A risk‐based unmanned aerial vehicle path planning scheme for complex air–ground environments

Multifarious applications of unmanned aerial vehicles (UAVs) are thriving in extensive fields and facilitating our lives. However, the potential third-party risks (TPRs) on the ground are neglected by developers and companies, which limits large-scale commercialization. Risk assessment is an efficacious method for mitigating TPRs before undertaking flight tasks. This article incorporates the probability of UAV crashing into the TPR assessment model and employs an A* path-planning algorithm to optimize the trade-off between operational TPR cost and economic cost, thereby maximizing overall benefits. Experiments demonstrate the algorithm outperforms both the best-first-search algorithm and Dijkstra's algorithm. In comparison with the path with the least distance, initially, the trade-off results in a <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mn>1.88</mml:mn> <mml:mo>%</mml:mo></mml:mrow> <mml:annotation>$1.88\%$</mml:annotation></mml:semantics> </mml:math> increase in distance while achieving an <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mn>89.47</mml:mn> <mml:mo>%</mml:mo></mml:mrow> <mml:annotation>$89.47\%$</mml:annotation></mml:semantics> </mml:math> reduction in TPR. As the trade-off progresses, this relationship shifts, leading to a <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mn>20.62</mml:mn> <mml:mo>%</mml:mo></mml:mrow> <mml:annotation>$20.62\%$</mml:annotation></mml:semantics> </mml:math> reduction in the distance with only a negligible increase in TPR by 0.0001, matching the TPR-cost-based algorithm. Furthermore, we conduct simulations on the configuration of UAV path networks in five major cities in China based on real-world travel data and building data. Results reveal that the networks consist of one-way paths that are staggered in height. Moreover, in coastal cities particularly, the networks tend to extend over the sea, where the TPR cost is trivial.

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Publication Details

Journal
Risk Analysis
Published
2024-12-22
DOI
https://doi.org/10.1111/risa.17685
Citations
1
Primary Topic
Air Traffic Management and Optimization
Type
article
Field-Weighted Citation Impact
1.30

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article

A risk‐based unmanned aerial vehicle path planning scheme for complex air–ground environments

Kai Zhou, Xiaobo Qu, Yuhao Wang, Kai Wang
1 citations
Risk Analysis
Air Traffic Management and Optimization
1.30
article

A risk‐based unmanned aerial vehicle path planning scheme for complex air–ground environments

Kai Zhou, Xiaobo Qu, Yuhao Wang, Kai Wang
article en
1 citations

Abstract

Multifarious applications of unmanned aerial vehicles (UAVs) are thriving in extensive fields and facilitating our lives. However, the potential third-party risks (TPRs) on the ground are neglected by developers and companies, which limits large-scale commercialization. Risk assessment is an efficacious method for mitigating TPRs before undertaking flight tasks. This article incorporates the probability of UAV crashing into the TPR assessment model and employs an A* path-planning algorithm to optimize the trade-off between operational TPR cost and economic cost, thereby maximizing overall benefits. Experiments demonstrate the algorithm outperforms both the best-first-search algorithm and Dijkstra's algorithm. In comparison with the path with the least distance, initially, the trade-off results in a <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mn>1.88</mml:mn> <mml:mo>%</mml:mo></mml:mrow> <mml:annotation>$1.88\%$</mml:annotation></mml:semantics> </mml:math> increase in distance while achieving an <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mn>89.47</mml:mn> <mml:mo>%</mml:mo></mml:mrow> <mml:annotation>$89.47\%$</mml:annotation></mml:semantics> </mml:math> reduction in TPR. As the trade-off progresses, this relationship shifts, leading to a <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mn>20.62</mml:mn> <mml:mo>%</mml:mo></mml:mrow> <mml:annotation>$20.62\%$</mml:annotation></mml:semantics> </mml:math> reduction in the distance with only a negligible increase in TPR by 0.0001, matching the TPR-cost-based algorithm. Furthermore, we conduct simulations on the configuration of UAV path networks in five major cities in China based on real-world travel data and building data. Results reveal that the networks consist of one-way paths that are staggered in height. Moreover, in coastal cities particularly, the networks tend to extend over the sea, where the TPR cost is trivial.

Risk AnalysisVol. 45(7)
Tsinghua University (CN)
National Natural Science Foundation of China
Sustainable cities and communities, Life below water
Openalex Percentile: Top 11%
Air Traffic Management and Optimization
1.30
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