Adaptive conflict resolution for multi-UAV 4D routes optimization using stochastic fractal search algorithm

The increasing unmanned aircraft system (UAS) applications in urban environments pose challenges for safe and efficient low altitude air traffic management. As an essential enabler to meet these challenges, pre-flight 4D routes optimization is required to conduct conflict detection and resolution (C...

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Main Authors: Pang, Bizhao, Low, Kin Huat, Lv, Chen
Other Authors: School of Mechanical and Aerospace Engineering
Format: Article
Language:English
Published: 2022
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Online Access:https://hdl.handle.net/10356/156316
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1563162022-05-14T20:10:23Z Adaptive conflict resolution for multi-UAV 4D routes optimization using stochastic fractal search algorithm Pang, Bizhao Low, Kin Huat Lv, Chen School of Mechanical and Aerospace Engineering Air Traffic Management Research Institute Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Unmanned Aircraft System Conflict Resolution Mixed-Integer Nonlinear Programming Third-Party Risk Urban Environments The increasing unmanned aircraft system (UAS) applications in urban environments pose challenges for safe and efficient low altitude air traffic management. As an essential enabler to meet these challenges, pre-flight 4D routes optimization is required to conduct conflict detection and resolution (CD&R) and to generate conflict-free flight routes before departure. Existing studies on strategic deconfliction cover several types of strategies such as scheduling or rerouting. However, a single type of strategy used to solve different types of conflicts may lead to an unsafe and inefficient way of conflict resolution. This paper proposes an adaptive decision-making framework to optimize the resolution strategies used for different types of conflicts with explainable mechanisms. The proposed framework is formulated as a double-layer optimization problem with the considerations of scheduling, speed adjustment, and rerouting strategies for conflict resolution. The first layer of the framework is established as a probabilistic selection model to make decisions on which strategy should be selected for what type of conflict. The second layer is developed as a mixed-integer nonlinear programming (MINLP) model to optimize the decision variables of the strategies selected by the first layer. To solve the proposed double-layer optimization problem, we introduce and improve a novel meta-heuristic stochastic fractal search (SFS) algorithm with two major improvements of a penalty-guided fitness function and an exploitation-exploration balancing scheme. Simulation results demonstrate that the proposed adaptive conflict resolution framework successfully optimizes the strategies used for each type of flight conflict, which subsequently optimizes the 4D routes with significant reductions in total operational cost, number of flight conflicts, and flight delays. The improved stochastic fractal search (ISFS) algorithm is also proved effective and reliable in solving the proposed optimization problem in different traffic density scenarios. Civil Aviation Authority of Singapore (CAAS) National Research Foundation (NRF) Submitted/Accepted version This research is supported by the National Research Foundation (NRF), Singapore, and the Civil Aviation Authority of Singapore (CAAS), under the Aviation Transformation Programme (ATP). 2022-05-09T00:26:44Z 2022-05-09T00:26:44Z 2022 Journal Article Pang, B., Low, K. H. & Lv, C. (2022). Adaptive conflict resolution for multi-UAV 4D routes optimization using stochastic fractal search algorithm. Transportation Research Part C: Emerging Technologies, 139, 103666-. https://dx.doi.org/10.1016/j.trc.2022.103666 0968-090X https://hdl.handle.net/10356/156316 10.1016/j.trc.2022.103666 139 103666 en Transportation Research Part C: Emerging Technologies © 2022 Elsevier Ltd. All rights reserved. This paper was published in Transportation Research Part C: Emerging Technologies and is made available with permission of Elsevier Ltd. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Unmanned Aircraft System
Conflict Resolution
Mixed-Integer Nonlinear Programming
Third-Party Risk
Urban Environments
spellingShingle Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Unmanned Aircraft System
Conflict Resolution
Mixed-Integer Nonlinear Programming
Third-Party Risk
Urban Environments
Pang, Bizhao
Low, Kin Huat
Lv, Chen
Adaptive conflict resolution for multi-UAV 4D routes optimization using stochastic fractal search algorithm
description The increasing unmanned aircraft system (UAS) applications in urban environments pose challenges for safe and efficient low altitude air traffic management. As an essential enabler to meet these challenges, pre-flight 4D routes optimization is required to conduct conflict detection and resolution (CD&R) and to generate conflict-free flight routes before departure. Existing studies on strategic deconfliction cover several types of strategies such as scheduling or rerouting. However, a single type of strategy used to solve different types of conflicts may lead to an unsafe and inefficient way of conflict resolution. This paper proposes an adaptive decision-making framework to optimize the resolution strategies used for different types of conflicts with explainable mechanisms. The proposed framework is formulated as a double-layer optimization problem with the considerations of scheduling, speed adjustment, and rerouting strategies for conflict resolution. The first layer of the framework is established as a probabilistic selection model to make decisions on which strategy should be selected for what type of conflict. The second layer is developed as a mixed-integer nonlinear programming (MINLP) model to optimize the decision variables of the strategies selected by the first layer. To solve the proposed double-layer optimization problem, we introduce and improve a novel meta-heuristic stochastic fractal search (SFS) algorithm with two major improvements of a penalty-guided fitness function and an exploitation-exploration balancing scheme. Simulation results demonstrate that the proposed adaptive conflict resolution framework successfully optimizes the strategies used for each type of flight conflict, which subsequently optimizes the 4D routes with significant reductions in total operational cost, number of flight conflicts, and flight delays. The improved stochastic fractal search (ISFS) algorithm is also proved effective and reliable in solving the proposed optimization problem in different traffic density scenarios.
author2 School of Mechanical and Aerospace Engineering
author_facet School of Mechanical and Aerospace Engineering
Pang, Bizhao
Low, Kin Huat
Lv, Chen
format Article
author Pang, Bizhao
Low, Kin Huat
Lv, Chen
author_sort Pang, Bizhao
title Adaptive conflict resolution for multi-UAV 4D routes optimization using stochastic fractal search algorithm
title_short Adaptive conflict resolution for multi-UAV 4D routes optimization using stochastic fractal search algorithm
title_full Adaptive conflict resolution for multi-UAV 4D routes optimization using stochastic fractal search algorithm
title_fullStr Adaptive conflict resolution for multi-UAV 4D routes optimization using stochastic fractal search algorithm
title_full_unstemmed Adaptive conflict resolution for multi-UAV 4D routes optimization using stochastic fractal search algorithm
title_sort adaptive conflict resolution for multi-uav 4d routes optimization using stochastic fractal search algorithm
publishDate 2022
url https://hdl.handle.net/10356/156316
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