Use Of Autonomous Underwater Vehicles Research Articles

Experimental Analysis of Deep-Sea AUV Based on Multi-Sensor Integrated Navigation and Positioning

The operation of underwater vehicles in deep waters is a very challenging task. The use of AUVs (Autonomous Underwater Vehicles) is the preferred option for underwater exploration activities. They can be autonomously navigated and controlled in real time underwater, which is only possible with precise spatio-temporal information. Navigation and positioning systems based on LBL (Long-Baseline) or USBL (Ultra-Short-Baseline) systems have their own characteristics, so the choice of system is based on the specific application scenario. However, comparative experiments on AUV navigation and positioning under both systems are rarely conducted, especially in the deep sea. This study describes navigation and positioning experiments on AUVs in deep-sea scenarios and compares the accuracy of the USBL and LBL/SINS (Strap-Down Inertial Navigation System)/DVL (Doppler Velocity Log) modes. In practice, the accuracy of the USBL positioning mode is higher when the AUV is within a 60° observation range below the ship; when the AUV is far away from the ship, the positioning accuracy decreases with increasing range and observation angle, i.e., the positioning error reaches 80 m at 4000 m depth. The navigational accuracy inside and outside the datum array is high when using the LBL/SINS/DVL mode; if the AUV is far from the datum array when climbing to the surface, the LBL cannot provide accurate position calibration while the DVL fails, resulting in large deviations in the SINS results. In summary, the use of multi-sensor combination navigation schemes is beneficial, and accurate position information acquisition should be based on the demand and cost, while other factors should also be comprehensively considered; this paper proposes the use of the LBL/SINS/DVL system scheme.

IMAGE FEATURE EXTRACTION METHODS FOR STRUCTURE DETECTION FROM UNDERWATER IMAGERY

Abstract. The use of autonomous underwater vehicles (AUVs) for surveying underwater infrastructure presents a potential cost saving in comparison to remotely operated vehicles (ROVs). One of the challenges when processing images of underwater structures captured by an AUV, is that vast number of images captured during the mission usually do not show the structure. For instance, images captured during the dive to the structure or of the sea floor, or of the deep sea facing away from the structure. Too many images captured, without relevant information for a 3D reconstruction of the structure, leads to increased processing time and issues during the reconstruction process. There are two solutions to reduce the images to only images showing the structure. Firstly, only images of the structure are captured in the first place or remove images that are not useful after the capture and before further processing. This study developed and evaluated techniques that would enable the first strategy to be applied in an AUV. To apply this strategy in an AUV, would require an on-board structure detection system to ensure that they are correctly orientated for capturing useful footage during a survey mission. However, the marine environment poses several challenges to image-based object detection. Furthermore, small AUVs have limited power and computational resources available while deployed on a mission. To investigate the suitability of creating a lightweight structure detection model for the purpose of image evaluation, three computationally efficient image feature extraction methods (colour moments, local binary patterns (LBP), and Haar wavelet decomposition) were evaluated for their ability to distinguish underwater structures from background areas using unsupervised k-means models. LBP was found to be an effective method for identifying underwater structures in open water conditions. For identifying a structure against the seabed, colour moments were identified as the most effective method.

Improved Artificial Potential Field Algorithm Assisted by Multisource Data for AUV Path Planning.

With the development of ocean exploration technology, the exploration of the ocean has become a hot research field involving the use of autonomous underwater vehicles (AUVs). In complex underwater environments, the fast, safe, and smooth arrival of target points is key for AUVs to conduct underwater exploration missions. Most path-planning algorithms combine deep reinforcement learning (DRL) and path-planning algorithms to achieve obstacle avoidance and path shortening. In this paper, we propose a method to improve the local minimum in the artificial potential field (APF) to make AUVs out of the local minimum by constructing a traction force. The improved artificial potential field (IAPF) method is combined with DRL for path planning while optimizing the reward function in the DRL algorithm and using the generated path to optimize the future path. By comparing our results with the experimental data of various algorithms, we found that the proposed method has positive effects and advantages in path planning. It is an efficient and safe path-planning method with obvious potential in underwater navigation devices.

Smart Navigation and Inspection System for Aquaculture Development under Controlled Parameters

Aquaculture is a rapidly growing industry that provides a critical source of food for millions of people worldwide. However, fish farmers face significant challenges such as environmental pollution, disease control, and inadequate water quality control systems. To address these challenges, intelligent monitoring and control systems are necessary. One promising technology that can improve monitoring efficiency and reduce human error is the use of autonomous underwater vehicles (AUVs) in cage aquaculture. AUVs can operate in harsh and challenging environments, collect data on water quality, temperature, and other environmental factors, and provide detailed information on fish health and behavior. Implementing AUVs in cage aquaculture can improve sustainability by reducing waste and minimizing environmental pollution, and aid in surveillance activities to detect illegal fishing and other unauthorized activities. Additionally, AUVs can assist in infrastructure inspection to identify and repair damaged equipment. In conclusion, the aquaculture industry faces significant challenges that can lead to substantial losses. Intelligent monitoring and control systems, along with the use of AUVs, can help mitigate these challenges by providing real-time information on fish health, cage condition, and the environment. Implementing these technologies can promote a more sustainable and profitable aquaculture industry, ensuring a reliable source of food for millions of people worldwide.

Model Identification and Control of a Buoyancy Change Device

There are several compelling reasons for exploring the ocean, for instance, the potential for accessing valuable resources, such as energy and minerals; establishing sovereignty; and addressing environmental issues. As a result, the scientific community has increasingly focused on the use of autonomous underwater vehicles (AUVs) for ocean exploration. Recent research has demonstrated that buoyancy change modules can greatly enhance the energy efficiency of these vehicles. However, the literature is scarce regarding the dynamic models of the vertical motion of buoyancy change modules. It is therefore difficult to develop adequate depth controllers, as this is a very complex task to perform in situ. The focus of this paper is to develop simplified linear models for a buoyancy change module that was previously designed by the authors. These models are experimentally identified and used to fine-tune depth controllers. Experimental results demonstrate that the controllers perform well, achieving a virtual zero steady-state error with satisfactory dynamic characteristics.

Experimental Study on the Vibration Transfer Function of a Small Autonomous Underwater Vehicle

This paper presents the experimental results about vibration characteristics of a small autonomous underwater vehicle (AUV) named OKPO 300. The autonomy of AUVs leads to the increased use of AUVs in scientific, military, and commercial areas because their autonomy makes it possible for AUVs to be utilized instead of humans in hazardous missions such as mine countermeasure missions (MCMs). It is impossible to use devices with electro-magnetic waves for gathering information in an underwater environment. Only sonar systems that use sound waves can be utilized underwater, and the performance of sonar can strongly affect the autonomy of AUVs. Since a thruster system that combines a motor and propeller in a single structure is widely used as the propulsion system of an AUV and is mounted on the outside of the AUV’s stern, the vibration generated by the thruster system can be transferred throughout the shell of the AUV from the stern to the bow. The transferred vibration can affect the performance of various sonar systems that are installed in the AUV such as side-scan sonar or forward-looking sonar. Therefore, it is necessary to identify the effect of the transferred vibration of the AUV on the sonar systems. Even if various numerical methods were used to analyze the vibration problem of surface ships, it would be hard to apply numerical methods of the surface ships to identify the vibration phenomena of an AUV because of the underwater environment. In this work, an experimental study with OKPO 300 and an impact hammer was carried out to analyze vibration features of an actual small AUV in the air. Based on the experimental results, the frequency response function of vibration is presented. Based on the experimental results of this study, the natural frequency of OKPO 300 was confirmed, and the possibility of estimating the vibration characteristics of AUV using the transfer function method was shown.

An experimental study on marine debris location and recognition using object detection

The large amount of debris in our oceans is a global problem that dramatically impacts marine fauna and flora. While a large number of human-based campaigns have been proposed to tackle this issue, these efforts have been deemed insufficient due to the insurmountable amount of existing litter. In response to that, there exists a high interest in the use of autonomous underwater vehicles (AUV) that may locate, identify, and collect this garbage automatically. To perform such a task, AUVs consider state-of-the-art object detection techniques based on deep neural networks due to their reported high performance. Nevertheless, these techniques generally require large amounts of data with fine-grained annotations. In this work, we explore the capabilities of the reference object detector Mask Region-based Convolutional Neural Networks for automatic marine debris location and classification in the context of limited data availability. Considering the recent CleanSea corpus, we pose several scenarios regarding the amount of available train data and study the possibility of mitigating the adverse effects of data scarcity with synthetic marine scenes. Our results achieve a new state of the art in the task, establishing a new reference for future research. In addition, it is shown that the task still has room for improvement and that the lack of data can be somehow alleviated, yet to a limited extent.

Real-Time Pipe and Valve Characterisation and Mapping for Autonomous Underwater Intervention Tasks.

Nowadays, more frequently, it is necessary to perform underwater operations such as surveying an area or inspecting and intervening on industrial infrastructures such as offshore oil and gas rigs or pipeline networks. Recently, the use of Autonomous Underwater Vehicles (AUV) has grown as a way to automate these tasks, reducing risks and execution time. One of the used sensing modalities is vision, providing RGB high-quality information in the mid to low range, making it appropriate for manipulation or detail inspection tasks. This work presents the use of a deep neural network to perform pixel-wise 3D segmentation of pipes and valves on underwater point clouds generated using a stereo pair of cameras. In addition, two novel algorithms are built to extract information from the detected instances, providing pipe vectors, gripping points, the position of structural elements such as elbows or connections, and valve type and orientation. The information extracted on spatially referenced point clouds can be unified to form an information map of an inspected area. Results show outstanding performance on the network segmentation task, achieving a mean F1-score value of 88.0% at a pixel-wise level and of 95.3% at an instance level. The information extraction algorithm also showcased excellent metrics when extracting information from pipe instances and their structural elements and good enough metrics when extracting data from valves. Finally, the neural network and information algorithms are implemented on an AUV and executed in real-time, validating that the output information stream frame rate of 0.72 fps is high enough to perform manipulation tasks and to ensure full seabed coverage during inspection tasks. The used dataset, along with a trained model and the information algorithms, are provided to the scientific community.

Design and Experimental Tests of a Buoyancy Change Module for Autonomous Underwater Vehicles

Ocean exploration is of major importance for several reasons, including energy and mineral resource retrieval, sovereignty, and environmental concerns. The use of autonomous underwater vehicles (AUV) has thus been receiving increased attention from the scientific community. In this context, it has been shown that the use of buoyancy change modules (BCMs) can significantly improve the energy efficiency of an AUV. However, the literature regarding the detailed design of these modules is scarce. This paper contributes to this field by describing the development of an electromechanical buoyancy change module prototype to be incorporated into an existing AUV. A detailed description of the constraints and compromises existing in the design of the device components is presented. In addition, the mechanical design of the hull based on FEM simulations is described in detail. The prototype is experimentally tested in a shallow pool where its full functionality is shown. The paper also presents preliminary experimental values of the power consumption of the device and compares them with the ones provided by existing models in the literature.

Model-Free High-Order Sliding Mode Controller for Station-Keeping of an Autonomous Underwater Vehicle in Manipulation Task: Simulations and Experimental Validation.

The use of autonomous underwater vehicles (AUVs) has expanded in recent years to include inspection, maintenance, and repair missions. For these tasks, the vehicle must maintain its position while inspections or manipulations are performed. Some station-keeping controllers for AUVs can be found in the literature that exhibits robust performance against external disturbances. However, they are either model-based or require an observer to deal with the disturbances. Moreover, most of them have been evaluated only by numerical simulations. In this paper, the feasibility of a model-free high-order sliding mode controller for the station-keeping problem is validated. The proposed controller was evaluated through numerical simulations and experiments in a semi-Olympic swimming pool, introducing external disturbances that remained unknown to the controller. Results have shown robust performance in terms of the root mean square error (RMSE) of the vehicle position. The simulation resulted in the outstanding station-keeping of the BlueROV2 vehicle, as the tracking errors were kept to zero throughout the simulation, even in the presence of strong ocean currents. The experimental results demonstrated the robustness of the controller, which was able to maintain the RMSE in the range of 1–4 cm for the depth of the vehicle, outperforming related work, even when the disturbance was large enough to produce thruster saturation.

Influence of Integration Schemes and Maneuvers on the Initial Alignment and Calibration of AUVs: Observability and Degree of Observability Analyses.

The use of autonomous underwater vehicles (AUV) has increased in a wide range of sectors, including the oil and gas industry, military, and marine research. The AUV capabilities to operate without a direct human operator and untethered to a support vessel are features that have aroused interest in the marine environment. The localization of AUV is significantly affected by the initial alignment and the calibration of the navigation sensors. In this sense, this paper proposes a thorough observability analysis applied to the latter problem. The observability analysis is carried out considering three types of sensor fusion integration and a set of maneuvers, and the results are validated through numerical simulations. As main contribution of this paper, it is shown how the addition of position errors in the observation vector can decouple some gyro and accelerometer biases from the latitude and altitude errors, particularly in the stationary observability analysis. The influence of oscillations in the diving plane and typical AUV maneuvers are analyzed, showing their relative impacts on the degree of observability of the inertial measurement unit (IMU)/Doppler velocity log (DVL) misalignment and DVL scale factor error. Finally, the state’s estimation accuracy is also analyzed, showing the limitation of the degree of observability as an assessment tool for the estimability of the states.

Submarine pipeline tracking technology based on AUVs with forward looking sonar

Submarine pipelines have provided an important approach to transport marine oil and gas resources because of their simplicity and efficiency. However, natural or man-made causes can easily lead to pipeline damage and thus result in waste of resources and environmental pollution. At present, the manual routine inspection of submarine pipelines is mainly completed by the survey vessels, which requires a large workload and high cost. The use of autonomous underwater vehicles with multibeam sonar system for real-time surveys is a good solution to this problem. Yet, the accumulation of errors in the integrated navigation system of the vehicle over a long period of operation can cause relatively large position deviations, since the signals of global positioning system cannot be received in the deep sea. To deal with it, the forward looking sonar is adopted in this study to obtain the precise position of the submarine pipeline and a tracking algorithm is proposed to track it. Firstly, the software development kit of the forward looking sonar is used to continuously obtain the pseudo-color images of the submarine pipeline, which are converted into 8-channel gray-scale images for image segmentation and binarization. Secondly, the optimized Rosenfeld algorithm is applied to thin the binary images, after which the submarine pipeline is identified and the corresponding function is fitted to determine the location of the target point to be tracked. Finally, the geodetic coordinates of the target point are determined through coordinate conversion to position the submarine pipeline. A sea trial is carried out and the results prove that accurate and stable pipeline tracking can be achieved by combining the integrated navigation system and the proposed algorithm.

Development of AUV MONACA - Hover-Capable Platform for Detailed Observation Under Ice –

The melting of ice and changes in ocean currents in Antarctica must be investigated to understand global climate change. In this regard, the volume changes of sea ice and ice shelves, bathymetry, and ocean currents in the Antarctic Ocean must be measured in three dimensions. Therefore, the use of autonomous underwater vehicles (AUVs), which can directly observe under ice, is being considered. The authors developed an AUV named Mobility Oriented Nadir AntarctiC Adventurer (MONACA) to observe sea ice and the lower region of the ice shelf in the Antarctic Ocean. Herein, we describe MONACA and its basic autonomous navigation methods (altitude control, depth control, and waypoint tracking), as well as report the results of a sea experiment conducted in Shimoda Bay, Japan. During the 5-day sea trial, the MONACA successfully measured bathymetry by tracking 15 waypoints in sequence, switching the control criteria in the -axis direction between 3 m depth and 3 m altitude.

Passive underwater acoustic identification tags using multi-layered shells.

The development of pre-deployed underwater infrastructures to aid in autonomous underwater vehicle (AUV) navigation is of keen interest, with the increased use of AUVs for undersea operations. Previous literature has introduced a class of passive underwater acoustic markers, termed acoustic identification (AID) tags [Satish, Trivett, and Sabra, J. Acoust. Soc. Am. 147(6), EL517-EL522 (2020)], which are inexpensive to construct, simple to deploy, and reflect unique engineered acoustic signatures that can be detected by an AUV instrumented with high-frequency sonar systems. An AID tag is built of multi-layer shells with different acoustic properties and thicknesses to generate a unique acoustic signature, composed of the multiple reflections created by the layer interfaces, thus akin to an "acoustic barcode." AID tags can be used as geospatial markers to highlight checkpoints in AUV trajectories or mark areas of interest underwater. This article investigates the optimization of the AID tag's design using energy based metrics and evaluates the detectability of an AID tag in the presence of interfering signals, such as clutter using matched-filter based techniques. Furthermore, experimental results of AID tags interrogated by a standard high-frequency sonar are presented to provide proof of concept of AID tag detection in a reverberant water tank.

Wi-Buoy: An Energy-Efficient Wireless Buoy Network for Real-Time High-Rate Marine Data Acquisition

The next frontier of maritime networking will see the deployment of large-scale buoy-based mesh networks, with an equal emphasis on both high-speed data transfer and energy-efficiency. One such challenging application is the operation of maritime seismic surveys for oil/gas exploration and academic studies. Large amounts of seismic data are generated at a rate of several Gigabits per second, by nearly 10,000–30,000 seismic sensors that are deployed on the seabed across an area of several hundred square kilometers in offshore oceanic environments. The task of monitoring existing reservoirs and identifying new oil and gas deposits require subsurface images of superior quality, which in turn are dependent on high-quality data for processing. Wireless technology can unlock real-time data transfer for rapid image-viewing, enhanced productivity, and reduced logistical costs. This interdisciplinary article outlines the challenges of marine seismic acquisition and the design of a buoy-based wireless backhaul network for high-rate data transfer over the ocean surface. Based on off-the-shelf IEEE 802.11 systems, a standards-compliant wireless buoy network architecture called Wi-buoy is proposed for real-time, scalable, and energy-efficient data delivery. In order to attain optimal power conservation, a Buoy-Based Power-Saving Backhaul (B-PSB) scheme is also proposed for specifying the operating parameters across all layers of the protocol stack. Essential aspects of the marine propagation environment are reviewed, and the performance of the proposed system is evaluated as a function of the antenna height, wind speed, compression ratio, and various flavors of the IEEE 802.11 standard. Furthermore, the use of Autonomous Underwater Vehicles (AUVs) is analyzed as an integral component of upcoming high-speed buoy-based networks in the maritime environment.

Adaptive sampling with an autonomous underwater vehicle in static marine environments

AbstractThis paper explores the use of autonomous underwater vehicles (AUVs) equipped with sensors to construct water quality models to aid in the assessment of important environmental hazards, for instance related to point‐source pollutants or localized hypoxic regions. Our focus is on problems requiring the autonomous discovery and dense sampling of critical areas of interest in real‐time, for which standard (e.g., grid‐based) strategies are not practical due to AUV power and computing constraints that limit mission duration. To this end, we consider adaptive sampling strategies on Gaussian process (GP) stochastic models of the measured scalar field to focus sampling on the most promising and informative regions. Specifically, this study employs the GP upper confidence bound as the optimization criteria to adaptively plan sampling paths that balance a trade‐off between exploration and exploitation. Two informative path planning algorithms based on (i) branch‐and‐bound techniques and (ii) cross‐entropy optimization are presented for choosing future sampling locations while considering the motion constraints of the sampling platform. The effectiveness of the proposed methods are explored in simulated scalar fields for identifying multiple regions of interest within a three‐dimensional environment. Field experiments with an AUV using both virtual measurements on a known scalar field and in situ dissolved oxygen measurements for studying hypoxic zones validate the approach's capability to quickly explore the given area, and then subsequently increase the sampling density around regions of interest without sacrificing model fidelity of the full sampling area.

Problematic Issues of Intellectualizing the Control System of Autonomous Underwater Vehicles

The paper considers ways of intellectualizing the control processes of autonomous underwater vehicles (AUVs) by the example of solving three problems, on which the successful use of AUVs largely depends. The first problem is to create an AUV control system (CS), which ensures the achievement of the mission’s objective in the event of emergencies caused by both external and internal reasons, as well as deliberate and unintentional counteraction. It is shown that for the construction of the AUV’s CS the decentralized multi-agent structure is most suitable, in which each AUV system is an independent intellectual agent with its own control system. The control system must be equipped with a set of adaptive algorithms that ensure: control of AUV in the event of emergency situations, taking into account restrictions on the supply of electric energy, speed, accuracy of autonomous underwater navigation, range of hydro-acoustic communication; rational distribution of energy resources by AUV systems in accordance with the current situation; maintaining the functional stability of the AUV with a partial malfunction of technical means. The second problem is to create an underwater navigation system that ensures the accomplishment of AUV missions at great distances from the base point. Since AUV navigation using only on-board means (inertial navigation system and lag) does not provide the necessary accuracy, a prerequisite for AUV navigation over long distances is to conduct an observation using external sources, the choice of which in the circumstances is a non-trivial task. The third problem is to create a network underwater communication system (NUCS), which provides for the group application of AUVs. The ground analogue of NUCS is network radio communication. But if the latter is fairly well developed, then the former only takes the initial steps. This is due to both the later practical relevance of NUCS, and many fundamental physical factors that impede the development of NUCS, which include: a substantially limited frequency band that can be used in practice for signal transmission; a large propagation time of the hydroacoustic signal compared to the radio signal; the formation of extended shadow zones and fading of the connected signal due to its multipath propagation; significant Doppler distortions, fast variability of the characteristics of the hydroacoustic medium.

Multi-AUVs Cooperative Target Search Based on Autonomous Cooperative Search Learning Algorithm

As a new type of marine unmanned intelligent equipment, autonomous underwater vehicle (AUV) has been widely used in the field of ocean observation, maritime rescue, mine countermeasures, intelligence reconnaissance, etc. Especially in the underwater search mission, the technical advantages of AUV are particularly obvious. However, limited operational capability and sophisticated mission environments are also difficulties faced by AUV. To make better use of AUV in the search mission, we establish the DMACSS (distributed multi-AUVs collaborative search system) and propose the ACSLA (autonomous collaborative search learning algorithm) integrated into the DMACSS. Compared with the previous system, DMACSS adopts a distributed control structure to improve the system robustness and combines an information fusion mechanism and a time stamp mechanism, making each AUV in the system able to exchange and fuse information during the mission. ACSLA is an adaptive learning algorithm trained by the RL (Reinforcement learning) method with a tailored design of state information, reward function, and training framework, which can give the system optimal search path in real-time according to the environment. We test DMACSS and ACSLA in the simulation test. The test results demonstrate that the DMACSS runs stably, the search accuracy and efficiency of ACSLA outperform other search methods, thus better realizing the cooperation between AUVs, making the DMACSS find the target more accurately and faster.

Human Error in Autonomous Underwater Vehicle Deployment: A System Dynamics Approach.

The use of autonomous underwater vehicles (AUVs) for various applications have grown with maturing technology and improved accessibility. The deployment of AUVs for under-ice marine science research in the Antarctic is one such example. However, a higher risk of AUV loss is present during such endeavors due to the extremities in the Antarctic. A thorough analysis of risks is therefore crucial for formulating effective risk control policies and achieving a lower risk of loss. Existing risk analysis approaches focused predominantly on the technical aspects, as well as identifying static cause and effect relationships in the chain of events leading to AUV loss. Comparatively, the complex interrelationships between risk variables and other aspects of risk such as human errors have received much lesser attention. In this article, a systems-based risk analysis framework facilitated by system dynamics methodology is proposed to overcome existing shortfalls. To demonstrate usefulness of the framework, it is applied on an actual AUV program to examine the occurrence of human error during Antarctic deployment. Simulation of the resultant risk model showed an overall decline in human error incident rate with the increase in experience of the AUV team. Scenario analysis based on the example provided policy recommendations in areas of training, practice runs, recruitment policy, and setting of risk tolerance level. The proposed risk analysis framework is pragmatically useful for risk analysis of future AUV programs to ensure the sustainability of operations, facilitating both better control and monitoring of risk.

Proposal of an Automated Mission Manager for Cooperative Autonomous Underwater Vehicles

In recent years there has been an increasing interest in the use of autonomous underwater vehicles (AUVs) for ocean interventions. Typical operations imply the pre-loading of a pre-generated mission plan into the AUV before being launched. Once deployed, the AUV waits for a start command to begin the execution of the plan. An onboard mission manager is responsible for handling the events that may prevent the AUV from following the plan. This approach considers the management of the mission only at the vehicle level. However, the use of a mission-level manager in coordination with the onboard mission manager could improve the handling of exogenous events that cannot be handled fully at the vehicle level. Moreover, the use of vehicle virtualization by the mission-level manager can ease the use of older AUVs. In this paper, we propose a new mission-level manager to be run at a control station. The proposed mission manager, named Missions and Task Register and Reporter (MTRR), follows a decentralized hierarchical control pattern for self-adaptive systems, and provides a basic virtualization in regard to the AUV’s planning capabilities. The MTRR has been validated as part of the SWARMs European project. During the final trials we assessed its effectiveness and measured its performance. As a result, we have identified a strong correlation between the length of mission plan and the time required to start a mission ( ρ s = 0.79 , n = 45 , p 0.001 ). We have also identified a possible bottleneck when accessing the repositories for storing the information from the mission. Specifically, the average time for storing the received state vectors in the relational database represented only 18.50% of the average time required for doing so in the semantic repository.