Heuristics for optimizing 3D mapping missions over swarm-powered ad hoc clouds

TL;DR

This paper introduces heuristics for optimizing 3D mapping missions using drone swarms and ad hoc clouds, significantly reducing reconstruction time in emergency scenarios.

Contribution

It proposes a novel mathematical programming heuristic and a variable neighborhood search heuristic for efficient 3D mapping in swarm-powered ad hoc clouds.

Findings

Heuristics quickly reach optimal solutions or improve known solutions.

Effective for instances with up to 1000 images and 15 drones.

Enhances timeliness of 3D maps in emergency response.

Abstract

Drones have been getting more and more popular in many economy sectors. Both scientific and industrial communities aim at making the impact of drones even more disruptive by empowering collaborative autonomous behaviors -- also known as swarming behaviors -- within fleets of multiple drones. In swarming-powered 3D mapping missions, unmanned aerial vehicles typically collect the aerial pictures of the target area whereas the 3D reconstruction process is performed in a centralized manner. However, such approaches do not leverage computational and storage resources from the swarm members.We address the optimization of a swarm-powered distributed 3D mapping mission for a real-life humanitarian emergency response application through the exploitation of a swarm-powered ad hoc cloud. Producing the relevant 3D maps in a timely manner, even when the cloud connectivity is not available, is crucial to increase the chances of success of the operation. In this work, we present a mathematical programming heuristic based on decomposition and a variable neighborhood search heuristic to minimize the completion time of the 3D reconstruction process necessary in such missions. Our computational results reveal that the proposed heuristics either quickly reach optimality or improve the best known solutions for almost all tested realistic instances comprising up to 1000 images and fifteen drones.