The prevalence of Unmanned Aerial Vehicles in precision agriculture has been growing rapidly. This paper tackles the UAV global mission planning problem by first incorporating a greater capacity for human-machine teaming in the design of a flexibly autonomous, near-fully-distributed Mission Management System for UAV swarms. Subsequently, to maximize the efficiency with which missions are carried out, the two problems of global mission planning: task assignment/routing and path planning, were

more »

... ved together, for small problem sizes, by an integrated solution. This consists of a geometric clustering algorithm which prioritizes the minimization of overall mission time, and an off-policy, modelfree 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.