Adaptive UAV Swarm Mission Planning by Temporal Difference Learning
Shreevanth Krishnaa Gopalakrishnan, Saba Jasim Al-Rubaye, Gökhan İnalhan · 2021
The 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.