Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/155581
Title: UAV path optimization with an integrated cost assessment model considering third-party risks in metropolitan environments
Authors: Pang, Bizhao
Hu, Xinting
Dai, Wei
Low, Kin Huat
Keywords: Civil engineering:Transportation
Engineering::Aeronautical engineering::Aviation
Issue Date: 2022
Source: Pang, B., Hu, X., Dai, W. & Low, K. H. (2022). UAV path optimization with an integrated cost assessment model considering third-party risks in metropolitan environments. Reliability Engineering and System Safety, 222, 108399-. https://dx.doi.org/10.1016/j.ress.2022.108399
Project: 2018-T1-002-124 
Journal: Reliability Engineering and System Safety 
Abstract: Various applications of unmanned aerial vehicles (UAVs) in urban environments facilitate our daily life and public services. However, UAV operations bring third party risk (TPR) issues, as UAV may crash to pedestrians and vehicles on the ground. It may also cause property damages to critical infrastructures and noise impacts to the public. Path planning is an effective method to mitigate these risks and impacts by avoiding high-risk areas before flight. However, most of the existing path planning methods focus on minimizing flight distance or energy cost, rarely considered risk cost. This paper develops a novel flight path optimization method that considers an integrated cost assessment model. The assessment model incorporates fatality risk, property damage risk, and noise impact, which is an extension of current TPR indicators at modeling and assessment levels. To solve the proposed integrated cost-based path optimization problem, a hybrid estimation of distribution algorithm (EDA) and CostA* (named as EDA-CostA*) algorithm is proposed, which provides both global and local heuristic in- formation for path searching in cost-based environments. A downtown area in Singapore is selected for the case study. Simulation results demonstrate the effectiveness of the developed cost-based path optimization model in reducing the risk cost. The statistical analysis for 100 sampled environments also shows the reliability of the proposed method, which reduced the cost by [42.64%, 44.15%] at 95% confidence level.
URI: https://hdl.handle.net/10356/155581
ISSN: 0951-8320
DOI: 10.1016/j.ress.2022.108399
Rights: © 2022 Elsevier Ltd.]. All rights reserved. This paper was published in Reliability Engineering and System Safety and is made available with permission of Elsevier Ltd.
Fulltext Permission: embargo_20240707
Fulltext Availability: With Fulltext
Appears in Collections:ATMRI Journal Articles
MAE Journal Articles

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  Until 2024-07-07
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