Spacecraft Autonomy Levels

The possible transition to fully autonomous cars represents a paradigm shift, which is likely to have a profound impact on driving experience and automobile technology acceptance. Using an online questionnaire, Rodel et al. [7] have found that measures for User Acceptance (UA) and User Experience (UX) decline with increasing autonomy level. In this study, we investigate the differences in UA and UX for vehicles with different levels of automation in a more immersive context. We used a simple driving simulator setup in a virtual reality environment (using an Oculus Rift headset). We designed three tasks which each represented a different level of automation and asked participants (N = 17) to fill out the Car Technology Acceptance Model (CTAM) questionnaire after using each autonomy level. The immersion of the simulator setup was assessed with a standardized questionnaire. In contrast to Rodel et al. [7] results do not show a general decline in UA and UX with increasing autonomy, but suggest that Performance Expectancy, Perceived Safety and Social Influence are significantly higher for the fully automated condition than for no automation. The scores for immersion ranging about the average of benchmark evaluations indicate that the users felt quite immersed, but that there is still room for improving the VR setup.

Levels of Autonomy are an important guide to structure our thinking of capability, expectation and safety in autonomous systems. Here we focus on autonomy in the context of digital healthcare, where autonomy maps out differently to e.g. self-driving cars. Specifically we focus here on mapping levels of autonomy to clinical decision support systems and consider how these levels relate to safety assurance. We then explore the differences in the generation of safety evidence that exist between medical applications based on supervised learning (often used for prediction tasks such as in diagnosis and monitoring) and reinforcement learning (which we recently established as a way for AI-guided medical intervention). These latter systems have the potential to intervene on patients and should therefore be regarded as autonomous systems.

Smart systems are becoming increasingly ubiquitous and consequently transforming our lives. The level of system autonomy plays a vital role in the development of smart systems as it profoundly affects how people and these systems interact with each other. However, to date, there are very few studies on human interaction with such systems. This paper presents findings from two field studies where two different prototypes for automating energy tariff-switching were developed and evaluated in the wild. Both prototypes offer flexible autonomy by which users can shift the system's level of autonomy among three options: suggestion-only, semi-autonomy, and full autonomy, whenever they like. Our findings based on thematic analysis show that flexible autonomy is a promising way to sustain users' engagement with smart systems, despite their occasional mistakes. The findings also suggest that users take responsibility for the undesired outcomes of automated actions when delegation of autonomy can be adjusted flexibly.

Resilience attributes are turned upside down when introducing autonomous maritime transport systems, as technology replaces or assists human functions. We enter an unknown world where we need to rely on autonomous functions and the collaboration between humans and technology. Since less human interaction might cause challenges and effects not yet uncovered, Kaber (2018)[1] discussed levels of autonomy in the context of human automation interaction (HAI). The paper particularly looks at the use of levels of autonomy as taxonomies to structure and improve analysis of human performance, workload, but also situational awareness as well as some of the problems that this may cause. The introduction of increasing automation changes the way human and machine interact in many ways. As such, to ensure resilience in autonomous transport systems, these human and machine interaction challenges must be addressed and systemized, and in this paper the different awareness categories humans, technology and external (Fjørtoft, K.E., 2021), are revisited and linked to resilience. Also, as an extension on how to utilize integrated planning for autonomous transport systems (IPA), as presented by Fjørtoft, et al. (2023), this paper further details emerging issues related to human and machine interface by presenting a framework for how to secure resilience during the design, plan and follow up autonomous ship operations. In other words, the ability to assess and ensure resilience when planning operations in autonomous maritime transport system, while accounting for the interaction between humans and technology. This is of particular relevance due to the likelihood of vessels sailing with different degrees of autonomy, depending on the ongoing operation and its surroundings. Described as operational envelopes in "Towards approval of autonomous ship systems by their operational envelope" (Rødseth 2021), the overall operation must be broken down into sub-operations such as sailing in open seas, berthing, cargo loading/unloading (crane operation), etc.The framework is exemplified through a case study, by combining research results from the Norwegian project MARMAN and EU projects like AUTOSHIP and AEGIS.

This paper addresses the challenge of embedded computing resources required by future autonomous Unmanned Aircraft Systems (UAS). Based on an analysis of the required onboard functions that will lead to higher levels of autonomy, we look at most common UAS tasks to first propose a classification of UAS tasks considering categories such as flight, navigation, safety, mission and executing entities such as human, offline machine, embedded system. We then analyse how a given combination of tasks can lead to higher levels of autonomy by defining an autonomy level. We link UAS applications, the tasks required by those applications, the autonomy level and the implications on computing resources to achieve that autonomy level. We provide insights on how to define a given autonomy level for a given application based on a number of tasks. Our study relies on the state-of-the-art hardware and software implementations of the most common tasks currently used by UAS, also expected tasks according to the nature of their future missions. We conclude that current computing architectures are unlikely to meet the autonomy requirements of future UAS. Our proposed approach is based on dynamically reconfigurable hardware that offers benefits in computational performance and energy usage. We believe that UAS designers must now consider the embedded system as a masterpiece of the system.

This chapter discusses autonomous and unmanned systems, their anticipated missions and tasks in times of peace and war, and the rules applicable to them while operating at sea with or without human intervention. These systems include both unmanned surface vessels and unmanned underwater vehicles. Rapid advances in artificial intelligence and maritime technology will allow unmanned systems to operate with some level of autonomy. Nonetheless, the integration of these systems to create a hybrid fleet of manned and unmanned platforms raises issues about their legal status as a ship, the rights and duties that inure to such systems regarding navigation at sea, and the ability of these systems to autonomously comply with existing international rules regarding safety of navigation, and whether such systems can conduct operations consistent with the law of armed conflict.KeywordsUnmanned systemsAutonomyNavigational rights and freedomsWarshipBelligerent rightsSovereign immunity

There exists a connection between a growing number of Unmanned Autonomous Systems (UAxS) and a number of possible security challenges. The likelihood that swarms of UAxS will be employed in high populated mega-city environments in the long and mid-term exacerbates these future challenges. The problem is two-fold: UAxS are likely to be used as additional capability for friendly forces, but they are also to be considered as possible threat. The challenges that an autonomous system poses depend not only on their physical vulnerability or the security of the networks and data, but also on the interaction with traditional troops and operating procedures to be followed, their levels of autonomy and Command and Control (C2) chain behind their employment. An extensive experimentation is necessary to address some of these challenges in a Concept Development and Experimentation (CD&E) process aimed to envision solutions. Modelling and Simulation (M&S) tools and architecture are essential to speed up this process in a cost effective way. This paper presents how the development of a particular air operational scenario in an urban environment, according to the Simulation Interoperability Standard Organization (SISO) guide using M&S tools, can support the capability development process for future UAxS as well as their countermeasures.

Aluminum alloys are the subject of increasing interest in the automotive industry, as well as the aircraft industry, aiming to reduce the weight of components and also allowing a profit in term of energy saving. In assembly process, riveting has been widely used in the aircraft industry, whereas welding seems to be available in the car industry in the case of aluminum alloys. Nevertheless, conventional fusion welding can generate defects, such as gas porosity, oxide inclusions, solidification cracking (hot tearing), reduced strength in both the weld, and heat-affected zone (HAZ), which could limit its development. Electron beam welding (EBW) has unique advantages over other traditional fusion welding methods due to high energy density, deep penetration, large depth-to-width ratio, and small HAZ. EBW has been developed for many years and is being increasingly implemented in various industrial applications. In many cases, it has proven to be an efficient method for joining difficult to weld materials. One of the major problems associated with the EBW is how to select a proper combination of the process parameters because improper selection of these parameters causes defects in the weld joint, which could seriously influence the weld mechanical properties. This work introduces an overview of the EBW process, its parameters, process simulation, process optimization, and finally characterization of the electron beam weld joint of 2219 aluminum alloy.

Application of maraging steels via selective laser melting process in the automotive industry was unavoidably involved in the resistance spot welding with conventional steels. Due to the rapid cooling rate of welding process, selective laser melted maraging steels with unique chemical components and stack microstructure could induce the different microstructural evolution, resulting in the complicated fracture behavior in the spot welds. This paper developed a FEA model to predict the fracture mode of spot welds of DP600 to maraging steel under KSII test conditions, and the effect of test conditions and printing orientations was studied. A method was proposed to calculate the material properties of fusion zone by introducing the combined effect of melting DP600 and maraging steels via selective laser melting, resulting in the accurate prediction of fracture mode and strength of spot welds. A diffusion layer with lower strength was found around the fusion zone and the fracture path propagated in the region, resulting in the partial interfacial failure of spot welds. Meanwhile, the printing orientation had no significant effect on the fracture mode and strength of spot welds, but the different material properties of maraging steels could affect the fracture displacement of spot welds. These findings could pave a way to guide the application of maraging steels via selective laser melting process in multiple industries, especially in the automotive industry.

The Autonomous Industrial Plant -Future of Process Engineering, Operations and Maintenance

<title>ABSTRACT</title> <p>The Robotic Technology Kernel (RTK) is a government-owned library of reusable software modules based on the first generation Robotic Operating System (“ROS 1”) that can be formed into “autonomy stacks” for integration onto defense robotic platforms. RTK has been used to demonstrate autonomous ground vehicle capabilities spanning many programs and mission scenarios over the past five years. Future use of RTK is dependent, however, on migrating it to be compatible with the 2nd generation of ROS middleware (“ROS 2”) scheduled to replace ROS 1 in May, 2025.</p> <p>This paper summarizes the methodologies, systems, results, and lessons learned thus far from a project to migrate RTK to ROS 2 for the purpose of informing similar ongoing or future large software-centric activities within the ROS and defense robotics communities. A key conclusion is that a well-defined set of organizational practices and technical guidance can enable a large, heterogeneous team of developers from multiple industry, non-profit, and FFRDC organizations to successfully execute a complex DoD software task.</p> <p><bold>Citation:</bold> M. Boulet, R. DelGizzi, S. Lathrop, B. Leahy, J. Montez, G. Rucinski, M. Spinola, W. Thomasmeyer, J. Towler, “Collaborative Migration of an Autonomous Ground Vehicle Software System to ROS 2”, In <italic>Proceedings of the Ground Vehicle Systems Engineering and Technology Symposium</italic> (GVSETS), NDIA, Novi, MI, Aug. 10-12, 2021.</p>

The User Acceptance in Autonomous Vehicle is the main subject that has received the attention of researcher and professional in all around the world. The main objective of the paper is to encourage people towards technological acceptance of Autonomous vehicles. All benefit, after technological difficulties, do not come without a certain amount of challenges. The present challenges of Autonomous Vehicle are Assurance of system and Software, Sensing and Connectivity, Judgment, and Verification and Validation. This paper helps to provide more information regarding this technology. It helps the users to understand how safe and best this technology to use. To better predict, explain and increase User acceptance, we need to better understand why people will not accept or reject this technology. To use the knowledge obtained in order to foresee improved knowledge has on the actions of both the automotive industry and government. These papers provide practical evidence of road accidents as well as it compares Manual and Automatic Cars, which helps users to decide what good for them. There are questions asked by every individual who has to be answered. Among those questions, there is a common question asked by every user “How safe are you in an autonomous vehicle”?

The exoskeletons are being developed and recommended worldwide as wearable devices that could be used by workers exposed to demanding working conditions, such as overhead work. Many studies, designed for laboratory research, have been carried out so far and most of them came to the same kind of conclusions. With these findings it is expected that user acceptance (UA) and intention of use (IU) would not be a concern. This study was designed to assess UA and IU of an upper limb exoskeleton, Skelex MARK 1.3®, in a group of workstations, where technical and organizational measures implemented were not enough to reduce the exposure risk to overhead work and/or work above shoulder level, from an automotive industry assembly line. The exoskeleton was tested in 6 workstations, by 88 workers, during 4 consecutive weeks, since UA and IU are influenced by factors which only exist in real working scenarios.

Autonomous systems provide tangible benefits in the field of human-computer interaction (HCI) by reallocating work from human operators to suitable machine substitutes. However, improper implementations of autonomy in HCI systems have led to dire consequences. As such, the expansion of autonomy in research and industry must be matched by solutions that properly balance the interaction between human and machine. Early human-computer teams relied on multiple human operators working with one or just a few complex machines. With the growth of technology and improved autonomy, however, this trend has gradually reversed in that multiple complex machines are now supervised and operated by individual human controllers. Past research suggests that simply increasing autonomy fails to address the imbalance between human and machine within a cooperative mission scenario. Instead, adaptive automation (AA) has been demonstrated to be a viable solution in balancing the levels of autonomy between human and machine. Previous studies have verified the existence of relationships between levels of autonomy, human cognition, and system performance. In the context of single-operator multiple-agent scenarios, AA systems allow the levels of automation to dynamically adapt to the needs of both the human and the machine. This research explores various methods of invoking AA that aims to balance the level of automation between the human and machine within a simulated multitasking scenario. As such, a military-inspired simulation testbed was designed and implemented to compare the effects of different adaptation mechanisms on objective task performance and operator cognitive workload. Comparisons of four adaptation conditions support the use of AA as opposed to random or adaptable automation mechanisms for maintaining overall mission performance requirements. Results indicate increased operator utilization and situation awareness over time with decreased subjective workload scores in the AA conditions compared to the adaptable and random automation conditions.

The paper describes a key risk area threatening the widespread deployment of unmanned air vehicles (UAVs), that of attaining high levels of autonomy. Autonomy is loosely defined in the context of UAVs and the meaning of ‘level of autonomy’ discussed. The paper argues that the achievement of high levels of autonomy is not merely a function of increasing machine intelligence but also of maintaining the human operator’s engagement with the decision making process and retaining human authority. An assumption is that a human being in the loop will be a requirement for safety, flight clearance and legal reasons on early systems. Therefore, developers of highly autonomous systems are presented with a paradox. It will be argued that the human must be placed at the centre of the design process and consequently human factors, the human machine interface and the system architecture become critical to achieving high levels of autonomy. This quality impacts on the entire knowledge acquisition and design cycle and broadens what is meant by that term placing it as a discipline firmly in the systems design community. The paper concludes by outlining the key barriers to the successful development of highly autonomous UAVs.