Evaluation of Technological Readiness in Mixed Maturity Sub-systems of Large Uncrewed Underwater Vehicles

With the increasing complexity of modern systems due to advancements in technology, as seen with autonomous, Large Uncrewed Underwater Vehicles (LUUV), there is an ever-increasing reliance on Test and Evaluation (T&E) to ascertain system viability within the capability lifecycles. One technique that assists with T&E efforts and decision-making during the development and acquisition phases is the application of Technology Readiness Levels (TRL); a NASA-originated measurement system that aims to evaluate technological maturity, with a spectrum ranging from theoretical concepts to the implementation of proven designs (Banke 2010). Although not strictly developed in a system engineering or project management context, determination of maturity and associated ‘readiness’ of technologies via the TRL method presented significant utility, especially during acquisition, whereby TRLs of system constituents are often at varied levels of maturity. The integration of multiple technologies in the system architecture requires consideration of how varying TRL subsystems impact the overall viability of the system from a top system-level perspective (Saucer et al. 2006).

Technological Readiness Assessments (TRA) conducted to determine TRLs can be leveraged for comparative analyses of performance and identification of critical aspects from a holistic system and subsystem perspective. Determination of this risk supports decisions including the development of suitable controls between decision gates to minimise the impact on key outcomes (Honea 2020; Sauser et al. 2006; Thamhain 2013; US Government Accountability Office 2016). Although risk management does not inherently address the issues of mixed maturity, early and better decision-making can often minimise subsequent program impacts, as seen in the case with the Australian Defence Force’s F-111, Collins Class submarines and F35 projects, and therefore improve ultimate outcomes and deliverables (Bennett 2010; Yule 2008). Of particular relevance is the Speartooth LUUV acquisition by the Royal Australian Navy in response to domestic military capability requirements and geopolitical postures (Bergmann 2022). Management, and potential service introduction, of the technological conglomerate underpinning the proposed Speartooth prototype capability will rely significantly upon consideration of the technological maturity architecture and processes to determine pathways towards implementation and integration.

This paper explores a methodology of undertaking TRAs against developing LUUV platforms such as the Speartooth where subs-system functions are undergoing varying stages of development, adaptable and re-design to support rapid prototyping and evolving operational requirements (Laird 2023). While the evaluation of an exact TRL remains an elusive objective given the degree of subjective interpretation with the proposed TRA methodology, the relative outcome, if evaluated consistently, can provide indicative regions of relatively low technological maturity, to which additional considerations may be considered to ensure that T&E activities, and acquisition more broadly, are informed and effective.

Contemporary projects and systems acquisitions are often highly complex due to an overwhelming quantity of considerations to ensure the optimisation of performance and organisational value over the product life cycle (Thamhain 2013). To gain a competitive edge, explore novel capabilities or address outstanding problems and issues, Ivanova et al. (2020) articulate that there often exists a need to incorporate emergent and established technologies. This often comes at a significant cost and risk to the system enterprise. The use of TRLs is often incorporated as part of the project management, systems engineering and product governance disciplines to better inform and guide especially in the context of a multi-disciplinary, multifaceted and complex system of system constructs (Olechowski et al. 2020).

Despite its utility, TRLs are not without limitations. One limitation from the NASA techniques legacy is when TRLs focus on singular technologies. This is distinct from contemporary systems which are fundamentally integrated, combining traditional technology elements with more modern elements such as information systems and human interfaces (Granic & Marangunic, 2019; Ivanova et al., 2020). Therein arises terminology such as System Readiness Level (SRL), techno-economic assessments, manufacturing readiness and integration readiness, which are all variants of the TRL approach however customised to specific outcomes other than purely technological based (Buchner et al., 2018; Sauser et al., 2006; US Office of the Secretary of Defense Manufacturing Technology Program, 2022). The trade-off in most variants is fidelity and therefore no robust model exists that can reliably ascertain whole-of-system performance (Javed et al., 2017; Zutin et al., 2020). Noting this, the intent of TRL application on any level beyond a singular technology is less concerning a definite assessment of a maturity or readiness but more to leverage off TRA as a comparative and informative process across the examined entities (Buchner et al., 2019).

Application of the TRL framework on emerging capabilities such as LUUVs, a subset of  Autonomous Underwater Vehicles (AUV) is relevant. LUUVs are relatively new in the context of military application and present a nuanced evolution of existing capability systems from crewed submarines (Salimzhan & Evgeniy, 2015).  With the evolution of maritime warfare into a multifaceted domain amid increasingly complex operational environments, nations are rapidly integrating AUV systems within military contexts for purposes such as Anti-Submarine Warfare, Reconnaissance and Surveillance and Sea-mining (Agarwala, 2022). This expansion into multiple domains means AUVs holistically have attracted significant industry attention and triggered the development and integration of both mature and emergent technologies to establish system functionalities that cater for the dynamic operational environment. In most instances, this development occurs within individual LUUV subsystems including Propulsion, Sensors, Navigation and Localisation (Agarwala, 2022; Heo et al., 2017; Wang & Ke, 2022; Wibisono et al., 2023), Communications, Structural, Control and Autonomy, and Energy (Bogue, 2015; Chen et al., 2021; Sahoo et al., 2019; Zhou et al., 2023).

In contrast to crewed underwater platforms such as submarines, the operational sequences and subsystem requirements on the LUUV differ significantly despite commonality across actual subsystems hence resulting in different configuration considerations. As elaborated by Heo, Kim and Kwon (2017), the absence of a crew on board reduces the size and noise profiles of LUUVs although there is a great emphasis placed upon autonomy given that there are no viable options for localised control once the platform is deployed. Typically, LUUVs are launched from surface vessels and are designed to operate across various environments, including shallow, deep, and littoral waters, often travelling up to 20 nautical miles from the launch point before completing their tasks and returning. Given this operational profile, the effectiveness of LUUVs relies heavily on the seamless interaction of subsystems and automated operations to conduct representative missions. However, integrating the technologies that support these functions presents significant challenges (Hu, Moreira, and Guedes Soares 2023; Salimzhan & Evgeniy 2015). Applying TRLs in this context can offer valuable insights into the limitations of current technologies and guide design efforts to develop a viable LUUV product.

Despite the use of classification systems such as TRLs being in widespread use for nearly two decades, there is limited comprehensive literature on the overall TRL of LUUVs, with most studies focusing on specific sub-systems or applications particular to a unique setting. Notwithstanding specific platform literature, the inference is that part of the development efforts from academia and industry into a particular technology should result in some publications, and the general status of the technological maturity, being broadly consistent across all articles and able to be deduced accordingly. However, quantifying the technological maturity and readiness of LUUV sub-systems can be complex given that technologies that underpin unconventional functions are often not widely publicised or well understood, or indeed in the context of already operational or near-operational platforms, may be commercially sensitive. Therefore, it is important to first understand the relevant aspects of technology that is commonly in use within the industry, before narrowing the scope of research to provide a better understanding of relevant technological advances that may be associated with these systems.

To validate this hypothesis, a comprehensive analysis was undertaken in two stages; an exploration of current operational systems and near-operational systems in use to identify any insights into their respective technologies, and a comprehensive literature review across the existing literature relating to technological developments concerning LUUVs, using a PRISMA methodology as the basis of article selection and exclusion. The databases used in the survey consisted of Science Direct, Scopus, ITEA,Google Scholar ResearchGate, IEEE Xplore, MDPI, Elsevier, Springer and Wiley Online. The search considered all information sources including grey literature from 2014, unless earlier sources deemed a higher TRL that reflected suitable operational use for the technology referenced. This high level analysis reviewed exemplars of operational and near-operational platforms such as the C2 Robotics Speartooth XLUUV (C2 Robotics, 2023), the Anduril Dive-LD XL-AUV and Ghost Shark XL-AUV, the Msubs Manta XLUUV, the Kongsberg Hugin AUV and the Cellula Robotics Solus-XR XLUUV. The subsequent literature review generated 499 literature sources after exclusion that provided a comprehensive understanding of both LUUV sub-systems, as well as relevant technologies that may be in development of use.

The results of this sub-system analysis were then further devolved, grouped thematically, and the relevant articles assigned a relative TRL based on the assessed maturity of the technology under examination. A visual representation of these results across major subsystems can be seen below in Figure 1, with further sub-system breakdown and analysis represented Figure 2. In both cases, the TRL of the technology reviewed was subjectively determined with reference to Copeland et al. (2015) based on the level of detail provided, unless otherwise specifically stated in the information source.

Figure 1: TRL Representation Across Major LUUV Subsystems

Within the examined articles in the literature survey, it can be seen that there is a spread of interpreted TRLs for the technologies within each functional subsystem domain. While the number of articles does not necessarily correlate to any conclusions, the general distribution can provide an indicative representation of where the subsystem TRL might be with respect to other elements. This indicative representation can also be more focused within the respective subsystem to provide a greater understanding of the key elements associated with the parent subsystem. While not applied in this analysis, a statistical approach can be applied to deduce a potential TRL value for each subsystem per the results of literature survey. Notwithstanding further interpretations, benefit however may still be gained by the researched content specific to LUUV technologies that may develop further insight into what is realistic for each subsystem function.

Figure 2: TRL Presentation Across LUUB Subsystem Elements

Energy Systems

Batteries

It is noted that across the majority of the literature reviewed most LUUVs used lithium-ion (li-ion) batteries as the primary energy source. The physical properties and performance of li-ion batteries are well-documented, with their industrial-scale use in electric vehicles (EVs) highlighted by the International Energy Agency (IEA, 2023). Their effectiveness as a power source for LUUVs is also validated, as demonstrated by autonomous vessels such as Anduril’s Dive-LD (Anduril, 2024a) and endorsements from original equipment manufacturers (OEMs) like Saft (2007). The proliferation of this technology indicates a higher level of potential technological maturity in the TRL realm of 8-9. This maturity also encompasses the auxiliary equipment of the batteries, including the encapsulating housing necessary for withstanding hydrostatic pressure and the variable environmental conditions that would be encountered with the requirements of deeper diving depths such as those encountered by the Speartooth LUUV (C2Robotics, 2023; d’Amore-Domenech et al., 2018).

However, it was observed that the housing of conventional li-ion batteries for LUUVs occupies significant space and adds considerable weight, leading to an overall increase in the mass of the vessel and subsequent reduction in performance. As a trade-off, smaller batteries are used to keep the mass down, which results in a lower overall energy density. Pressure tolerant li-ion batteries suitable with flooded hulls are an emerging technology that replaces the external housing with internal soft packings which regulate pressure changes with depth, thus saving space and allowing for greater energy by volume (Li et al., 2022). This technology’s gradual implementation in deep subsea applications is evident with Kraken Robotics (2024) utilising proprietary pressure-tolerant gel encapsulation technology for lithium polymer batteries in Kongsberg’s HUGIN UUV and Teledyne Gavia’s SeaRaptor.

Motors

A common trend across the reviewed literature highlighted the prevalent use of typical electric motor types in drivetrains incorporating common propeller configurations. Motors include permanent magnetic motors of both the AC and DC variants and can be seen in active use within Kongsberg’s HUGIN and Hydroid’s REMUS 600 (Rolls-Royce 2023: Naval Technology 2018). This observation aligns with the notion that motor technology is reasonably advanced at the higher TRLs of 8-9, with fewer breakthrough innovations expected except in specialised areas; optimising motor control (Arzhanov et al. 2020; Vinida et al 2018) and incorporating magnetic gearing for drive outputs (Fang et al 2021) being examples of still novel technologies at lower TRLs of 3-4.

Thermal Management

Thermal management for LUUV energy systems is a key research area due to the importance of maintaining optimal operational battery temperatures. Correct temperature regulation is important to ensure component efficiency, and longevity as well as the prevention of overheating in diverse underwater operational environments. While B. Li et al. (2024) explain that li-ion batteries’ thermal safety systems are also reasonably mature technologies (Shahid & Agelin-Chaab, 2022), the thermal management for optimal performance is still in the process of development (Deutsch et al., 2022). Supporting this observation, battery performance simulations presented by Wang and Wu (2019) on the management of li-ion battery temperatures, involving heat preservation outside the battery and heat dissipation inside it, highlight this technology is evolving through S&T opportunities and stages of development and deployment of TRL 4 to 7. Therefore, based on the laboratory experiments and trials conducted by Zhang et al. (2021), it can be inferred that although progress is being made, additional development is necessary to advance battery thermal management technology to a stage where it reduces uncertainty and risk in meeting performance standards.    

Propulsion

Propellers

Propulsion systems for LUUVs typically use single, open propellers like those in Anduril’s Dive-LD and Kongsberg’s Hugin (Anduril, 2024a; Kongsberg, 2024), or ducted propellers found in Speartooth, Anduril’s Ghost Shark, and MSubs Manta range (ADM, 2024; C2Robotics, 2023; Msubs, 2024). Dual propeller systems are used in the HSU001 LDUUV by the China Shipbuilding Industry Corporation (MaritimeExecutive, 2021). These systems are mature, drawing on decades of extensive submarine and vessel design experience (Stapersma, 2019). Research by Zhang et al. (2022) indicates potential for propeller design improvements, primarily focusing on realising efficiency gains which is important for extended range requirements. Subsequent propeller designs by Zhang et al. (2022), using unique design codes to optimise efficiency, were verified through simulations of the Explore100 AUV, which showed improved performance with their augmented design. While there is still potential for technological growth in this domain, significant advancements have already been made.

Variable Buoyancy Propulsion

Variable buoyancy systems have seen a surge in research activity in recent years. This research often takes place at the prototype or laboratory testing stage indicating a lower TRL. However, there have been several articles and use cases that feature technologies mature in system prototypes as well as operational environments (Tiwari & Sharma, 2020).

Such an attempt to employ variable buoyancy propulsion is seen with the Speartooth LUUV in which C2 robotics have leveraged the efficiency gains and minimal noise signature from this capability into their design (C2Robotics, 2023). This technology, despite its maturation in the 1990s, still has limitations for propulsion control, as stated by Orozco-Muñiz et al. in 2020. These limitations are particularly evident when it comes to size. Small autonomous vessels require a minor capacity of buoyancy change for propulsion which can be efficiently handled, whereas large LUUVs require a significant change. Inducing such large changes in buoyancy can be power-intensive and necessitates complicated mechanisms to manage the buoyancy capacity to weight ratio. This can affect payload capacity, weight, and ultimately, the endurance of the system as noted by Tiwari & Sharma (2020). Figure 3 demonstrates the concept of VBS through simulation results depicting from top left to bottom right; (a) the adjustment in weight, (b) the change in UUV pitch angle, (c) the resultant heave velocity, and (d) the change in depth.

Figure 3: VBS System Response during Simulation (Tiwari & Sharma, 2020)

Integrating such a system into an existing working capability can present compatibility issues that may offset the benefits gained through other propulsion system developments, such as optimised propellor design or emergent power system configurations. While variable buoyancy systems present a promising path for propulsion technology, trade-off studies will be necessary to assess their feasibility considering system performance from a top-level perspective.

Azimuth Propulsion

Rather than relying on planes and rudders for pitch and yaw control, LUUVs navigating in shallow, turbid estuarine environments may necessitate more responsive systems to avoid obstacles or seabed collisions. Such systems should maintain a referenced location for dynamic positioning, essential for equipment placement. This would bring specific requirements for responsive, off-axis propulsion, which would be directly linked to deployment and placement methods. This technology exists but appears to be developmental and proven only in small autonomous vessels rather than LUUVs (Eastridge et al., 2023). However, LUUVs with this need can leverage advancements made in the offshore industry where Platform Supply Vessels employ azimuth propulsors to augment dynamic positioning operations. Upscaling and integration of off-axis, highly responsive, and suitably powerful propulsion may pose a technological risk to incumbent LUUVs, particularly given specific environmental and mission requirements

Hull and Structure

Hull Structure

Most LUUVs across the reviewed literature generally featured a distinct hull type that resembles fullness in the forebody ellipsoid with parallel midbody of slenderness varying greatly depending on the form and resistance requirements of the vessel (Neira et al., 2021).  These hull forms are either flooded and/or dry. In a flooded hull, water is allowed to enter some compartments which are specifically designed to be flooded without damaging the internal systems. Typically, electronic payloads are kept in watertight vessels. This design contrasts with a dry hull, where all internal areas are sealed off from the external environment to keep water out entirely. A flooded hull design enables LUUVs to withstand the greater magnitudes of deep-sea pressures due to the equalisation effect, further increasing their operational diversity; a concept that was demonstrated in systems such as the Solus-XR XLUUV (CellulaRobotics, 2024) and Anduril’s Ghost Shark (McFadden, 2024). In the majority of research, most novel structural technologies were demonstrated to be transitioning from modelling and simulation to technology validation in relevant operational environments; this research, when combined with the technology already present in operational vessels, establishes structural technologies at more mature TRL levels between the realms of approximately 6-9.

Hydrodynamics

LUUVs are subjected to a wide variety of forces during operation which can make efficient hull design difficult, and thus optimising the hydrodynamic efficiency of LUUVs was seen repeatedly within the reviewed literature as a key focus of research. These improvements aimed at reducing vessel energy consumption and enhancing the operational viability of LUUVs in complex environments.  Most of the recent literature was seen using Computational Fluid Dynamics (CFD) as the primary mechanism of simulation and testing (Ji et al., 2021; Safari et al., 2022; Vasudev et al., 2018). Ao et al. (2023) build on CFD techniques and describe the use of artificial intelligence-aided design (AIAD) to augment the optimisation of the hull shape for LUUVs; a resultant 8.8% increase in hydrodynamic efficiency was realised in the AI-supported hull design compared to the initial hull design using traditional techniques. Ao et al. (2023) suggest that the use of data-driven algorithms and AIAD to develop highly optimised hull designs may provide a design and operational edge, however, the relative novelty of the use of AI for design optimisation yields a lower TRL; a modicum of caution is needed because AI, as a relatively new technology, may deliver good results for some performance parameters but miss the nuances and sensitivities of the overall LUUV hull design.

Materials

There was considerable research dedicated to the exploration of the material properties of hull structures to fortify the structural integrity of LUUVs under deep-sea pressures, further extending operational endurance in more diverse environments. Research currently focuses on improving historically used materials such as steel (Zhu et al., 2024), or exploring newer materials such as composites like carbon fibre (Liu et al. (2021). Composite materials in particular saw large investments in research; composite hulls are the preferred approach for military LUUVs due to minimal magnetic and thermal signatures inherent with composites over aluminium alloys (Council, 1997). Composites have increasingly been used in the construction of operational LUUVs; the hull of the C2 Systems Speartooth LUUV is Designed for Manufacturability (DfM) and is said to be of composite construction (Janes, 2022), and composite hull design is also shared with other LUUV’s such as Anduril’s Dive LD (Compositesworld, 2023). This places hull material technology as a more mature TRL of 8-9.

The main concern for composite hulls under large hydrostatic pressures is buckling failure modes. Prevention and improvement methods for buckling and other failure modes under large hydrostatic pressures are currently active areas of research (Yang et al. (2021). The prevalence of simulation-based testing leading to rapid prototyping and system testing in real-time operational environments signals that this technology is undergoing maturation and innovation (Cui et al., 2023), however, caution should be applied when stressing LUUVs to their operational envelope limits for hull durability if emergent composite structures make up the hull form.

Autonomy

Control and Learning Protocols

The literature survey showed that autonomy systems were the most researched among all LUUV subsystems, indicating significant efforts to improve this element’s technical maturity. Much focus was on control methodologies and learning protocols, often merging adaptive control strategies with machine learning protocols to boost autonomous adaptability to changing environmental conditions. Advancements in decision-making and fault detection and remediation were also noted, often linked to enhanced collision-free path planning and navigation in complex environments. These advancements use machine learning protocols for accurate identification and diagnosis of systematic faults. However, these areas were often still in the proof of concept stage, indicating low TRLs of 3-4. Sands (2020) research into the use of non-stochastic deterministic artificial intelligence over traditional stochastic algorithms is also noteworthy. Non-stochastic AI enables the system to learn and adapt to changes, such as system degradation including control and buoyancy, thereby providing the LUUV with the ability to manage such situations. This is achieved by providing the AI with knowledge of itself and its overarching purpose so that it is enabled to understand its context and react to changes more effectively than traditional AI (Sands, 2020). It is found the technological readiness of an autonomous capability can be determined by the level of maturity of the AI driving the system. This is particularly true for systems whose successful development and deployment hinge on the ability to accurately verify and validate the (system’s) decision-making processes, and where system operating performance needs to be balanced with the psychological response and acceptance of the humans interacting with the system (IDA, 2018; Rayhan, 2023). In all of the research reviewed, a maritime autonomous capability had yet to see proven operation in a complex environment making independent decisions based on dynamic excitation sources, therefore sub TRL 8.

Software

It is recognised that software has become the defining component of an autonomous system’s ability to fulfil its mission and objectives (DoD, 2019). Anduril’s software-centric approach to its LUUVs with its operating system (OS) Lattice is significant. Lattice is a hardware-agnostic AI-powered OS that is being fielded in a multitude of Anduril systems (Anduril, 2024b). The software represents a constantly evolving technology, with Anduril conducting bi-weekly tests for decision-making and sensor data processing to support the product’s growth. However, the literature review indicates that while commercial software like Anduril’s Lattice OS, and others such as Palantir’s Gotham, Orca AI, and c3ai are readily available, there is still a need for further development in software communication and sensor interface technology, particularly with human-in-the-loop systems and systems-of-systems arrangements.

Such focus should be the conduct of short-response time actions to augment the automated systems when human-in-the-loop responses are too slow (Bae & Hong, 2023). Other examples are collision avoidance and initial emergency response. Accurate docking contact strategies are provided as a particularly complicated activity to execute requiring integration of many aspects such as control, communication, hydrodynamics and sensor fusion (Bae & Hong, 2023).

Additional challenges exist for software in processing large volumes of sensor data (sensor fusion) and maintaining communication links in adverse environments that require a human interface to LUUVs, as well as considering other independent systems as part of a larger, more complex system. Konoplin et al. (2023) have developed code to improve communication interlinks and have conducted semi-natural simulations with promising results. This shows that software with communication interfaces is at a relatively moderate TRL of 2-5. Figure 4 demonstrates through simulation that the selection of an inappropriate localisation algorithm can result in various problems from path inefficiencies to significant anomalous behaviour.

Figure 4: Path Determination Performance During Simulation of Multiple Localisation Algorithms Experiencing Intermittent Position Updates (Liu et al., 2023)

Furthermore, Kongsberg, in collaboration with the Norwegian Defence Research Establishment, is developing goal-based mission execution software for their LUUVs. This technology is designed to enable the LUUV to autonomously evaluate the most effective method to fulfil its mission and process the data accordingly, making it easier for the operator-in-the-loop to take corrective action if the vessel encounters an unforeseen situation. Kongsberg is currently prototyping this technology with a roadmap of improvements scheduled for late 2024 and 2025. Guardian AI is another commercially available software platform by Marine AI that focuses its capabilities on an enterprise-wide solution that brings together the autonomous operation of fielded assets like the MSubs Orca LUUV with the shore-based control centres.

Sensors, Navigation and Localisation (SNL)

Suitable levels of reliability, accuracy and precision in sensors, navigation and localisation systems are of paramount importance for autonomous navigation and task execution. Increasing the level of autonomy requires an increase in sensors (Watson et al., 2020). The fundamental challenge for LUUV navigation system development is two-fold. Firstly, the integration of multiple sensor systems through sensor fusion for specific mission states and tasks is complicated. Navigation systems must be amalgamated (Bae & Hong, 2023) and fuse the inputs from several systems to create suitable situational awareness at suitable levels of precision and accuracy for the mission task (Watson et al., 2020).  Localisation methods would need to be multi-modal and tailored to the specific mission of the LUUV (Wibisono et al., 2023). Secondly, the accuracy of localisation systems for long-duration missions is critical for LUUVs based on inertial-based navigation (INS) guidance (Chen et al., 2020). The limited utility of satellite-based positioning and the limitations in the effectiveness of traditional optical sensors in adverse environments present challenges (Zhang et al., 2023; Wibisono et al. 2023). INS was determined to be mature and suitable for LUUVs (UST, 2024), however, the inherent limitation of INS is the cumulative accuracy degradation and resultant INS drift (Zhuang et al., 2023). This limitation is most pronounced over long time periods. To combat the relatively lower TRL of INS in this context, inertial-based systems are being augmented with acoustic methods such as Side Scan Sonar (SSS) and Doppler Velocity Log (DVL) in methods akin to GPS guidance updates for terrestrial vehicles.

Acoustic Navigation Technologies

Traditional sonar remains a key sensor to determine water depth and distance from obstacles. This encompasses Front-Looking Sonar (FLS), SSS and Synthetic Aperture Sonar (SAS) (Bae & Hong, 2023). Watson et al. (2020) summarise that the three methods of acoustic localisation; Sonar SLAM, Beacons and DVLs are proven technologies in open water with the potential for use in smaller confined spaces, however, there is no literature known to the author to independently document the use of these methods in such environments. Bae and Hong (2023) state that one challenge with sonar is its susceptibility to distance distortion due to water effects such as temperature, salinity and pressure. Wibisono et al. (2023) list additional environmental, sensing and recognition systems as optical, chemical (concentration of gases and pollutants) and physical (water properties) sensors in nature and suggest that these categories of sensors are routinely integrated to provide a comprehensive understanding of a submersible’s environment.

Inertial Navigation System Technologies

Auxiliary sensors to provide acceleration, velocity and position information are commonly used on LUUVs and are based on technologically mature equipment. Gyros, magnetometers, and INS both gimballed and strap-down are increasingly incorporated into guidance systems (Bae & Hong, 2023; Wibisono et al. 2023).

It is noted that LUUVs in under-ice Arctic areas on long missions provide parallels with tactical limitations for covert military LUUVs. In these environments the ability to surface and re-orientate or update INS guidance using GPS signal or beacon inputs is restricted (Chen et al., 2020). To address the issues and limitations of INS drift beyond hardware optimisation alone, three potential solutions were identified. Firstly, the use of specific Kalman filter methodologies and particle swarm optimisation (PSO) algorithms were shown to enhance the accuracy of INS (Chen et al., 2020). Secondly, proprioceptive navigation through neural networks augmenting accelerometers can be applied to develop an alternative INS. The use of neural networks to determine orientation rather than gyros allows for position estimation and reduced errors caused by traditional gyros in the underwater environment. This is a contemporary area of research (Neira et al., 2021). Thirdly, Yu et al. (2022) are exploring INS technology for compatibility with AI software deep learning algorithms to improve accuracy in image development and recognition to inform navigation system performance. They have developed a custom framework that has been successfully verified and validated at sea trails in the Sailfish-324 AUV. The level of unknowns and speculations in the different concept arrangements for INS augmentation indicates a low to moderate TRL. This is distinct from the higher TRL of standard inertial systems, which required short-periodic updates to remain accurate.

Optical Navigation Technologies

Visual odometry (VO) has been used as an auxiliary navigation method for LUUV applications. Wibisono et al. (2023) show that visual localisation encompassing augmented reality markers (AR), external tracking systems and visual-based SLAM are methods of optical navigation that can supplement acoustic or inertial-based systems. These systems are assessed to show potential for some LUUV tasks in complex and confined environments, but the reduced visual environment compared to terrestrial use can pose unique challenges. The LUUV would likely progress from less precise INS or acoustic techniques to optical sensors as it approaches shallow-water environments and infrastructure with which it must interact. As this is augmentation technology for traditional concepts, low TRL is experienced in this space.

Communications, Manipulators and Control Systems

When approaching the task of interacting with external infrastructure on the seabed, a traditional approach with LUUVs is Human-in-the-Loop (HITL) manipulation. The consideration for technological readiness level of communication, manipulators and autonomy are intrinsically linked. Vital for the transmission of commands, and data or to conduct HITL manipulation or control tasks, communication is disrupted in the underwater environment by fluid properties and attenuation resulting in a low Signal to Noise Ratio (SNR) (Wibisono et al., 2023). The ability of underwater platforms to successfully transmit data at a suitable rate for near-real-time acquisition of sonar, optical or other sensor pictures over long distances is an identified gap in technological maturity (Niemann et al., 2021).

Communication Technologies

The low technological readiness of high-bandwidth underwater communication technology (Kumar and Anjaneyulu 2018) signposts the complications of underwater data transfer that must be considered when identifying LUUV platform limitations (Niemann et al., 2021). This influences the level of autonomy required. Real-time transmission of video appears practically unavailable thus demanding autonomy for control of a LUUV. This was supported by the observation that LUUVs are growing in communications, control systems, and the use of artificial intelligence to improve automation (Wibisono et al. 2023).

There has been progress and innovation to address the challenges presented by underwater environments. New techniques and protocols have been attempted to address issues such as limited bandwidth, high latency, and signal degradation. Optical and acoustic communications research on hybrid optical-acoustic systems was demonstrated to show the potential to enhance network performance and reliability as the use of lasers alone to transmit data is prone to attenuation (Kumar & Anjaneyulu, 2018). An approach involving adaptive modulation to match sea environmental conditions using machine learning was reported to result in improvements in long-range communications (Huang and Diamant 2020). The broadband acoustic underwater data communication (BAUDC) results demonstrated the ability to transmit near-real-time data images to a ship from a LUUV via wireless communications over 200m in a shallow-water environment (Niemann et al., 2021). The research and development of communications technologies appear to have borne minimal gains in high-bandwidth communication over suitable distances for a military LUUV.

Manipulators Technologies

Manipulators are mechanical devices with articulated joints designed to manipulate tools, parts or other specific devices for a task (Neira et al. (2021)). When determining the design of the manipulator there are compromises to be made regarding degrees of freedom, power consumption, and chatter effects (which may increase acoustic detectability) that must be overcome with novel control laws that will need to be specifically designed and tuned to account for LUUV and manipulator design. Liu (2021) tabulates the limitations observed on LUUVs with manipulators such as expensive hardware, long process time, over or undershoots, chattering effect/external noise effect and steady-state errors, excess power consumption, time delay, slow convergence and gimbal lock. The most observed limitations were the chattering effect and slow process time.

Several different approaches to the manipulation task were identified. Alekseev et al. (2021) discuss the use of landers to pre-place equipment for subsequent use. A LUUV can then use this lander’s beacon to home in on a location and collect equipment for a task. The use of a lander vehicle is relatively common, however, the use of a guided lander which can incorporate water column modelling and characteristics to increase accuracy appears to be relatively novel and thus low TRL (Alekseev et al., 2021). Konoplin and Pyatavin (2021) discuss the use of point clouds to replicate a known 3-dimensional shape. Using additional computer processing this can be correlated with the sensed external environment, thus enhancing the autonomous use of manipulators to perform a task. The use of 3D point cloud modelling may reduce the need for high-power active sensors (Konoplin & Pyatavin, 2021). This has the potential for application in a turbid water environment where the augmentation of visual or special sensors will increase the precision and accuracy when detecting known objects (Konoplin & Pyatavin, 2021). Figure 5 shows a depiction of a target object using 3D point cloud modelling. The known object shape is correlated with multipath sonar returns to support determination of validity of the target object through position and orientation of the cloud points.

Figure 5: Multipath Sonar Mapping for Correlation With 3D Point Cloud Model of Target Object (Konoplin & Pyatavin, 2021)

Control Methodologies During Manipulation

Sliding Mode Control (SMC) for steady station keeping is well developed but may induce undesired behaviour of manipulators. There may be unique issues depending on the ratio of inertia and subsequent coupling between the hull and the manipulator. The design of an underactuated or fully actuated system concerning the manipulator may have significant second-order design consequences for cost, endurance and noise signature management (Liu, 2021). Underactuated systems are very hard to control and require hybrid control designs such as model reference adaptive control (MRAC) and SMC (Liu, 2021). These control laws are not without precedence but will have a significant integration risk as they will be required to be designed and tuned specifically for the LUUVs tasks. Discussions with Konoplin and Pyatavin (2021) suggest that the use of sensor augmentation to increase the accuracy of manipulators is a low TRL.

TRLs prove to be a highly versatile and useful tool for comparative analysis in the context of developing technology LUUVs. While direct methods for gauging a LUUV platform’s TRL may be lacking, literature surveys offer a valuable alternative for estimating TRL scores based on existing published works related to the technology domain associated with specific subsystems. Furthermore, conducting such surveys provides a comprehensive understanding of the available technologies, allowing for reasonable assumptions and inferences about their functional viability in meeting defined requirements as demonstrated within this paper.

Despite its strengths, the application of this TRA methodology is limited to comparative analysis. However, it holds potential for integration into higher-order TRL assessments or risk evaluations, provided there is a deeper understanding of subsystem interactions or if such assessments are explicitly elicited as part of the design process. In the context of LUUVs subsystems, the majority of subsystem technologies reside within the domain of TRL 3 to 4 although there are aspects of the subsystem functionality that may be at higher or lower levels depending on their application under different contexts. For LUUV capabilities, the collective effect of the subsystem TRL distribution would place the holistic system at an approximate TRL of at most 4, thus requiring additional design and operational test and evaluation efforts to demonstrate field performance . Furthermore, the technologies that underpin propulsion, energy and automation systems are notably immature in reference to the system requirements and therefore would require further effort to ensure development through the system acquisition and lifecycle phases where applicable.

ADM. 2024. “Defence and Anduril unveil first Ghost Shark prototype.” Accessed 18 April 2024. https://www.australiandefence.com.au/news/news/defence-and-anduril-unveil-first-ghost-shark-prototype.
Agarwala, N. 2022. “Integrating UUVs for Naval Applications.” Maritime Technology and Research 4 (3): 254470-254470.

Alekseev, Yu K., A. N. Zhirabok, and A. V. Medvedev. 2021. “Alternative Ways for Deep Sea Research and Improving Methods for Automatic Control Systems of Underwater Robotics.” IOP Conference Series: Earth and Environmental Science 666 (4): 042044. https://doi.org/10.1088/1755-1315/666/4/042044.

Anduril. 2024a. “Dive-LD – The Most Reliable & Flexible AUV Enables Boundless Exploration of the World’s Oceans.” Accessed 04 May 2024. https://www.anduril.com/hardware/dive-ld/.

Anduril. 2024b. “Lattice Solutions.” Accessed 7 April 2024. https://www.anduril.com/command-and-control/.
Ao, Yu, Jian Xu, Dapeng Zhang, and Shaofan Li. 2023. “Artificial Intelligence Aided Design of Hull Form of Unmanned Underwater Vehicles for Minimization of Energy Consumption.” Journal of Computing and Information Science in Engineering 24 (1). https://doi.org/10.1115/1.4062661.

Arzhanov, Kirill V., Vladimir V. Arzhanov, and Alla V. Arzhanova. “Highly Efficient Control of Electric Drives with Synchronous Motors with Permanent Magnets for Uninhabited Underwater Vehicles.” In 2020 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM), pp. 1-4. IEEE, 2020.

Bae, Inyeong, and Jungpyo Hong. 2023. “Survey on the Developments of Unmanned Marine Vehicles: Intelligence and Cooperation.” Sensors 23 (10): 4643. https://doi.org/https://doi.org/10.3390/s23104643.

Banke, J. 2010. “Technology Readiness Levels Demystified.” NASA. August 20, 2010. Accessed April 20, 2024. https://www.nasa.gov/aeronautics/technology-readiness-levels-demystified/.

Bennett, F. 2010. “The Seven Deadly Risks of Defence Projects.” Security Challenges 6 (3): 97-111. http://www.jstor.org/stable/26459801.

Bogue, Robert. “Underwater Robots: A Review of Technologies and Applications.” Industrial robot 42, no. 3 (2015): 186-91. https://doi.org/10.1108/IR-01-2015-0010.

Buchner, G. A., A. W. Zimmermann, A. E. Hohgräve, and R. Schomäcker. 2018. “Techno-Economic Assessment Framework for the Chemical Industry—Based on Technology Readiness Levels.” Industrial & Engineering Chemistry Research 57 (25): 8502-8517. https://doi.org/10.1021/acs.iecr.8b01248.

Buchner, G. A., K. J. Stepputat, A. W. Zimmermann, and R. Schomäcker. 2019. “Specifying Technology Readiness Levels for the Chemical Industry.” Industrial & Engineering Chemistry Research 58 (17): 6957-6969. https://doi.org/10.1021/acs.iecr.8b05693.

C2Robotics. 2023. “Speartooth LUUV.” Accessed 03 March 2024. https://youtu.be/9wtj-qAohPY?si=sARVrePvxt9pxeJP.

CellulaRobotics. 2024. “Cellula Robotics LDUUV and XLUUV at West 2024.” Accessed 04 May 2024. https://www.youtube.com/watch?v=3cahyoyUvm4.

Chen, Danhe, K. A. Neusypin, and M. S. Selezneva. 2020. Correction Algorithm for the Navigation System of an Autonomous Unmanned Underwater Vehicle. Sensors 20 (8). https://doi.org/10.3390/s20082365.

Chen, Xi, Neil Bose, Mario Brito, Faisal Khan, Bo Thanyamanta, and Ting Zou. “A Review of Risk Analysis Research for the Operations of Autonomous Underwater Vehicles.” Reliability Engineering & System Safety 216 (2021): 108011.

Compositesworld. 2023. “Large-format 3D printing enables toolless, rapid production for AUVs.” Accessed 03 April 2024. https://www.compositesworld.com/articles/large-format-3d-printing-enables-toolless-rapid-production-for-auvs.

Copeland, E. J, T. H. Holzer, T. J. Eveleigh, and S. Sarkani. 2015. “The effects of system prototype demonstrations on weapon systems.” Defense ARJ 22: 115.

Council, National Research. 1997. “Technology for the United States Navy and Marine Corps, 2000-2035: Becoming a 21st-Century Force.” 6.

Cui, Weicheng, Lian Lian, and Guang Pan. 2023. “Frontiers in Deep-Sea Equipment and Technology.” Journal of Marine Science and Engineering 11 (4). https://doi.org/10.3390/jmse11040715.

d’Amore-Domenech, Rafael, Miguel A. Raso, Antonio Villalba-Herreros, Óscar Santiago, Emilio Navarro, and Teresa J. Leo. 2018. “Autonomous underwater vehicles powered by fuel cells: Design guidelines.” Ocean Engineering 153: 387-398. https://doi.org/10.1016/j.oceaneng.2018.01.117.

Energy Conversion and Management: X, 14. https://doi.org/10.1016/j.ecmx.2022.100193 DoD. (2019). Who Cares: Why Does Software Matter for DoD? SWAP Study.

Eastridge, J. R., D. T. Lucas, and M. M. MacBain. 2023. “Investigation of the Viability of Centrifugal-Type Thrusters for Small UUV Propulsion.” OCEANS 2023 – MTS/IEEE U.S. Gulf Coast, 25-28 Sep. 2023.

Fang, Hongwei, Ziyan Li, and Xiuna Wei. “Design and optimization of magnetic gear composite motor for autonomous underwater vehicle.” In 2021 24th International Conference on Electrical Machines and Systems (ICEMS), pp. 421-425. IEEE, 2021.

Gafurov, Salimzhan A., and Evgeniy V. Klochkov. 2015. “Autonomous Unmanned Underwater Vehicles Development Tendencies.” Procedia Engineering 106: 141-148. https://doi.org/10.1016/j.proeng.2015.06.017.

Granic, Aleksandar, and Nenad Marangunic. 2019. “Technology Acceptance Model in Educational Context: A Systematic Literature Review.” British Journal of Educational Technology 50 (5): 2572-2593. https://doi.org/10.1111/bjet.12864.

Heo, Jinyeong, Junghoon Kim, and Yongjin Kwon. 2017. “Technology Development of Unmanned Underwater Vehicles (UUVs).” Journal of Computer and Communications 5: 28-35. https://doi.org/10.4236/jcc.2017.57003.

Huang, J., and R. Diamant. 2020. “Adaptive Modulation for Long-Range Underwater Acoustic Communication.” IEEE Transactions on Wireless Communications 19 (10): 6844-6857. https://doi.org/10.1109/TWC.2020.3006230.

Hu, Haitong, Lúcia Moreira, and C. Guedes Soares. 2023. Maritime Autonomous Vessels. Basel: MDPI – Multidisciplinary Digital Publishing Institute. https://doi.org/10.3390/jmse11010168.

IDA. 2018. “The Status of Test, Evaluation, Verification, and Validation (TEV&V) of Autonomous Systems.”

IEA. (2023). Trends in batteries – Battery demand for EVs continues to rise. Retrieved 03 March 2024 from https://www.iea.org/reports/global-ev-outlook-2023/trends-in-batteries

Ivanova, K., S. Elsawah, and J. Fidock. 2020. “Technological Ecosystems in Capability Development: A Case Study in Emerging Technologies.” Systems Engineering 23 (4): 423-435. https://doi.org/10.1002/sys.21535.

Javed, K., R. Gouriveau, and N. Zerhouni. 2017. “State of the Art and Taxonomy of Prognostics Approaches, Trends of Prognostics Applications and Open Issues Towards Maturity at Different Technology Readiness Levels.” Mechanical Systems and Signal Processing 94: 214-236. https://doi.org/10.1016/j.ymssp.2017.01.050.

Ji, D., Yao, X., Li, S., Tang, Y., & Tian, Y. 2021. “Model-free fault diagnosis for autonomous underwater vehicles using sequence Convolutional Neural Network.” Ocean Engineering, 232, 108874-. https://doi.org/10.1016/j.oceaneng.2021.108874

Janes. 2022. “Indo Pacific 2022: Royal Australian Navy breaks cover on Speartooth large unmanned underwater vehicle.” https://www.janes.com/defence-news/news-detail/indo-pacific-2022-royal-australian-navy-breaks-cover-on-speartooth-large-unmanned-underwater-vehicle.

Kongsberg. 2024. HUGIN Autonomous Underwater Vehicle (AUV). Retrieved 03 May 2024 from https://www.kongsberg.com/discovery/autonomous-and-uncrewed-solutions/hugin/

Konoplin, A. Yu, and P. A. Pyatavin. 2021. “Method of Autonomous Implementation of Manipulation Operations with Underwater Objects Having Predetermined Shape.” IOP Conference Series: Earth and Environmental Science 666 (4): 042083. https://doi.org/10.1088/1755-1315/666/4/042083.

Konoplin, Alexander, Alexander Yurmanov, and Maxim Panchuk. 2023. “Development of System for Tracking of AUV in HANS-USBL Operation Area.” 2023 International Russian Automation Conference (RusAutoCon).

Kumar, Arun, and L. Anjaneyulu. 2018. “Design and development of redeployable underwater data communication link for defence application.” Defense science journal 68 (1): 111-116. https://doi.org/10.14429/dsj.68.11837.

Laird, Robert. 2023. “C2 Robotics and Crafting New Undersea Operational Capabilities for Defence and Security – Second Line of Defense.” SLDinfo.com. October 11, 2023. Accessed August 12, 2024. https://sldinfo.com/2023/10/c2-robotics-and-crafting-new-undersea-operational-capabilities-for-defence-and-security/.

Li, Bo, Zhaoyong Mao, Baowei Song, Xueliang Wang, Wenlong Tian, Qixuan Sun, Yan-Feng Wang, and Zhaoguo Jin. 2024. “Experimental investigation on efficient thermal management of autonomous underwater vehicle battery packs using anisotropic expanded graphite/paraffin composite materials.” Applied Thermal Engineering 242. https://doi.org/10.1016/j.applthermaleng.2024.122477.

Li, Mengjie, Yuli Hu, Chengyi Lu, Bo Li, Wenlong Tian, Jiming Zhang, and Zhaoyong Mao. 2022. “Effect of hydrostatic pressure on electrochemical performance of soft package lithium-ion battery for autonomous underwater vehicles.” Journal of Energy Storage 54. https://doi.org/10.1016/j.est.2022.105325.

Liu, Boni. 2021. “Recent Advancements in Autonomous Robots and Their Technical Analysis.” Mathematical Problems in Engineering 2021: 6634773. https://doi.org/10.1155/2021/6634773. https://doi.org/10.1155/2021/6634773.

Liu, J., B. He, T. Yan, F. Yu, and Y. Shen. 2021. “Study on Carbon Fiber Composite Hull for AUV Based on Response Surface Model and Experiments.” Ocean Engineering 239: 109850.

MarineAI. 2024. “GUARDIANAI™ AUTONOMY Guarding Our Future.” Accessed 01 May 2024. https://www.marineai.co.uk/products.

MaritimeExecutive. 2021. “How China is Militarizing Autonomous Underwater Vehicle Technology.” https://maritime-executive.com/editorials/how-china-is-militarizing-autonomous-underwater-vehicle-technology.

McFadden, Christopher. 2024. “Anduril delivers first giant ‘Ghost Shark’ spy drone to Australian Navy.” Accessed 24 April 2024. https://interestingengineering.com/innovation/anduril-first-ghost-shark.

Msubs. 2024. “XLUUV / MANTA / S201.” Accessed 01 May 2024. https://msubs.com/unmanned-submersibles/xluuv/.

Neira, J., Sequeiros, C., Huamani, R., Machaca, E., Fonseca, P., & Nina, W. 2021. “Review on Unmanned Underwater Robotics, Structure Designs, Materials, Sensors, Actuators, and Navigation Control.” Journal of Robotics. 2021, 5542920. https://doi.org/10.1155/2021/5542920

Niemann, E., K. Nicolaides, and K. Dhuness. 2021. “A Prototype Broadband Acoustic Underwater Data Communication System for High Data Rates.” OCEANS 2021: San Diego – Porto, 20-23 Sept. 2021.
Office of the Secretary of Defense Manufacturing Technology Program. 2022. Manufacturing Readiness Level (MRL) Deskbook. U.S. Department of Defense.

Olechowski, Andrew L., Steven D. Eppinger, Nitin Joglekar, and Klaus Tomaschek. 2020. “Technology Readiness Levels: Shortcomings and Improvement Opportunities.” Systems Engineering 23 (4): 395-408. https://doi.org/10.1002/sys.21533.

OrcaAI. 2024. “Orca AI – Vision you can trust.” https://www.orca-ai.io/.

Orozco-Muñiz, Juan Pablo, Tomas Salgado-Jimenez, and Noe Amir Rodriguez-Olivares. 2020. “Underwater Glider
Propulsion Systems VBS Part 1: VBS Sizing and Glider Performance Analysis.” Journal of Marine Science and Engineering 8 (11). https://doi.org/10.3390/jmse8110919.

Palantir. 2024. “Gotham.” Accessed 04 April 2024. https://www.palantir.com/platforms/gotham/.

Rayhan, Shahana Rayhan; Abu. 2023. “The Psychological Impact of AI Adapting to a World of Smart Machines.” https://doi.org/10.13140/RG.2.2.29902.64329/1.

Robotics, Kraken. 2024. “SeaPower Subsea Batteries.” Accessed 23 March 2024. https://krakenrobotics.com/products/subsea-batteries/.

Rolls-Royce. “Helping Autonomous Underwater Vehicles Map the Ocean Floor.” Last modified 2023. Accessed August 15, 2024. https://www.rolls-royce.com/media/our-stories/discover/2023/helping-autonomous-underwater-vehicles-map-the-ocean-floor.aspx?utm_source=LinkedIn&utm_medium=organic&utm_campaign=Electrical&utm_content=marine.

Safari, Farhad, Mansour Rafeeyan, and Mohammad Danesh. 2022. “Estimation of Hydrodynamic Coefficients and Simplification of the Depth Model of an AUV Using CFD and Sensitivity Analysis.” Ocean Engineering 263: 112369-. https://doi.org/10.1016/j.oceaneng.2022.112369

Saft, 2007, “Saft lithium-ion battery power for BAE Systems’ Talisman autonomous underwater vehicle.”

Sahoo, Avilash, Santosha K Dwivedy, and PS Robi. “Advancements in the Field of Autonomous Underwater Vehicle.” Ocean Engineering 181 (2019): 145-60.

Sands, Timothy. 2020. Development of Deterministic Artificial Intelligence for Unmanned Underwater Vehicles (UUV). Journal of Marine Science and Engineering 8 (8). https://doi.org/10.3390/jmse8080578.

Sauser, Brian, Jose A. Ramirez-Marquez, Deepak Verma, and Richard Grove. 2006. “From TRL to SRL: The Concept of Systems Readiness Levels.” In Proceedings of the Conference on Systems Engineering Research (CSER).

Shahid, Seham, and Martin Agelin-Chaab. 2022. “A review of thermal runaway prevention and mitigation strategies for lithium-ion batteries.” Energy Conversion and Management: X 16. https://doi.org/10.1016/j.ecmx.2022.100310.

Stapersma, Hans Woud; Douwe. 2019. Design of Propulsion and Electric Power Generation Systems.

SED 1123 Episode 1123 [Interview]. Software Engineering Daily. SWE. 2014. Safe, Configurable, Pressure Tolerant Subsea Lithium Ion Battery System for Oil & Gas Deep Water Fields and ROVs.

Thamhain, Hans J. 2013. “Contemporary Methods for Evaluating Complex Project Proposals.” Journal of Industrial Engineering International 9 (1): 1-14. https://doi.org/10.1186/2251-712X-9-34.

Tiwari, Brij Kishor, and Rajiv Sharma. 2020. “Design and Analysis of a Variable Buoyancy System for Efficient Hovering Control of Underwater Vehicles with State Feedback Controller.” Journal of Marine Science and Engineering 8 (4). https://doi.org/10.3390/jmse8040263.

UST. 2024. “Underwater Navigation & Positioning.” Accessed 23 April 2024. https://www.unmannedsystemstechnology.com/expo/underwater-navigation-and-positioning/.

Vasudev, K. L., Sharma, R., & Bhattacharyya, S. K. 2018. “Shape optimisation of an AUV with ducted propeller using GA integrated with CFD.” Ships and Offshore Structures, 13(2), 194–207. https://doi.org/10.1080/17445302.2017.1351292

Vinida, K., and Mariamma Chacko. 2018. “An optimized speed controller for electrical thrusters in an autonomous underwater vehicle.” Int J Pow Elec & Dri Syst ISSN 2088, no. 8694 (2018): 1167.

Wang, Jin-Yun, and Wei Ke. 2022. “Development Plan of Unmanned System and Development Status of Uuv Technology in Foreign Countries.” Journal of Robotics and Control (JRC) 3, no. 2 (2022): 187-95.

Wang, Yan-Feng, and Jiang-Tao Wu. 2019. “Performance improvement of thermal management system of lithium-ion battery module on purely electric AUVs.” Applied Thermal Engineering 146: 74-84. https://doi.org/10.1016/j.applthermaleng.2018.09.108.

Watson, Simon, Daniel A. Duecker, and Keir Groves. 2020. Localisation of Unmanned Underwater Vehicles (UUVs) in Complex and Confined Environments: A Review. Sensors 20 (21). https://doi.org/10.3390/s20216203.

Wei, X., S. Xing, and D. Wei. 2021. “Application Status and Research Progress of Resin Matrix Composites in Unmanned Underwater Vehicle.” In Journal of Physics: Conference Series, vol. 2133, no. 1, p. 012022. IOP Publishing.

Wibisono, Arif, Piran Md Jalil, Hyoung-Kyu Song, and Byung Moo Lee. 2023. “A Survey on Unmanned Underwater Vehicles: Challenges, Enabling Technologies, and Future Research Directions.” Sensors 23 (17): 7321. https://doi.org/https://doi.org/10.3390/s23177321. https://login.wwwproxy1.library.unsw.edu.au/login?url=https://www.proquest.com/scholarly-journals/survey-on-unmanned-underwater-vehicles-challenges/docview/2862730308/se-2?accountid=12763

Yang, Y., Xiao, Y., & Li, T. 2021. “A Survey of Autonomous Underwater Vehicle Formation: Performance, Formation Control, and Communication Capability.” IEEE Communications Surveys and Tutorials, 23(2), 815–841. https://doi.org/10.1109/COMST.2021.3059998

Yu, Fei, Bo He, Jixin Liu, and Qi Wang. 2022. “Dual-branch framework: AUV-based target recognition method for marine survey.” Engineering Applications of Artificial Intelligence 115. https://doi.org/10.1016/j.engappai.2022.105291.

Yule, Peter L., and Woolner, David. 2008. The Collins Class Submarine Story: Steel, Spies, and Spin. Cambridge University Press.

Zhang, Bingbing, Daxiong Ji, Shuo Liu, Xinke Zhu, and Wen Xu. 2023. “Autonomous Underwater Vehicle navigation: A review.” Ocean Engineering 273. https://doi.org/10.1016/j.oceaneng.2023.113861.

Zhang, Feng, Hui Zhi, Puzhe Zhou, Yuandong Hong, Shijun Wu, Xiaoyan Zhao, and Canjun Yang. 2021. “State of charge estimation of Li-ion battery for underwater vehicles based on EKF–RELM under temperature-varying conditions.” Applied Ocean Research 114. https://doi.org/10.1016/j.apor.2021.102802.

Zhang, Wencan, Lihong Wu, Xiangwei Jiang, Xisheng Feng, Yiping Li, Junbao Zeng, and Chongde Liu. 2022. “Propeller Design for an Autonomous Underwater Vehicle by the Lifting-line Method based on OpenProp and CFD.” Journal of Marine Science and Application 21 (2): 106-114. https://doi.org/10.1007/s11804-022-00275-w.

Zhou, Jing, Yulin Si, and Ying Chen. 2023.. “A Review of Subsea Auv Technology.” Journal of Marine Science and Engineering 11, no. 6 (2023): 1119.

Zhu, Duanyi, Qiang Li, Xiuzhi He, Rongqi Wang, Qiang Liu, and Qian Li. 2024. “Preparation of highly dewetted porous steel for shallow water AUV based on laser ablation method.” Applied Surface Science 652 (2024): 159261.

Zhuang, Yuan, Xiao Sun, You Li, Jianzhu Huai, Luchi Hua, Xiansheng Yang, Xiaoxiang Cao, Peng Zhang, Yue Cao, Longning Qi, Jun Yang, Nashwa El-Bendary, Naser El-Sheimy, John Thompson, and Ruizhi Chen. 2023. “Multi-sensor integrated navigation/positioning systems using data fusion: From analytics-based to learning-based approaches.” Information Fusion 95: 62-90. https://doi.org/10.1016/j.inffus.2023.01.025.

Zutin, G. C., G. F. Barbosa, P. C. de Barros, E. B. Tiburtino, F. L. Kawano, and S. B. Shiki. 2022. “Readiness Levels of Industry 4.0 Technologies Applied to Aircraft Manufacturing—A Review, Challenges, and Trends.” International Journal of Advanced Manufacturing Technology 120 (1-2): 927-943. https://doi.org/10.1007/s00170-022-08769-1.