Modular AUV System with Integrated Real-Time Water Quality Analysis

Prototyping a low-cost open-source autonomous unmanned surface vehicle for real-time water quality monitoring and visualization

A low-cost open-source autonomous unmanned surface vehicle (USV) named "iDroneboat" is developed for real-time monitoring and visualization of water quality. The iDroneboat equipped with Internet of Things (IoT) sensors transmits real-time water quality data, including dissolved oxygen (DO), electronical conductivity (EC), pH, and water temperature (WT) to the cloud for data sharing through Long-term Evolution (LTE) communication protocols. Since material and supplies needed are readily accessible from online marketplaces or local hardware stores, the iDroneboat is easily replicable for local water quality studies and citizen-science activities. The iDroneboat appears to be a promising tool to advance environmental research activities, especially for impaired waterways (e.g., lakes, rivers, and reservoirs). The preliminary result shows that the proposed low-cost platform, iDroneboat, effectively displays water quality components in real-time to the cloud web services (e.g., ThingSpeak), ultimately contributing to citizen science activities and environmental stewardship in water research ecosystems.

Adaptive control for follower gliders mapping underwater oil patches

Adaptive control was applied to follower gliders in cooperating multiple glider teams on missions to delineate underwater oil patches. The influence of water currents on the motion of the oil patches was included. The cooperation strategy with adaptive control was compared with strategies without cooperation or adaptive control through simulation experiments. In addition, the optimal number of follower gliders in a team was assessed. From the simulations, strategies with adaptive control achieved a higher score of performance, defined as a measure of the percentage of valuable-rich information collected to the percentage of the mission area covered by information-rich patches; this measure was applied when the percentage of the area of information-rich patches was less than 60%. The cooperation strategy with adaptive control had a lower duty cycle and a longer mission duration, but had the best score of performance, especially for long-duration missions. Backseat driver hardware was installed on a Slocum glider to support adaptive control and a field experiment successfully realized cooperation between a simulated scout glider and the follower glider and the adaptive control of the follower. The experiment also indicated the limitations of timely completion of missions owing to the operating speed of the glider relative to that of ambient currents.

UAS-based real-time water quality monitoring, sampling, and visualization platform (UASWQP)

Urbanization, land use change, and agricultural activities continue to affect water quality standards at the urban-rural interface, such as the Boise River System located in Idaho, USA. This project demonstrates how the off-the-shelf unmanned aircraft system (UAS, also known as drone) equipped with other necessary hardware attachments can be used to monitor real-time water quality components, including pH, water temperature, electric conductivity (EC), and dissolved oxygen at open waterbodies. The proposed UAS-based hardware platform for water quality studies (UASWQP) appears a promising tool to advance environmental research activities, especially for impaired waterways (e.g., rivers, lakes, and reservoirs). The preliminary result shows that the proposed UASWQP effectively displays water quality components in real-time to the ThingSpeak Cloud web services, while an adequate water sample was also collected easily for further analysis at laboratory facilities, when needed. It is anticipated that UASWQP will be a useful tool to promote environmental stewardship by contributing to the water research communities in years to come.

Artificial Intelligence Search Strategies for Autonomous Underwater Vehicles Applied for Submarine Groundwater Discharge Site Investigation

In this study, a set of different search strategies for locating submarine groundwater discharge (SGD) are investigated. This set includes pre-defined path planning (PPP), adapted random walk (RW), particle swarm optimisation (PSO), inertia Levy-flight (ILF), self-organising-migration-algorithm (SOMA), and bumblebee search algorithm (BB). The influences of self-localisation and communication errors and limited travel distance of the autonomous underwater vehicles (AUVs) on the performance of the proposed algorithms are investigated. This study shows that the proposed search strategies could not outperform the classic search heuristic based on full coverage path planning if all AUVs followed the same search strategy. In this study, the influence of self-localisation and communication errors was investigated. The simulations showed that, based on the median error of the search runs, the performance of SOMA was in the same order of magnitude regardless the strength of the localisation error. Furthermore, it was shown that the performance of BB was highly affected by increasing localisation errors. From the simulations, it was revealed that all the algorithms, except for PSO and SOMA, were unaffected by disturbed communications. Here, the best performance was shown by PPP, followed by BB, SOMA, ILF, PSO, and RW. Furthermore, the influence of the limited travel distances of the AUVs on the search performance was evaluated. It was shown that all the algorithms, except for PSO, were affected by the shorter maximum travel distances of the AUVs. The performance of PPP increased with increasing maximum travel distances. However, for maximum travel distances > 1800 m the median error appeared constant. The effect of shorter travel distances on SOMA was smaller than on PPP. For maximum travel distances < 1200 m, SOMA outperformed all other strategies. In addition, it can be observed that only BB showed better performances for shorter travel distances than for longer ones. On the other hand, with different search strategies for each AUV, the search performance of the whole swarm can be improved by incorporating population-based search strategies such as PSO and SOMA within the PPP scheme. The best performance was achieved for the combination of two AUVs following PPP, while the third AUV utilised PSO. The best fitness of this combination was 15.9. This fitness was 26.4% better than the performance of PPP, which was 20.4 on average. In addition, a novel mechanism for dynamically selecting a search strategy for an AUV is proposed. This mechanism is based on fuzzy logic. This dynamic approach is able to perform at least as well as PPP and SOMA for different travel distances of AUVs. However, due to the better adaptation to the current situation, the overall performance, calculated based on the fitness achieved for different maximum travel distances, the proposed dynamic search strategy selection performed 32.8% better than PPP and 34.0% better than SOMA.

Water Quality Monitoring and Management of Building Water Tank Using Industrial Internet of Things

Water being one of the foremost needs for human survival, conservation, and management of the resource must be given ultimate significance. Water demand has increased tremendously all over the world from the past decade due to urbanization, climatic change, and ineffective management of water. The advancement in sensor and wireless communication technology encourages implementing the IoT in a wide range. In this study, an IoT-based architecture is proposed and implemented for monitoring the level and quality of water in a domestic water tank with customized hardware based on 2.4 GHz radiofrequency (RF) communication. Moreover, the ESP 8266 Wi-Fi module-based upper tank monitoring of the proposed architecture encourages provide real-time information about the tank through internet protocol (IP). The customized hardware is designed and evaluated in the Proteus simulation environment. The calibration of the pH sensor and ultrasonic value is carried out for setting the actual value in the prototype for obtaining the error-free value. The customized hardware that is developed for monitoring the level and quality of water is implemented. The real-time visualization and monitoring of the water tank are realized with the cloud-enabled Virtuino app.

A Dimensional Comparison between Evolutionary Algorithm and Deep Reinforcement Learning Methodologies for Autonomous Surface Vehicles with Water Quality Sensors

The monitoring of water resources using Autonomous Surface Vehicles with water-quality sensors has been a recent approach due to the advances in unmanned transportation technology. The Ypacaraí Lake, the biggest water resource in Paraguay, suffers from a major contamination problem because of cyanobacteria blooms. In order to supervise the blooms using these on-board sensor modules, a Non-Homogeneous Patrolling Problem (a NP-hard problem) must be solved in a feasible amount of time. A dimensionality study is addressed to compare the most common methodologies, Evolutionary Algorithm and Deep Reinforcement Learning, in different map scales and fleet sizes with changes in the environmental conditions. The results determined that Deep Q-Learning overcomes the evolutionary method in terms of sample-efficiency by 50-70% in higher resolutions. Furthermore, it reacts better than the Evolutionary Algorithm in high space-state actions. In contrast, the evolutionary approach shows a better efficiency in lower resolutions and needs fewer parameters to synthesize robust solutions. This study reveals that Deep Q-learning approaches exceed in efficiency for the Non-Homogeneous Patrolling Problem but with many hyper-parameters involved in the stability and convergence.

A comprehensive review of catchment water quality monitoring using a tiered framework of integrated sensing technologies

Due to the growing threat of climate change, new advances in water quality monitoring strategies are needed now more than ever. Reliable and robust monitoring practices can be used to improve and better understand catchment processes affecting the water quality. In recent years the deployment of long term in-situ sensors has increased the temporal and spatial data being obtained. Furthermore, the development and research into remote sensing using satellite and aerial imagery has been incrementally integrated into catchments for monitoring areas that previously might have been impossible to monitor, producing high-resolution data that has become imperative to catchment monitoring. The use of modelling in catchments has become relevant as it enables the prediction of events before they occur so that strategic plans can be put in place to deal with or prevent certain threats. This review highlights the monitoring approaches employed in catchments currently and examines the potential for integration of these methods. A framework might incorporate all monitoring strategies to obtain more information about a catchment and its water quality. The future of monitoring will involve satellite, in-situ and air borne devices with data analytics playing a key role in providing decision support tools. The review provides examples of successful use of individual technologies, some combined approaches and identifies gaps that should be filled to achieve an ideal catchment observation system.

ENDURUNS: An Integrated and Flexible Approach for Seabed Survey Through Autonomous Mobile Vehicles

The oceans cover more than two-thirds of the planet, representing the vastest part of natural resources. Nevertheless, only a fraction of the ocean depths has been explored. Within this context, this article presents the H2020 ENDURUNS project that describes a novel scientific and technological approach for prolonged underwater autonomous operations of seabed survey activities, either in the deep ocean or in coastal areas. The proposed approach combines a hybrid Autonomous Underwater Vehicle capable of moving using either thrusters or as a sea glider, combined with an Unmanned Surface Vehicle equipped with satellite communication facilities for interaction with a land station. Both vehicles are equipped with energy packs that combine hydrogen fuel cells and Li-ion batteries to provide extended duration of the survey operations. The Unmanned Surface Vehicle employs photovoltaic panels to increase the autonomy of the vehicle. Since these missions generate a large amount of data, both vehicles are equipped with onboard Central Processing units capable of executing data analysis and compression algorithms for the semantic classification and transmission of the acquired data.

Node Deployment of Marine Monitoring Networks: A Multiobjective Optimization Scheme

The increasing demands for real-time marine monitoring call for the wide deployment of Marine Monitoring Networks (MMNs). The low-rate underwater communications over a long distance, long propagation delay of underwater acoustic channel, and high deployment costs of marine sensors in a large-scale three-dimensional space bring great challenges in the network deployment and management of MMN. In this paper, we first propose a multitier, hierarchical network architecture of MMN with the support of edge computing (HMMN-EC) to enable efficient monitoring services in a harsh marine environment, taking into consideration the salient features of marine communications. Specifically, HMMN-EC is composed of three subnetworks, i.e., underwater acoustic subnetwork, the sea-surface wireless subnetwork, and the air wireless subnetwork, with a diversity of network nodes with different capabilities. We then jointly investigate the deployment diverse network nodes with various constraints in different subnetworks of HMMN-EC. To this end, we formulate a Multiobjective Optimization (MO) problem to minimize the network deployment cost while achieving the maximal network lifetime, subject to the limited energy of different marine nodes and the complex deployment environment. To solve the formulated problem, we present an Ant-Colony-based Efficient Topology Optimization (AC-ETO) algorithm to find the optimal locations of nodes in different subnetworks of MMN in a large-scale deployment. The time complexity of the proposed algorithm is also analyzed. Finally, extensive simulations are carried out to validate the superior performance of the proposed algorithm compared with some existing solutions.

Adaptive Water Sampling Device for Aerial Robots

Water quality monitoring and predicting the changes in water characteristics require the collection of water samples in a timely manner. Water sample collection based on in situ measurable water quality indicators can increase the efficiency and precision of data collection while reducing the cost of laboratory analyses. The objective of this research was to develop an adaptive water sampling device for an aerial robot and demonstrate the accuracy of its functions in laboratory and field conditions. The prototype device consisted of a sensor node with dissolved oxygen, pH, electrical conductivity, temperature, turbidity, and depth sensors, a microcontroller, and a sampler with three cartridges. Activation of water capturing cartridges was based on in situ measurements from the sensor node. The activation mechanism of the prototype device was tested with standard solutions in the laboratory and with autonomous water sampling flights over the 11-ha section of a lake. A total of seven sampling locations were selected based on a grid system. Each cartridge collected 130 mL of water samples at a 3.5 m depth. Mean water quality parameters were measured as 8.47 mg/L of dissolved oxygen, pH of 5.34, 7 µS/cm of electrical conductivity, temperature of 18 °C, and 37 Formazin Nephelometric Unit (FNU) of turbidity. The dissolved oxygen was within allowable limits that were pre-set in the self-activation computer program while the pH, electrical conductivity, and temperature were outside of allowable limits that were specified by Environmental Protection Agency (EPA). Therefore, the activation mechanism of the device was triggered and water samples were collected from all the sampling locations successfully. The adaptive water sampling with Unmanned Aerial Vehicle-assisted water sampling device was proved to be a successful method for water quality evaluation.

Design and Experiment of a Plateau Data-Gathering AUV

The design, control, and implementation of an autonomous underwater vehicle (AUV) for collecting hydrological information from plateau rivers and lakes are presented in this paper. The hardware and software structures of the control system were previously described. A novel sliding mode controller (SMC) with combinational reaching law of vertical hovering motion is proposed to improve the robustness and stability. The S-plane control, a nonlinear controller with little parameters, is used in the horizontal motion. Besides, the navigation strategy based on the dead-reckoning algorithm, a path tracking based on the light-of-sight (LOS) algorithm, and a control allocation strategy considering saturation are present. Finally, experiments were performed in a tank and in a river in the Qinghai–Tibet Plateau to prove the feasibility and reliability of the AUV system.

A Fast Binocular Localisation Method for AUV Docking

Docking technology plays a critical role in realising the long-time operation of autonomous underwater vehicles (AUVs). In this study, a binocular localisation method for AUV docking is presented. An adaptively weighted OTSU method is developed for feature extraction. The foreground object is extracted precisely without mixing or missing lamps, which is independent of the position of the AUV relative to the station. Moreover, this extraction process is more precise compared to other segmentation methods with a low computational load. The mass centre of each lamp on the binary image is used as matching feature for binocular vision. Using this fast feature matching method, the operation frequency of the binocular localisation method exceeds 10 Hz. Meanwhile, a relative pose estimation method is suggested for instances when the two cameras cannot capture all the lamps. The localisation accuracy of the distance in the heading direction as measured by the proposed binocular vision algorithm was tested at fixed points underwater. A simulation experiment using a ship model has been conducted in a laboratory pool to evaluate the feasibility of the algorithm. The test result demonstrates that the average localisation error is approximately 5 cm and the average relative location error is approximately 2% in the range of 3.6 m. As such, the ship model was successfully guided to the docking station for different lateral deviations.

Autonomous In Situ Measurements of Noncontaminant Water Quality Indicators and Sample Collection with a UAV

The objective of this research was to conduct in situ measurements of electrical conductivity (EC), pH, dissolved oxygen (DO), and temperature, and collect water samples simultaneously at different depths using an unmanned aerial vehicle (UAV). The UAV system consists of a hexacopter, water sampling cartridges (WSC), and a sensor node. Payload capacity and endurance of the UAV were determined using an indoor test station. The UAV was able to produce 106 N of thrust for 10 min with 6.3 kg of total takeoff weight. The thrust-to-weight ratio of the UAV was 2.5 at 50% throttle. The decision for activating the water sampling cartridges and sensor node was made autonomously from an onboard microcontroller. System functions were verified at 0.5 m and 3.0 m depths in 6 locations over a 1.1 ha agricultural pond. Average measurements of EC, pH, DO, and temperature at 0.5 m depth were 42 µS/cm, 5.6, 8.2 mg/L, and 31 °C, while the measurements at 3 m depth were 80 µS/cm, 5.3, 5.34 mg/L, and 24 °C, respectively. The UAV-assisted autonomous water sampling system (UASS) successfully activated the WSC at each sampling location. The UASS would reduce the duration of water quality assessment and help practitioners and researchers to conduct observations with lower operational costs. The developed system would be useful for sampling and monitoring of water reservoirs, lakes, rivers, and ponds periodically or after natural disasters.

Optimization of the Energy Consumption of Depth Tracking Control Based on Model Predictive Control for Autonomous Underwater Vehicles

For long-term missions in complex seas, the onboard energy resources of autonomous underwater vehicles (AUVs) are limited. Thus, energy consumption reduction is an important aspect of the study of AUVs. This paper addresses energy consumption reduction using model predictive control (MPC) based on the state space model of AUVs for trajectory tracking control. Unlike the previous approaches, which use a cost function that consists of quadratic deviations of the predicted controlled output from the reference trajectory and quadratic input changes, a term of quadratic energy (i.e., quadratic input) is introduced into the cost function in this paper. Then, the MPC control law with the new cost function is constructed, and an analysis on the effect of the quadratic energy term on the stability is given. Finally, simulation results for depth tracking control are given to demonstrate the feasibility and effectiveness of the improved MPC on energy consumption optimization for AUVs.