Cooperative localisation in underwater robotic swarms for ocean bottom seismic imaging.

Spatial information must be collected alongside the data modality of interest in wide variety of sub-sea applications, such as deep sea exploration, environmental monitoring, geological and ecological research, and samples collection. Ocean-bottom seismic surveys are vital for oil and gas exploration, and for productivity enhancement of an existing production facility. Ocean-bottom seismic sensors are deployed on the seabed to acquire those surveys. Node deployment methods used in industry today are costly, time-consuming and unusable in deep oceans. This study proposes the autonomous deployment of ocean-bottom seismic nodes, implemented by a swarm of Autonomous Underwater Vehicles (AUVs). In autonomous deployment of ocean-bottom seismic nodes, a swarm of sensor-equipped AUVs are deployed to achieve ocean-bottom seismic imaging through collaboration and communication. However, the severely limited bandwidth of underwater acoustic communications and the high cost of maritime assets limit the number of AUVs that can be deployed for experiments. A holistic fuzzy-based localisation framework for large underwater robotic swarms (i.e. with hundreds of AUVs) to dynamically fuse multiple position estimates of an autonomous underwater vehicle is proposed. Simplicity, exibility and scalability are the main three advantages inherent in the proposed localisation framework, when compared to other traditional and commonly adopted underwater localisation methods, such as the Extended Kalman Filter. The proposed fuzzy-based localisation algorithm improves the entire swarm mean localisation error and standard deviation (by 16.53% and 35.17% respectively) at a swarm size of 150 AUVs when compared to the Extended Kalman Filter based localisation with round-robin scheduling. The proposed fuzzy based localisation method requires fuzzy rules and fuzzy set parameters tuning, if the deployment scenario is changed. Therefore a cooperative localisation scheme that relies on a scalar localisation confidence value is proposed. A swarm subset is navigationally aided by ultra-short baseline and a swarm subset (i.e. navigation beacons) is configured to broadcast navigation aids (i.e. range-only), once their confidence values are higher than a predetermined confidence threshold. The confidence value and navigation beacons subset size are two key parameters for the proposed algorithm, so that they are optimised using the evolutionary multi-objective optimisation algorithm NSGA-II to enhance its localisation performance. Confidence value-based localisation is proposed to control the cooperation dynamics among the swarm agents, in terms of aiding acoustic exteroceptive sensors. Given the error characteristics of a commercially available ultra-short baseline system and the covariance matrix of a trilaterated underwater vehicle position, dead reckoning navigation - aided by Extended Kalman Filter-based acoustic exteroceptive sensors - is performed and controlled by the vehicle's confidence value. The proposed confidence-based localisation algorithm has significantly improved the entire swarm mean localisation error when compared to the fuzzy-based and round-robin Extended Kalman Filter-based localisation methods (by 67.10% and 59.28% respectively, at a swarm size of 150 AUVs). The proposed fuzzy-based and confidence-based localisation algorithms for cooperative underwater robotic swarms are validated on a co-simulation platform. A physics-based co-simulation platform that considers an environment's hydrodynamics, industrial grade inertial measurement unit and underwater acoustic communications characteristics is implemented for validation and optimisation purposes

Dynamic localization plan for underwater mobile sensor nodes using fuzzy decision support system.

Underwater mobile sensor node localization is a key enabling technology for several subsea missions. A novel scalable underwater localization scheme, called Best Suitable Localization Algorithm (BLSA), is proposed to dynamically fuse multiple position estimates of sensor nodes using fuzzy logic, aiming at improving localization accuracy and availability along the whole trajectory in missions. Numerical simulation has been conducted to demonstrate significant improvement in localization accuracy and availability by using the proposed fuzzy inference system. The proposed method provides a costeffective localization system by harnessing all available localization methods on-board

A Fuzzy Cooperative Localisation Framework for Underwater Robotic Swarms

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

Sliding mode control with disturbance estimation for underwater robot.

This paper proposes a sliding mode control with a disturbance estimation for an underwater robot. The mobility performance of an underwater robot is influenced by modeling error, observation noise, and several disturbances such as ocean current and tidal current. Therefore, a robust control system is needed for precise motion control of an underwater robot. This paper uses a sliding mode control, which is one of the robust control methods. In a sliding mode control, chattering tends to occur, if the switching gain is set to a high value. On the other hand, it is desirable to set the switching gain high from the viewpoint of robustness. Therefore, there is a trade-off between the switching gain and robustness. In the proposed method, the disturbance is estimated in real-time, and this estimated value is added to the control input. Most of the disturbances are compensated by this estimated value, and the sliding mode control is used for the rest of the disturbances. As a result, the robust control system is achieved by using the proposed method, even if the switching gain is set to a low value. The validity of the proposed method was confirmed from the simulation and experimental results

Confidence-based Underwater Localization Scheme for Large-Scale Mobile Sensor Networks

The absence of Global Positioning System in underwater environment predominates in the challenges of underwater vehicles navigation or sensor nodes tracking. Localization of single or few underwater vehicles has been fostered in recent years. However, online simultaneous tracking of large-scale mobile sensor network is still a very challenging research area due to the high cost and the very limited number of vehicles that can be simultaneously localized using Ultra-Short Base Line (USBL) system. We propose a confidence-based localization algorithm for large-scale underwater mobile sensor networks that employs high precision localized sensor nodes in neighboring sensor nodes localization. Numerical simulation shows that a swarm of 100 sensor nodes can be tracked using a single USBL system, range measurement sensors and communication modems

Confidence-based underwater localization scheme for large-scale mobile sensor networks.

The absence of Global Positioning System in underwater environment predominates in the challenges of underwater vehicles navigation or sensor nodes tracking. Localization of single or few underwater vehicles has been fostered in recent years. However, online simultaneous tracking of large-scale mobile sensor network is still a very challenging research area due to the high cost and the very limited number of vehicles that can be simultaneously localized using Ultra-Short Base Line (USBL) system. We propose a confidence-based localization algorithm for large-scale underwater mobile sensor networks that employs high precision localized sensor nodes in neighboring sensor nodes localization. Numerical simulation shows that a swarm of 100 sensor nodes can be tracked using a single USBL system, range measurement sensors and communication modems