LoCUS: A multi-robot loss-tolerant algorithm for surveying volcanic plumes

TL;DR

LoCUS is a robust multi-robot algorithm designed for volcanic plume surveying, enabling drones to coordinate and self-heal despite frequent drone losses, outperforming independent solutions in simulations.

Contribution

The paper introduces LoCUS, a novel multi-robot, loss-tolerant algorithm with self-healing capabilities for volcanic gas measurement, demonstrating improved reliability over existing independent methods.

Findings

LoCUS outperforms MoBS in drone simulation tests.

LoCUS maintains survey accuracy despite drone failures.

The underlying data structures have broader fault-tolerant applications.

Abstract

Measurement of volcanic CO2 flux by a drone swarm poses special challenges. Drones must be able to follow gas concentration gradients while tolerating frequent drone loss. We present the LoCUS algorithm as a solution to this problem and prove its robustness. LoCUS relies on swarm coordination and self-healing to solve the task. As a point of contrast we also implement the MoBS algorithm, derived from previously published work, which allows drones to solve the task independently. We compare the effectiveness of these algorithms using drone simulations, and find that LoCUS provides a reliable and efficient solution to the volcano survey problem. Further, the novel data-structures and algorithms underpinning LoCUS have application in other areas of fault-tolerant algorithm research.