Dynamic path planning of unmanned aerial vehicle based on crowd density prediction

Jiao, Qingyu; Chen, Xinfeng; Zheng, Zhigang; Bai, Yiqin; Liu, Yansi; Zhang, Zhengjuan; Sun, Longni

doi:10.18306/dlkxjz.2021.09.007

Cited by 3 publications

(3 citation statements)

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“…Ground-based crowd density possesses both spatial and temporal attributes [40], and it varies in different raster cells and time periods. Therefore, a crowd density prediction model CNN-LSTM based on past crowd density data was established by utilizing convolutional neural network (CNN) and long-short-term memory network (LSTM) [41], and the ground population density ρ in the area at moment t could be obtained by joint training of the models.…”

Section: Ground Risk Assessmentmentioning

confidence: 99%

Research on eVTOL Air Route Network Planning Based on Improved A* Algorithm

Ye,

Zhao,

Guan

et al. 2024

Sustainability

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With the continuous opening of low-altitude airspace and the development of aircraft such as electric vertical takeoff and landing (eVTOL) vehicles, urban air traffic has become a sustainable and green development direction for future transportation. Air route networks, as a mainstream design scheme for air traffic, are able to provide prerequisites for eVTOL and other green aircraft to enter urban airspace for safe operation, among which air route planning is a fundamental component of air route network design. Currently, most of the research on aircraft path planning is performed in free airspace, lacking the analysis and processing for the complex operation environment, which has led to the high risk and large operation cost of path planning results, failing to meet the demand for safe and efficient development in the future. Aiming at the above problems, eVTOL-oriented air route planning research was carried out. Firstly, the urban low-altitude airspace structure was planned, and the operational levels of eVTOL were clarified; this was followed by introducing the urban dynamic air–ground risk factors and constructing a dynamic risk assessment model containing risk level information; finally, the improved A* algorithm based on the risk cost was employed to plan the eVTOL air route network, which finally realized the purpose of short path length and low total risk. The simulation results showed that the route generated by the improved A* algorithm could reduce the risk cost by at least 30% with a relatively small path cost, which ensured the operation efficiency and safety of eVTOLs and laid the foundation for the further sustainable and green development of urban airspace in the future.

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Section: Ground Risk Assessmentmentioning

confidence: 99%

Research on eVTOL Air Route Network Planning Based on Improved A* Algorithm

Ye,

Zhao,

Guan

et al. 2024

Sustainability

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“…Therefore, an improved A * algorithm was used for real-time path planning of drones. Finally, Beijing was used as an experimental city to demonstrate the feasibility of the proposed method in dynamic path planning [6].…”

Section: Related Workmentioning

confidence: 99%

A Path Planning Method for Logistics Oriented Drone Flight Routes

Xiao-Ying Wu,

Xin-Qian Fan

et al. 2023

Journal of Computers

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<p>This article mainly studies the path planning of unmanned aerial vehicle logistics delivery, considering the constraints in the process of unmanned aerial vehicle delivery, and establishes a unmanned aerial vehicle flight environment model based on logistics management. Based on the performance constraints and task requirements of logistics drones, a multi constraint logistics drone path planning model is established from the perspectives of transportation safety, economy, and speed. The established constraints include flight altitude, maximum angle constraints, energy consumption constraints, etc. Then, a hybrid algorithm is used to plan the drone path, and dynamic window algorithm is used for local path planning, Finally, the hybrid algorithm was fused through a smoothing strategy, and simulation experiments confirmed that the drone’s flight range, energy consumption, and planning time were significantly improved during the delivery process.</p> <p> </p>

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“…In global path planning algorithms, the A* algorithm [9,10] using the heuristic search method is one of the most commonly used path planning algorithms, with its excellent pathfinding completeness and path optimization. It is widely used for path planning in the field of unmanned driving, for example, urban logistics [11][12][13][14][15][16], environmental perception [17][18][19][20], underwater navigation [21][22][23], and robotic arm design [23]. Therefore, the traditional A* algorithm has always been the focus of scholars' research.…”

Section: Introductionmentioning

confidence: 99%

Application of A* Algorithm Based on Extended Neighborhood Priority Search in Multi-Scenario Maps

et al. 2023

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The robustness of the traditional A* algorithm of path planning is poor due to its excessive number of traversal nodes, slow search speed, and large turning angle. Aiming to solve the above problems, a multi-scenario adaptive A* algorithm based on extended neighborhood priority search is proposed. Firstly, this algorithm designs the heuristic function that can adapt to various scene changes by quantifying the scene map information, and the search weight is adjusted adaptively to enhance the robustness and adaptability of the algorithm. Secondly, the search strategy based on extended neighborhood priority is adopted to improve the orientation of the algorithm, and the redundant node removal strategy is used to smooth the path to reduce the number of traversed nodes and the turning angle. Finally, simulation tests are conducted in several representative map environments. The test results show that the proposed algorithm is superior to the traditional A* algorithm due to its stronger robustness and significantly improved performance metrics, with an 84.95% reduction in the number of traversal nodes, an 83.84% reduction in the number of path nodes, a 62.28% reduction in turning points on the path, a 77.38% reduction in the total turning angle, and a 58.47% reduction in the search time.

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Dynamic path planning of unmanned aerial vehicle based on crowd density prediction

Cited by 3 publications

References 0 publications

Research on eVTOL Air Route Network Planning Based on Improved A* Algorithm

Research on eVTOL Air Route Network Planning Based on Improved A* Algorithm

A Path Planning Method for Logistics Oriented Drone Flight Routes

Application of A* Algorithm Based on Extended Neighborhood Priority Search in Multi-Scenario Maps

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