Combined Optimal Control and Combinatorial Optimization for Searching and Tracking Using an Unmanned Aerial Vehicle

Albert, Anders; Imsland, Lars

doi:10.1007/s10846-018-0915-4

Cited by 8 publications

(4 citation statements)

References 49 publications

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“…Many researchers have been looking into the problem of cooperative path planning of multiple UAVs [12][13][14]. More research on drones can be found in [15][16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Optimizing UAV Photography: Strategies in Flight Control and Image Capture

zheng,

li,

xiao

et al. 2024

Preprint

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This paper addresses the challenges faced by novice drone operators in mastering flight altitude, speed, and shooting angles. It analyzes strategies for adjusting these parameters to capture satisfactory photographs, focusing on four key questions. Firstly, it examines the geometric relationship between flight altitude and camera coverage area, establishing an optimization model for drone shooting accuracy. Secondly, it independently analyzes the numerical relationship between shooting angles and camera coverage area, identifying optimal shooting angles using a UAV shooting accuracy score optimization model. Thirdly, it develops a small UAV system model and ground target model, employing a recursive target tracking algorithm to continuously adjust shooting angles for target acquisition. Finally, it introduces a novel RRT* algorithm for path planning around obstacles encountered during flight. We use MATLAB to select a reasonable obstacle avoidance strategy, and the global optimal route is obtained by smoothing processing. Simulation results demonstrate model stability and robustness across varying flight conditions.

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“…Many researchers have been looking into the problem of cooperative path planning of multiple UAVs [12][13][14]. More research on drones can be found in [15][16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Optimizing UAV Photography: Strategies in Flight Control and Image Capture

zheng,

li,

xiao

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Many researchers have been looking into the problem of cooperative path planning of multiple UAVs [12][13][14]. More research on drones can be found in [15][16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

The optimization model of UAV shooting flight routes

Zheng¹,

Li²,

Xiao³

et al. 2023

Preprint

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For the first drone operator, it is difficult to master the techniques of flight altitude, flight speed and shooting angle. The main task of this paper is to analyze how to adjust the flying height, flight speed and shooting angle of the drone and use the drone to take satisfactory photos. Mainly complete the following four questions. In the first part, we analyze the geometric relationship between the flying height and the camera's acquisition range area. Then, we set a pixel accuracy score index as the criterion. The sum of the weighted capture area and the pixel accuracy score is taken as the objective function, the flight height does not exceed the height limit area, and the unit pixel point is greater than 800 as the constraint condition. An optimization model for the accuracy of drone shooting was established. We use computer simulation to solve the accuracy method step by step. We obtain that the height range of the drone is [25 m, 100 m], and the optimal aerial height is 33 m when θ = 60º. In the second part, based on the first part, we independently analyzed the numerical relationship between the shooting angle and the camera acquisition range area. The sum of the captured area and the pixel accuracy score after updating the weight is the objective function, the shooting angle is the decision variable, and the UAV shooting accuracy score optimization model is used. The golden section algorithm is used to solve this problem. When the flying speed of the drone is 40 m/s and the flying height is 33 m, the optimal range of the shooting angle of the drone is [56.2248º, 77.4972º]. In the third part, we established a small UAV system model and a ground target model based on the knowledge of robotics. We generated motion trajectories of discrete and continuous states of the object. The recursive target tracking algorithm is used to continuously adjust the UAV shooting angle to ensure that the target is within its shooting area. Finally, the corresponding values of the two shooting angles with respect to time are obtained. The range of angle changes is [42.18º, 79.87º] and [32.81º, 79.89º]. In the fourth part, we generated 1 skyscraper, 2 signal towers, 3 telephone poles, 3 residential buildings, for a total of 9 obstacles. We present a new RRT* algorithm for path planning based on the obstacles encountered by UAVs during flight, that is, using the cost function to select the node with the minimum cost in the field of expanding nodes as the parent node, and the two new nodes are recalculated. We use MATLAB to select a reasonable obstacle avoidance strategy, and the global optimal route is obtained by smoothing processing. Finally, we verify the rationality of the model. Through computer simulation, we determine the three-dimensional field of flight speed constant, flying height and shooting angle with respect to the score, which proves model stability and robustness.

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“…Explicitly, as measurements collected and processed by search agents are normally imperfect, the probabilistic approach is an ideal way to represent this feature. Moreover, strategically, the approach can also indicate the site where potentially has a higher possibility to find the target than the other locations in the field [19][20][21].…”

Section: Introductionmentioning

confidence: 99%

A novel cooperative searching architecture for multi‐unmanned aerial vehicles under restricted communication

Ran

Sun

Song

et al. 2021

Asian Journal of Control

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Searching for a distinguishable lost target in a bounded area automatically with unmanned aerial vehicle (UAV) is a fundamental problem in the theory of physical search. This paper studies the problem in detail, presents a new and more realistic Bayesian formula representing the problem by taking communication capability of UAVs into consideration, and focuses on the case of applying a team of multiple cooperative UAVs in the field for the search, where each UAV can autonomously search the area for the target using Bayesian filtering algorithm. The perturbation of the searching performance by the different prior belief probability distribution functions is investigated. Empirical evidence findings illustrate that our approach yields improved accuracy.

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Combined Optimal Control and Combinatorial Optimization for Searching and Tracking Using an Unmanned Aerial Vehicle

Cited by 8 publications

References 49 publications

Optimizing UAV Photography: Strategies in Flight Control and Image Capture

Optimizing UAV Photography: Strategies in Flight Control and Image Capture

The optimization model of UAV shooting flight routes

A novel cooperative searching architecture for multi‐unmanned aerial vehicles under restricted communication

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