Metaheuristic Optimization-Based Path Planning and Tracking of Quadcopter for Payload Hold-Release Mission

Belge, Egemen; Altan, Aytaç; Hacıoğlu, Rıfat

doi:10.3390/electronics11081208

Cited by 89 publications

(21 citation statements)

References 43 publications

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“…Two different data driven Linear Parameter-Varying MPC (MPCLPV) algorithms have been proposed by using a subspace identification technique. Belge et al [39] performs the optimum path planning and tracking using Harris hawk optimization (HHO)-grey wolf optimization (GWO), a hybrid metaheuristic optimization algorithm, to enable the UAV to actualize the payload hold-release mission avoiding obstacles. His novel approach generates a fast and safe optimal path without becoming stuck with local minima, and the quad copter tracks the generated path with minimum energy and time consumption.…”

Section: Related Workmentioning

confidence: 99%

Data Driven Model Estimation for Aerial Vehicles: A Perspective Analysis

et al. 2022

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Unmanned Aerial Vehicles (UAVs) are important tool for various applications, including enhancing target detection accuracy in various surface-to-air and air-to-air missions. To ensure mission success of these UAVs, a robust control system is needed, which further requires well-characterized dynamic system model. This paper aims to present a consolidated framework for the estimation of an experimental UAV utilizing flight data. An elaborate estimation mechanism is proposed utilizing various model structures, such as Autoregressive Exogenous (ARX), Autoregressive Moving Average exogenous (ARMAX), Box Jenkin’s (BJ), Output Error (OE), and state-space and non-linear Autoregressive Exogenous. A perspective analysis and comparison are made to identify the salient aspects of each model structure. Model configuration with best characteristics is then identified based upon model quality parameters such as residual analysis, final prediction error, and fit percentages. Extensive validation to evaluate the performance of the developed model is then performed utilizing the flight dynamics data collected. Results indicate the model’s viability as the model can accurately predict the system performance at a wide range of operating conditions. Through this, to the best of our knowledge, we present for the first time a model prediction analysis, which utilizes comprehensive flight dynamics data instead of simulation work.

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Section: Related Workmentioning

confidence: 99%

Data Driven Model Estimation for Aerial Vehicles: A Perspective Analysis

et al. 2022

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“…There are also some literatures on related research. In the literature [33], Belge et al optimal path planning and tracking using the Harris Hawk optimization (HHO) and grey wolf optimization (GWO) algorithms to enable UAVs to achieve payload hold-release missions and avoid obstacles. In the literature [34], Aytaç et al proposed an MPC controller based on the Hammerstein model for real-time target tracking of a three-axis frame system, resulting in the robustness of the UAV under external disturbances.…”

Section: Introductionmentioning

confidence: 99%

A Moving Target Online Location Algorithm Based on Factor Graph

Wang

Zhang

et al. 2023

International Journal of Aerospace Engineering

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The superior performance of factor graphs compared to Kalman filtering in various fields and the use of factor graph algorithms instead of Kalman filtering algorithms in moving target localization tasks can reduce target localization error by more than 50%. However, the global factor graph algorithm may cause computational delays due to excessive computational effort. A moving target localization algorithm based on a combination of global and incremental optimization with improved factor graphs is proposed to improve localization accuracy and ensure that the computation time can be adapted to the requirements of online location. A reference point is introduced into the incremental calculation process, and it is first determined whether global or incremental calculation is used for this calculation by comparing the distance between the incremental localization results of the calculated reference point. The position of the UAV itself is then corrected by determining the position of the reference point, and this is used to finally locate the target. Simulation results show that the algorithm has good real-time performance compared to the time-consuming global algorithm. The online localization error of moving targets can be reduced by 17% compared to the incremental calculation results of the classical factor graph algorithm.

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“…[13][14][15] Additionally, many remarkable achievements have been made in topology optimization of material structure, effective performance prediction, and multiscale composite design using the artificial neural network. 16,17 Flight parameters were used by Belge et al, 18 to plan and track the path with minimum energy and time consumption for unmanned aerial vehicle. Additionally, the neural network based realtime control of a hexarotor unmanned aerial vehicle was performed by the same authors to reduce error for payloads on the targets determined by path tracking.…”

Section: Introductionmentioning

confidence: 99%

Warning model of new energy vehicle under improving time-to-rollover with neural network

Chao

Zhang

Wang

et al. 2022

Measurement and Control

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The probability of electric vehicle rollover accident can be effectively reduced by shortening the prediction time interval and improving the prediction accuracy. Based on a multilayer neural network, an improved time-to-rollover method is presented in this paper. Firstly, the force model of vehicle rollover is established and analyzed where the structure and mass of a battery box have an important influence on the occurrence of rollover. Then, the rollover indexes considering hyperparameters are divided into five categories, and the multi-layer neural network is used to simplify the algorithm structure of the time to rollover, and quickly calculate the operating state parameters with a variation step size in real time. Finally, the influence of the hyperparameters on the prediction results of neural network is studied, and higher efficiency is obtained by comparing with traditional methods.

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Metaheuristic Optimization-Based Path Planning and Tracking of Quadcopter for Payload Hold-Release Mission

Cited by 89 publications

References 43 publications

Data Driven Model Estimation for Aerial Vehicles: A Perspective Analysis

Data Driven Model Estimation for Aerial Vehicles: A Perspective Analysis

A Moving Target Online Location Algorithm Based on Factor Graph

Warning model of new energy vehicle under improving time-to-rollover with neural network

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