A Fuzzy Cooperative Localisation Framework for Underwater Robotic Swarms.

Adham Sabra,Wai-Keung Fung

doi:10.3390/s20195496

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.

Highlights

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]
The proposed method can be extended to accommodate some other newly developed localisation methods by expanding the fuzzy rule base and, better scalability is obtained with increasing swarm size
The proposed fuzzy-based localisation algorithm greatly improves the entire swarm standard deviation by 35.17% at swarm size of 150 Autonomous Underwater Vehicle (AUV) when compared to the round-robin Extended Kalman Filter (EKF)-based method

Summary

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 [2,3,4,5], there has been little investigation of underwater robotic swarms. 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 challenging, due to the high cost of maritime assets and limited bandwidth of underwater acoustic communication channel

Methods

Results

Conclusion