On rapid parallel tuning of controllers of a swarm of MAVs -- distribution strategies of the updated gains

TL;DR

This paper introduces a scalable, model-free method for rapidly tuning controllers in MAV swarms by leveraging parallel strategies, improving tuning speed and reliability through averaging and parallel testing of gains.

Contribution

It presents two novel parallel tuning approaches for MAV swarms that enhance speed and robustness without relying on detailed models.

Findings

Improved tuning speed through parallel testing.

Enhanced robustness via averaging of performance indices.

Validated methods in both simulation and real-world MAV experiments.

Abstract

In this paper, we present a reliable, scalable, time deterministic, model-free procedure to tune swarms of Micro Aerial Vehicles (MAVs) using basic sensory data. Two approaches to taking advantage of parallel tuning are presented. First, the tuning with averaging of the results on the basis of performance indices reported from the swarm with identical gains to decrease the negative effect of the noise in the measurements. Second, the tuning with parallel testing of varying set of gains across the swarm to reduce the tuning time. The presented methods were evaluated both in simulation and real-world experiments. The achieved results show the ability of the proposed approach to improve the results of the tuning while decreasing the tuning time, ensuring at the same time a reliable tuning mechanism.