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GA-guided task planning for multiple-haps in realistic time-varying operation environments

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Kiam, Jane Jean and Besada Portas, Eva and Hehtke, Valerie and Schulte, Axel (2019) GA-guided task planning for multiple-haps in realistic time-varying operation environments. In Proceedings of the 2019 Genetic and Evolutionary Computation Conference (GECCO'19). Association for Computing Machinery, Nueva York, pp. 1232-1240. ISBN 978-1-4503-6111-8

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Official URL: http://dx.doi.org/10.1145/3321707.3321768


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Abstract

High-Altitude Pseudo-Satellites (HAPS) are long-endurance, fixed-wing, lightweight Unmanned Aerial Vehicles (UAVs) that operate in the stratosphere and offer a flexible alternative for ground activity monitoring/imaging at specific time windows. As their missions must be planned ahead (to let them operate in controlled airspace), this paper presents a Genetic Algorithm (GA)-guided Hierarchical Task Network (HTN)-based planner for multiple HAPS. The HTN allows to compute plans that conform with airspace regulations and operation protocols. The GA copes with the exponentially growing complexity (with the number of monitoring locations and involved HAPS) of the combinatorial problem to search for an optimal task decomposition (that considers the time-dependent mission requirements and the time-varying environment). Besides, the GA offers a flexible way to handle the problem constraints and optimization criteria: the former encodes the airspace regulations, while the latter measures the client satisfaction, the operation efficiency and the normalized expected mission reward (that considers the wind effects in the uncertainty of the arrival-times at the monitoring-locations). Finally, by integrating the GA into the HTN planner, the new approach efficiently finds overall good task decompositions, leading to satisfactory task plans that can be executed reliably (even in tough environments), as the results in the paper show.


Item Type:Book Section
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©2019 Copyright held by the owner/author(s). Publication rights licensed to the Association for Computing Machinery.
Genetic and Evolutionary Computation Conference (GECCO)(2019. Praga)

Uncontrolled Keywords:Genetic algorithms; Multiobjective constrained optimization; Mission task-planning; Haps;
Subjects:Sciences > Computer science > Artificial intelligence
ID Code:60837
Deposited On:12 Jun 2020 11:36
Last Modified:19 Sep 2020 17:56

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