  Optimal transport (OT) is a framework that can guide the design of efficient
resource allocation strategies in a network of multiple sources and targets.
This paper applies discrete OT to a swarm of UAVs in a novel way to achieve
appropriate task allocation and execution. Drone swarm deployments already
operate in multiple domains where sensors are used to gain knowledge of an
environment [1]. Use cases such as, chemical and radiation detection, and
thermal and RGB imaging create a specific need for an algorithm that considers
parameters on both the UAV and waypoint side and allows for updating the
matching scheme as the swarm gains information from the environment.
Additionally, the need for a centralized planner can be removed by using a
distributed algorithm that can dynamically update based on changes in the swarm
network or parameters. To this end, we develop a dynamic and distributed OT
algorithm that matches a UAV to the optimal waypoint based on one parameter at
the UAV and another parameter at the waypoint. We show the convergence and
allocation of the algorithm through a case study and test the algorithm's
effectiveness against a greedy assignment algorithm in simulation.
