical experiments show that MGOEO has better optimization precision than EO and several other swarm intelligent algorithms. Then, the paths of UAVs are simulated in three different obstacle environments. The simulation results show that MGOEO can obtain safe and smooth paths, which are better than EO and other eight state-of-the-art optimization algorithms.

Three-dimensional containment control for multiple unmanned aerial vehicles

2016 ◽  
Vol 353 (13) ◽  
pp. 2929-2942 ◽  
Author(s):  
Tao Han ◽  
Zhi-Hong Guan ◽  
Yonghong Wu ◽  
Ding-Fu Zheng ◽  
Xian-He Zhang ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Three Dimensional ◽  
Containment Control ◽  
Aerial Vehicles ◽  
Multiple Unmanned Aerial Vehicles

Extension of the Spatial PDI Model to Three Dimensions

1997 ◽  
Vol 84 (1) ◽  
pp. 176-178
Author(s):  
Frank O'Brien
Keyword(s):  
Population Density ◽  
Dimensional Space ◽  
Finite Lattice ◽  
Three Dimensional ◽  
Upper Bounds ◽  
Three Dimensions ◽  
Lower And Upper Bounds ◽  
Density Index ◽  
Three Dimensional Space

The author's population density index ( PDI) model is extended to three-dimensional distributions. A derived formula is presented that allows for the calculation of the lower and upper bounds of density in three-dimensional space for any finite lattice.

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