Gopalakrishnan, Shreevanth KrishnaaAl-Rubaye, SabaInalhan, Gokhan2021-12-082021-12-082021-11-15Gopalakrishnan SK, Al-Rubaye S, Inalhan G. (2021) Adaptive UAV swarm mission planning by temporal difference learning. In: Proceedings of the 2021 AIAA/IEEE 40th Digital Avionics Systems Conference (DASC), 3-7 October 2021, San Antonio, America.978-1-6654-3421-82155-7195https://doi.org/10.1109/DASC52595.2021.9594300https://dspace.lib.cranfield.ac.uk/handle/1826/17325The prevalence of Unmanned Aerial Vehicles (UAVs) in precision agriculture has been growing rapidly. This paper tackles the UAV global mission planning problem by incorporating a greater capacity for human-machine teaming in the architecture of a flexibly autonomous, near-fully-distributed Mission Management System for UAV swarms. Subsequently, the two problems of global mission planning are solved simultaneously using an integrated solution. This consists of a geometric clustering algorithm which prioritizes the minimization of overall mission time, and an off-policy, model-free Temporal Difference Learning global agent capable of learning about an initially unknown mission environment through simulations. The latter component makes the solution adaptive to missions with different requirements.enAttribution-NonCommercial 4.0 Internationalhttp://creativecommons.org/licenses/by-nc/4.0/Reinforcement LearningTemporal Difference LearningUAVGlobal Mission PlanningPrecision AgricultureConference paper978-1-6654-3420-12155-7209