Sensor-Carrying Platforms

Information and communication technology, autonomy, and miniaturization in terms of, for example, microelectromechanical systems are enabling technologies with significant impact on the development of sensors, sensor-carrying platforms, control systems, data gathering, storage, and analysis methods. Sensor-carrying platforms are grouped in stationary devices such as landers and moorings to dynamic platforms such as marine robotics, ships, aerial systems, and remote-sensing satellites from space. Lately, the development of low-cost small satellites with customized payload sensors and accessible mission control centers has opened for a democratization of the space for remote sensing as well. The mapping and monitoring strategy may be carried out by each type of sensor-carrying platform suitable for the mission. However, we see a quantum leap by operating heterogeneous sensor-carrying platforms for the most efficient mapping and monitoring in spatial and temporal scales. We are facing a paradigm shift in terms of resolution and coverage capabilities. There have been several research efforts to improve the technology and methodology for mapping and monitoring of the oceans. Today, we see that the mapping coverage may be 100–1000 times higher than the state-of-the-art technology 6 years ago. The entailed increase in data harvesting does also create new challenges in handling of big data sets. It is an increasing need to update the oceanographic and ecosystem numerical model capabilities, taking full benefit of the ongoing shift in technology. The Arctic can truly be characterized as a remote and harsh environment for scientific operations and even more demanding during the Polar Night due to the darkness. During winter operations, extreme coldness may also be a challenge dependent on the weather conditions. Enabling technology and proper operational procedures may be the only way to reveal and understand the processes taking place there. The spatial scale is enormous, and as several research campaigns have already taught us, the variability is huge not only during the seasons but also over the years. This clearly also tells us the importance of prolonged presence. In this chapter, we will briefly present the various sensor-carrying platforms and payload sensors. We will also describe the philosophy behind integrated operations using heterogenous platforms and why and how to bridge science and technology being successful in the development of autonomous systems for efficient and safe operations. Examples and experience from Arctic missions will also be presented.acceptedVersionThis is a post-peer-review, pre-copyedit version of an article. Locked until 9/4-2022 due to copyright restrictions. The final authenticated version is available online at: http://dx.doi.org/10.1007/978-3-030-33208-

Towards Autonomous Shipping – Exploring Potential Threats and Opportunities in Future Maritime Operations

This article presents findings from an ongoing research project aiming to study the future of shipping operations with a specific focus on issues related to human roles, responsibilities and the organization of work. A focus group with representatives for the Swedish shipping cluster (n = 6) and academia (n = 2) has been conducted to explore potential strengths, weaknesses, opportunities and threats (SWOT) with the developments towards autonomous shipping. The results show an overall concern for how to realize the transition between today’s maritime traffic and a future setting where vessels may be operated from shore. Technology to automate navigational tasks and increase the degree of autonomy in shipping are developing, but more attention needs to be paid to the transition of work that may accompany the ongoing developments. Clear roles, responsibili- ties and a definition of potential operator competences need to be formulated to ensure a human-centered development for safer shipping.</p

A risk model for autonomous marine systems and operation focusing on human–autonomy collaboration

Autonomous marine systems, such as autonomous ships and autonomous underwater vehicles, gain increased interest in industry and academia. Expected benefits of autonomous marine system in comparison to conventional marine systems are reduced cost, reduced risk to operators, and increased efficiency of such systems. Autonomous underwater vehicles are applied in scientific, commercial, and military applications for surveys and inspections of the sea floor, the water column, marine structures, and objects of interest. Autonomous underwater vehicles are costly vehicles and may carry expensive payloads. Hence, risk models are needed to assess the mission success before a mission and adapt the mission plan if necessary. The operators prepare and interact with autonomous underwater vehicles to carry out a mission successfully. Risk models need to reflect these interactions. This article presents a Bayesian belief network to assess the human–autonomy collaboration performance, as part of a risk model for autonomous underwater vehicle operation. Human–autonomy collaboration represents the joint performance of the human operators in conjunction with an autonomous system to achieve a mission aim. A case study shows that the human–autonomy collaboration can be improved in two ways: (1) through better training and inclusion of experienced operators and (2) through improved reliability of autonomous functions and situation awareness of vehicles. It is believed that the human–autonomy collaboration Bayesian belief network can improve autonomous underwater vehicle design and autonomous underwater vehicle operations by clarifying relationships between technical, human, and organizational factors and their influence on mission risk. The article focuses on autonomous underwater vehicle, but the results should be applicable to other types of autonomous marine systems