Formation flight of unmanned aerial vehicles using track guidance

Lee, Dongwoo; Kim, Taegyun; Suk, Jinyoung

doi:10.1016/j.ast.2018.01.026

Cited by 47 publications

(22 citation statements)

References 5 publications

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“…For prevent collisions within the team during the formation forming, the safe distance is set. The cost function of the inteam collision is shown as formula (3)(4)(5)(6)(7)(8):…”

Section: ) the Cost Function Of The In-team Collisionmentioning

confidence: 99%

“…g t best is brought in formula (3-11) to get the optimized individual g 0 new at the next moment. That is the increased optimizing population operation, which is shown in formula (3)(4)(5)(6)(7)(8)(9)(10)(11).…”

Section: A Pre-dmpc-de Algorithm For Dmpc Modelmentioning

confidence: 99%

“…At present, the main methods to realize multi-UAV formation forming and reconfiguration include: the artificial potential field method [7], the bionic algorithm and the optimization control method [8]- [10]. In the case of the largescale formation and the dynamic transformation, artificial potential field method is difficult to make a mathematical model and easy to fall into local optimum.…”

Section: Introductionmentioning

confidence: 99%

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Trajectory Following and Improved Differential Evolution Solution for Rapid Forming of UAV Formation

Bian

Sun

2019

IEEE Access

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In this paper we proposed the circle trajectory assembly algorithm to control the multi-UAVs circular assembly formation. CTFAP solution provides rapid formation of UAVs on a circular orbit and solve the problem of large scattering distance. Proposed Distributed model prediction control framework improves the optimization ability and reduces the computation consumption with the better convergence ability of the UAV formation. Firstly, a circular trajectory following algorithm with an adaptive parameter is proposed to complete the rapid formation of UAVs on a circular orbit and solve the problem of large scattering distance during formation forming. Then, in the stage of formation reconfiguration, with distributed model prediction control framework (DMPC), the proposed method gets the prediction information of DMPC to optimize the population of classical differential evolution (DE) algorithm and improve the iterative optimization ability of DE algorithm. Experiments show that the proposed differential evolution algorithm greatly improves the efficiency of solving the formation reconfiguration problem under the DMPC framework and overcomes the disadvantages of random population of classical DE. For the proposed rapid forming method, assembly range is reduced by 41% compared with direct linearly formation assembly, and the formation forming time is reduced by approximately 21%. Compared with other optimization algorithms such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and DE, the proposed differential evolution algorithm reduces instruction response time of single-drones by 16%-30%.

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“…For prevent collisions within the team during the formation forming, the safe distance is set. The cost function of the inteam collision is shown as formula (3)(4)(5)(6)(7)(8):…”

Section: ) the Cost Function Of The In-team Collisionmentioning

confidence: 99%

Section: A Pre-dmpc-de Algorithm For Dmpc Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Trajectory Following and Improved Differential Evolution Solution for Rapid Forming of UAV Formation

Bian

Sun

2019

IEEE Access

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“…As a result, Achtelik et al [16], Wendel et al [17], Fraundorfer et al [18] created maps by a single camera, or stereo cameras [15], [16], [18]. Achtelik et al [19] created sparse maps of the environment using a single, cheap camera [15]. Wendel et al [9] created dense maps of the environment using a camera [8].…”

Section: Related Workmentioning

confidence: 99%

Autonomous UAV Navigation Using Reinforcement Learning

Liaq¹,

Byun²

2019

IJMLC

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Over the last few years, UAV applications have grown immensely from delivery services to military use. Major goal of UAV applications is to be able to operate and implement various tasks without any human aid. To best of our knowledge, in the existing works for autonomous navigation for UAV's, ideal environments (e.g., 2D) are considered instead of realistic or special hardware are used (e.g., nine range sensors) to navigate through an ideal environment. Therefore, in this thesis, we aim to overcome the limitations of the existing works by proposing a model for navigating a drone in an unknown environment without any human help or aid. The goal of this research is to navigate from location A to location B in unknown terrain without having any prior knowledge about the terrain using default drone sensors only. We present a model which is compatible with almost every off-the-shelf drone available in the market. Our methodology utilizes only standard drone sensors which are attached to almost every drone. These include a camera, GPS, IMU, magnetometer, and barometer. Our methodology uses 3D, POMDP, and continuous environment. It also takes into account environmental factors such as winds and rain. Our main contributions are: 1) We are using realistic environment model including factors like rain and wind. 2) We are only using onboard computing resources to run our model instead of some external server. 3) We were able to fly the achieve complete autonomous flight using only standard drone sensors.

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“…Study [8] presented a novel approach to a position control problem in rigid formations of nonholonomic UAVs. Reference [9] described a track guidance method for formation flight of UAVs. Study [10] presented a multirotor aircraft formation flight control method with collision avoidance capability.…”

Section: Introductionmentioning

confidence: 99%

Formation Generation for Multiple Unmanned Vehicles Using Multi-Agent Hybrid Social Cognitive Optimization Based on the Internet of Things

Yao

Wen

2019

Sensors

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Multi-agent hybrid social cognitive optimization (MAHSCO) based on the Internet of Things (IoT) is suggested to solve the problem of the generation of formations of unmanned vehicles. Through the analysis of the unmanned vehicle formation problem, formation principles, formation scale, unmanned vehicle formation safety distance, and formation evaluation indicators are taken into consideration. The application of the IoT enables the optimization of distributed computing. To ensure the reliability of the formation algorithm, the convergence of MAHSCO has been proved. Finally, computer simulation and actual unmanned aerial vehicle (UAV) formation generation flight generating four typical formations are carried out. The result of the actual UAV formation generation flight is consistent with the simulation experiment, and the algorithm performs well. The MAHSCO algorithm based on the IoT is proved to be able to generate formations that meet the mission requirements quickly and accurately.

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Formation flight of unmanned aerial vehicles using track guidance

Cited by 47 publications

References 5 publications

Trajectory Following and Improved Differential Evolution Solution for Rapid Forming of UAV Formation

Trajectory Following and Improved Differential Evolution Solution for Rapid Forming of UAV Formation

Autonomous UAV Navigation Using Reinforcement Learning

Formation Generation for Multiple Unmanned Vehicles Using Multi-Agent Hybrid Social Cognitive Optimization Based on the Internet of Things

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