Dynamic and Distributed Optimization for the Allocation of Aerial Swarm Vehicles

TL;DR

This paper introduces a dynamic, distributed optimal transport algorithm for UAV swarms that enables efficient, adaptable task allocation based on environmental parameters, removing the need for centralized control.

Contribution

It presents a novel dynamic and distributed OT algorithm tailored for UAV swarms, allowing real-time, parameter-based task matching without centralized planning.

Findings

The algorithm converges reliably in simulations.

It outperforms greedy assignment methods.

It adapts to environmental changes effectively.

Abstract

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.