RT Book, Section
T1 GA-guided task planning for multiple-haps in realistic time-varying operation environments
A1 Kiam, Jane Jean
A1 Besada Portas, Eva
A1 Hehtke, Valerie
A1 Schulte, Axel
AB 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.
PB Association for Computing Machinery
SN 978-1-4503-6111-8
YR 2019
FD 2019-07
LK https://hdl.handle.net/20.500.14352/14043
UL https://hdl.handle.net/20.500.14352/14043
LA eng
NO ©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)
DS Docta Complutense
RD 17 abr 2025