Article
A Fuzzy Cooperative Localisation Framework for
Underwater Robotic Swarms
Adham Sabra
1
and Wai-Keung Fung
1,2,
*
1
School of Engineering, Robert Gordon University, Aberdeen AB10 7GJ, UK; a.a.k.sabra@rgu.ac.uk
2
Cardiff School of Technologies, Cardiff Metropolitan University, Llandaff Campus, Cardiff CF5 2YB, UK
* Correspondence: WFung@cardiffmet.ac.uk
Received: 18 August 2020; Accepted: 20 September 2020; Published: 25 September 2020
Abstract:
This article proposes a holistic localisation framework for underwater robotic swarms
to dynamically fuse multiple position estimates of an autonomous underwater vehicle while
using fuzzy decision support system. A number of underwater localisation methods have been
proposed in the literature for wireless sensor networks. The proposed navigation framework
harnesses the established localisation methods in order to provide navigation aids in the absence of
acoustic exteroceptive sensors navigation aid (i.e., ultra-short base line) and it can be extended to
accommodate newly developed localisation methods by expanding the fuzzy rule base. Simplicity,
flexibility, and scalability are the main three advantages that are inherent in the proposed localisation
framework when compared to other traditional and commonly adopted underwater localisation
methods, such as the Extended Kalman Filter. A physics-based simulation platform that considers
environment’s hydrodynamics, industrial grade inertial measurement unit, and underwater acoustic
communications characteristics is implemented in order to validate the proposed localisation
framework on a swarm size of 150 autonomous underwater vehicles. The proposed fuzzy-based
localisation algorithm improves the entire swarm mean localisation error and standard deviation by
16.53% and 35.17%, respectively, when compared to the Extended Kalman Filter based localisation
with round-robin scheduling.
Keywords:
underwater wireless sensor networks; underwater swarm robotics; autonomous
underwater vehicles; underwater localisation; cooperative navigation; fuzzy systems
1. Introduction
Over the past two decades, swarm robotics have been widely investigated and robotic swarms
have been proven to be more efficient in solving complicated tasks or tasks that require wide
spatial coverage than a single overly complicated robot [
1
]. While aerial and terrestrial swarm
robotics have been extensively investigated [
2
–
5
], there has been little investigation of underwater
robotic swarms. Swarm connectivity is a primary concern of any swarm system, which is realised
by intra-swarm communication to enable nodes collaboration. Intra-swarm communication can
be achieved in either direct or indirect fashion. Radio and acoustic links are examples of direct
communication, whereas indirect communication occurs through the environment, such as stigmergic
collaboration [
6
]. Underwater robotic swarm deployment is particularly challenging, due to the
high cost of maritime assets and limited bandwidth of underwater acoustic communication channel.
The wide variety of marine missions that can be achieved by means of mobile underwater sensor
networks (i.e., underwater swarm robotics), such as deep sea exploration and environmental
monitoring, have enabled and motivated underwater robotics research for decades [
7
]. Localisation
is one of the most critical problems in robotic swarms, as it is required to be successfully obtained in
advance of nodes’ guidance and control. The navigation module of an autonomous node estimates
Sensors 2020, 20, 5496; doi:10.3390/s20195496 www.mdpi.com/journal/sensors
