Dear Colleagues,

This special issue is about two complementary aspects of unmanned underwater and surface vehicles (UxVs): i) autonomy which makes the vehicle as self-sufficient as possible, and ii) cooperation between two or more vehicles which is required for coordinated tasks. In order to enhance autonomy (cooperation) novel learning algorithms (communication and control protocols) may become resource hungry thus taxing the overall performance of the UxVs. If these two aspects may have been investigated independently so far, this special issue intends to highlight how ‘in between’ solutions could be beneficial for UxVs.

UxVs are an enabling technology for ocean exploration, monitoring, and sustainable exploitation. UxVs are designed to perform unmanned and autonomous data gathering over extended ocean areas. International scientific ocean observatories, such as Jerico, Ocean Observatory Initiative, Neptune Canada, and NorGlider, include underwater and surface vehicles to extend the sensing coverage to unravel the physical, chemical, and biological processes at different scales. UxVs are deployed to provide services such as sensor data muling, visual surveys of damaged areas or instrumentation, and remote mechanical maintenance of submerged instrumentation. UxVs increase safety, reduce operational costs and CO₂ emissions compared to operating a manned vessel to perform all these tasks. For these reasons, they are of great interest to offshore industries, such as fisheries, aquaculture, O&G, and offshore windfarms. However, UxVs need to integrate sophisticated learning algorithms to be truly autonomous in carrying out missions, while working under the constraints of limited computational and battery resources. Furthermore, differently from aerial unmanned vehicles, underwater vehicles cannot rely on radio wave-based communication and positioning systems ensuring stable control links and precise location estimates. 

Cooperation implies information exchange among the vehicles. This exchange can be implemented through underwater wireless communications. If we focus on underwater acoustic communications (UAC) alone, over the past three decades, numerous algorithms and methods for point-to-point and multipoint systems have been developed. UAC systems face a wide variety of the acoustic channel conditions, which often push the receiving algorithms to their performance limits. In water, sound propagates over longer ranges than radio waves. However, UAC performance depends on channels that have a long multipath, large Doppler spread, with a limited bandwidth that depends on the transmission range and center frequency, and a long propagation delay. UAC systems have been improved for different use cases, and higher spectral efficiency can be achieved through channel equalization and synchronization, multiple input multiple output systems, spread-spectrum modulations, linear and nonlinear channel estimators (e.g., sparse, Bayesian, subspace methods, etc.), linear and nonlinear interference cancellation techniques, iterative decoders, and so on. All these techniques are promising for the use of UAC in UxVs. However, the variety of space-time scales variations in underwater acoustic channel conditions can negatively affect their reliability. This calls for communication and control protocols able to reconfigure themselves in situ depending on the environmental conditions and the tasks of UxVs.

Thanks to their technological maturity, UxVs are routinely used in several operational activities. However, most commercially deployed systems can only be used in a small number of autonomous operations and are quite limited in their ability to deviate from nominal missions and cope with the uncertainties of real-world scenarios. Having UxVs performing complex missions (e.g., deep water, under ice, etc.) over extended time periods (e.g., weeks, months, or years) still poses many challenges. In these scenarios, the environment will change over the lifetime of the system or may be unknown when the mission begins, and vehicle components might fail or their performance change overtime (e.g., loss of camera calibration, communications errors, etc.). At the same time, external events in the surrounding underwater environment might trigger an increase in data uncertainty. For the end user of the data, it is crucial to have not only the collected data but also complementary information that can be used for data uncertainty estimations. For instance, for an adaptive automated or autonomous navigation control based on UxVs, the underlying decision-making algorithms need to balance performance with risk mitigation and data quality and availability.

In all these situations, the robot faces decisions in terms of trading off its objectives, the utility of the data that it collects, and its own safety. This Special Issue calls for high-quality, unpublished research papers on recent advances in the development of novel algorithms and methods for enhancing the autonomy and cooperation of UxVs. We welcome contributions that present and solve open research problems or that integrate efficient novel solutions, performance evaluations, and comparisons with existing solutions. Theoretical and experimental studies concerning typical and emerging underwater wireless communication systems and protocols for UxVs, and use cases enabled by recent advances in the autonomy and cooperation of UxVs are encouraged.

Potential topics include, but are not limited to, the following:

• Cooperative cognitive control for UxVs;
• Cognitive communication for UxVs;
• Swarming autonomous maritime vehicles;
• Cooperative localization and navigation;
• Machine learning for communication or control;
• Distributed algorithms for UxVs;
• Adaptive control algorithms for UxVs;
• Data quality and uncertainty estimation in monitoring with UxVs.

Dr. Beatrice Tomasi
Dr. Andrea Munafo
Dr. Pierre-jean Bouvet
Dr. Antony Pottier
Dr. Rodica Mihai
Guest Editors

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