Unmanned Aerial Vehicle Coverage Path Planning Algorithm Based on Cellular Automata

Song, Zhihua; Zhang, Han; Liu, Fei; Chen, Shitao; Zhang, Fa

doi:10.1109/iciscae.2018.8666895

Cited by 9 publications

(4 citation statements)

References 5 publications

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“…The increase in lift is related to the payload to be transported by the UAV. This research targets to be able to lift the liquid foliar fertilizer load of 4 L. By optimizing the arm using the arm carbon method, so that without reducing the strength and function of the UAV, the gross weight of the components becomes much lighter so that the weight can be focused on increasing the capacity of liquid fertilizer [7]. In addition, with the increasing capacity of liquid fertilizer, of course, the weight will be heavier, this deficiency causes problems, namely making the balance PID value can change with a large enough value which results in shock and can cause flight failure or the UAV to fall, therefore research is carried out to improve this condition is by using the split tank method, so that later it can minimize the shaking effect of the large capacity and heavy weight lifted so that the chance of the UAV failing to fly or falling does not occur.…”

Section: Methodsmentioning

confidence: 99%

Load and Stability Optimization Based Carbon Arm with Split Tank on Unmanned Aerial Vehicle for Foliar Fertilizer

Lutfi¹,

Purnomo²,

Rofii³

et al. 2023

jtim

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This research focuses on the load and stability of unmanned aerial vehicles that are as light as possible but with optimal stability functions and increasing the capacity of liquid fertilizer lifted by unmanned aerial vehicles. The increase in lift is related to the payload and stability to be carried by the unmanned aerial vehicle. By optimizing the arm using the arm carbon method, so without reducing the strength and function of the drone, the gross weight of the components becomes much lighter, so that the weight can be focused on increasing the capacity of liquid fertilizer. With the increase in liquid fertilizer capacity, of course, the weight also increases, this raises a new problem, which is to make the balance value when the drone take-off has a large magnitude, thus affecting stability which causes the potential to fail to fly, therefore this research was carried out so that the drone has stability. maximum and can minimize the effects of shocks from increasing capacity after using arm-carbon. The measurements taken were the success rate of the system in flying over agricultural land, power testing and current measurements. After testing, it was found that by using a split tank and carbon arm system, current and power tend to be more stable, so that battery life is longer, liquid fertilizer capacity can be increased and the potential for flight failure can be minimized with a split tank system with 90% accuracy, carried out optimizing weight with carbon arms and increasing the capacity of liquid fertilizer by 30%, using a split tank system increases the stability of the drone at take-off by up to 80%.

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Section: Methodsmentioning

confidence: 99%

Load and Stability Optimization Based Carbon Arm with Split Tank on Unmanned Aerial Vehicle for Foliar Fertilizer

Lutfi¹,

Purnomo²,

Rofii³

et al. 2023

jtim

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“…Z. Song et al [4] proposed a cellular automata to generate coverage trajectory of UAV with given width of footprint on the ground. But this research focused on designing a path to cover an area, not points.…”

Section: B Limitation Of Related Workmentioning

confidence: 99%

“…Our proposed algorithm focuses on covering points in the scenario by multi-UAV with the optimal path. By contrast, [4], [26], [27] both pay attention to design a trajectory for UAV to achieve the optimal area coverage. Compared with [5] and [6], they aim at designing a dynamically control method for UAV.…”

Section: Challenges and Contributionsmentioning

confidence: 99%

A Deep Learning Trained by Genetic Algorithm to Improve the Efficiency of Path Planning for Data Collection With Multi-UAV

Pan

Yang

2021

IEEE Access

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To collect data of distributed sensors located at different areas in challenging scenarios through artificial way is obviously inefficient, due to the numerous labor and time. Unmanned Aerial Vehicle (UAV) emerges as a promising solution, which enables multi-UAV collect data automatically with the preassigned path. However, without a well-planned path, the required number and consumed energy of UAVs will increase dramatically. Thus, minimizing the required number and optimizing the path of UAVs, referred as multi-UAV path planning, are essential to achieve the efficient data collection. Therefore, some heuristic algorithms such as Genetic Algorithm (GA) and Ant Colony Algorithm (ACA) which works well for multi-UAV path planning have been proposed. Nevertheless, in challenging scenarios with high requirement for timeliness, the performance of convergence speed of above algorithms is imperfect, which will lead to an inefficient optimization process and delay the data collection. Deep learning (DL), once trained by enough datasets, has high solving speed without worries about convergence problems. Thus, in this paper, we propose an algorithm called Deep Learning Trained by Genetic Algorithm (DL-GA), which combines the advantages of DL and GA. GA will collect states and paths from various scenarios and then use them to train the deep neural network so that while facing the familiar scenarios, it can rapidly give the optimized path, which can satisfy high timeliness requirements. Numerous experiments demonstrate that the solving speed of DL-GA is much faster than GA almost without loss of optimization capacity and even can outperform GA under some specific conditions.

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“…The fused filament fabrication process, also known as fusion deposition modelling, is one of the most popular AM methods for polymer or composite parts manufacturing. The process uses a continuous filament of thermoplastic material as raw material (Song et al, 2018). Molten raw materials are extruded from the print head and deposited layer by layer on the construction platform.…”

Section: Introductionmentioning

confidence: 99%

Study on UAV Based on Fused Filament Fabrication

2022

IJEC

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Air Unmanned Aerial Vehicles (UAVs) have the characteristics of simple structure and convenient carrying, while quadrotor UAVs have better manoeuvrability, can achieve fixed-point hover, and have vertical takeoff and landing capabilities. Additive Manufacturing (AM) is also often referred to as 3D printing. In the past ten years, AM technology has received extensive attention. Among the seven categories of AM technologies released by the American Society for Testing and Materials, Fused Filament Fabrication (FFF) is one of the most widely used and essential processing technologies in additive manufacturing. It mainly manufactures models by extruding molten wire (such as plastics, resins, composites, etc.). First, consider the additive manufacturing of the quadrotor aerial UAV entirely through fused filament fabrication, and control the UAV's flexibility by adjusting the filling material's parameter during the additive manufacturing process. However, the increased flexibility makes it difficult to control the UAV, and the structural, aerodynamic and aeroelastic effects must be further explored.

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Unmanned Aerial Vehicle Coverage Path Planning Algorithm Based on Cellular Automata

Cited by 9 publications

References 5 publications

Load and Stability Optimization Based Carbon Arm with Split Tank on Unmanned Aerial Vehicle for Foliar Fertilizer

Load and Stability Optimization Based Carbon Arm with Split Tank on Unmanned Aerial Vehicle for Foliar Fertilizer

A Deep Learning Trained by Genetic Algorithm to Improve the Efficiency of Path Planning for Data Collection With Multi-UAV

Study on UAV Based on Fused Filament Fabrication

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