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ABSTRACT In an effort to explore methods to improve overall system reliability, this systematic literature review (SLR) explores the current state of dynamic fault trees (DFTs) for complex, redundant systems that exhibit performance degradation over time. DFTs were first developed to manage and optimize redundant resources of computer system components, but have also been successfully used to manage redundant resources in aircraft and naval fuel distribution systems, transportation systems, industrial plants, computer networks, cardiac assist systems, and spacecraft systems. More recent DFT gates were added to identify, filter, and manage time‐dependent failures to minimize their impacts on the overall system performance and reliability, and have been applied to aircraft fault management systems, nuclear power plants, and alarm management systems. This SLR also explored the current state of technology for dynamically calculating not only the current capacity of system components, but also predicting their remaining useful life (RUL), remaining useful performance (RUP), and capacity degradation profiles. This is essential in understanding capacity degradation in the broader context of optimization of resources in complex, redundant systems. This SLR analysis revealed two gaps in the current technology and application of DFTs to complex systems. First, the usage of DFTs for failure management requires that the DFT is developed with a priori knowledge of specific failure modes, meaning that DFTs are inherently not scalable for new failures that are discovered. Second, although DFTs do address failure management, there are inherent limitations in the detection and application of partial failures or capacity degradation in the DFT infrastructure. [ABSTRACT FROM AUTHOR]

Taking fast action, and effectively utilizing the available resources, are important when conducting time-critical surveillance missions. In addition, the potential complexity of the search, such as the ruggedness of the terrain or large size of the search region, should be considered. Such issues can be tackled by using a heterogeneous fleet of manned and unmanned vehicles equipped with different sensors by considering speed, coverage and flexibility. This article centres on designing a fast solution approach for planning coverage missions through zoning and routing. The challenge is to cover an area where targets can be potentially found with a variable likelihood. The search area is modelled using a scoring map to support the choice of search in sub-areas, where the scores represent the importance of tasks. Heuristic approaches are proposed to automate search processes and plan missions, given a small time budget. [ABSTRACT FROM AUTHOR]

The increasing need for autonomy in space exploration missions is becoming more and more relevant in the design of missions to small bodies. The long communication latencies and sensitivity of the system to unplanned environmental perturbations mean autonomous methods could be a key design block for this type of mission. In this work, a fully autonomous Guidance, Navigation, and Control (GNC) methodology is introduced. This methodology relies on published CNN-based techniques for surface recognition and pose estimation and also on existing MPC-based techniques for the design of a trajectory to perform a soft landing on an asteroid. Combining Hazard Detection and Avoidance (HDA) with relative navigation systems, a Global Safety Map (GSM) is built on the fly as images are acquired. These GSMs provide the GNC system with information about feasible landing spots and populate a longitude–latitude map with safe/hazardous labels that are later processed to find an optimal landing spot based on mission requirements and a distance-fromhazard metric. The methodology is exemplified using Bennu as the body of interest, and a GSM is built for an arbitrary reconnaissance orbit. [ABSTRACT FROM AUTHOR]

Copyright of e & i Elektrotechnik und Informationstechnik is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Estimating the state of a system by fusing sensor data is a major prerequisite in many applications. When the state is time-variant, derivatives of the Kalman filter are a popular choice for solving that task. Two variants are the square-root unscented Kalman filter (SRUKF) and the square-root cubature Kalman filter (SCKF). In contrast to the unscented Kalman filter (UKF) and the cubature Kalman filter (CKF), they do not operate on the covariance matrix but on its square root. In this work, we modify the SRUKF and the SCKF for use on manifolds. This is particularly relevant for many state estimation problems when, for example, an orientation is part of a state or a measurement. In contrast to other approaches, our solution is both generic and mathematically coherent. It has the same theoretical complexity as the UKF and CKF on manifolds, but we show that the practical implementation can be faster. Furthermore, it gains the improved numerical properties of the classical SRUKF and SCKF. We compare the SRUKF and the SCKF on manifolds to the UKF and the CKF on manifolds, using the example of odometry estimation for an autonomous car. It is demonstrated that all algorithms have the same localization performance, but our SRUKF and SCKF have lower computational demands. [ABSTRACT FROM AUTHOR]

Legged robots in general, and quadrupedal structures in particular, have demonstrated high potential as a means of locomotion, possessing the ability to carry out tasks that traditional vehicles are unable to accomplish. In the last thirty years, legged locomotion technology has been advanced globally, leading to the creation of numerous equipment and techniques. This systematic review explores research studies from different academic databases, i.e. Web of Science, Scopus, and IEEE, to provide an overview of recent innovations and future directions in quadruped robotics. Advanced sensory systems, optimal structural design and smart control mechanisms are the key elements for enabling autonomous navigation and agile interaction of these robots with their environment. This systematic review explores the interconnected domains of structural design, control systems, and sensory integration. The analysis aims to identify common research themes and emerging trends by employing keyword co-occurrence and machine learning clustering techniques, providing a detailed understanding of the research landscape. [ABSTRACT FROM AUTHOR]

Autonomous navigation in agricultural environments is challenged by varying field conditions that arise in arable fields. State‐of‐the‐art solutions for autonomous navigation in such environments require expensive hardware, such as Real‐Time Kinematic Global Navigation Satellite System. This paper presents a robust crop row detection algorithm that withstands such field variations using inexpensive cameras. Existing data sets for crop row detection do not represent all the possible field variations. A data set of sugar beet images was created representing 11 field variations comprised of multiple grow stages, light levels, varying weed densities, curved crop rows, and discontinuous crop rows. The proposed pipeline segments the crop rows using a deep learning‐based method and employs the predicted segmentation mask for extraction of the central crop using a novel central crop row selection algorithm. The novel crop row detection algorithm was tested for crop row detection performance and the capability of visual servoing along a crop row. The visual servoing‐based navigation was tested on a realistic simulation scenario with the real ground and plant textures. Our algorithm demonstrated robust vision‐based crop row detection in challenging field conditions outperforming the baseline. [ABSTRACT FROM AUTHOR]

The growing appreciation of the critical importance of domestic industry in national security resilience has led to the recent issue of the Defence Industry Development Strategy (DIDS). This article outlines the DIDS strategy and discusses the seven priority areas for industry development and several topical policy areas, notably naval shipbuilding and autonomous systems. Throughout this discussion the development of skilled workforce is an important challenge with no clear resolution in sight. [ABSTRACT FROM AUTHOR]

The responsibility gap, commonly described as a core challenge for the effective governance of, and trust in, AI and autonomous systems (AI/AS), is traditionally associated with a failure of the epistemic and/or the control condition of moral responsibility: the ability to know what we are doing and exercise competent control over this doing. Yet these two conditions are a red herring when it comes to understanding the responsibility challenges presented by AI/AS, since evidence from the cognitive sciences shows that individual humans face very similar responsibility challenges with regard to these two conditions. While the problems of epistemic opacity and attenuated behaviour control are not unique to AI/AS technologies (though they can be exacerbated by them), we show that we can learn important lessons for AI/AS development and governance from how philosophers have recently revised the traditional concept of moral responsibility in response to these challenges to responsible human agency from the cognitive sciences. The resulting instrumentalist views of responsibility, which emphasize the forward-looking and flexible role of agency cultivation, hold considerable promise for integrating AI/AS into a healthy moral ecology. We note that there nevertheless is a gap in AI/AS responsibility that has yet to be extensively studied and addressed, one grounded in a relational asymmetry of vulnerability between human agents and sociotechnical systems like AI/AS. In the conclusion of this paper we note that attention to this vulnerability gap must inform and enable future attempts to construct trustworthy AI/AS systems and preserve the conditions for responsible human agency. [ABSTRACT FROM AUTHOR]

Our research is a value sensitive based approach to designing autonomous systems in a military context. Value sensitive design is an iterative process of conceptual, empirical and technical considerations. We enhance value sensitive design with Participatory Value Evaluation. This allows us to mine values of a large unorganized stakeholder group relevant to our context of research, namely Australian citizens. We found that value prioritizations differ depending on the context of use and that no one value fits all autonomous systems. General ethical frameworks can serve as high level guiding principles, but when it comes to actual technologies, a more nuanced and granulated approach to selecting and incorporating values is needed. Our research contributes to a greater awareness of the importance of inclusion of values in autonomous systems design in the context of defence. We suggest relevant frameworks and methods for values-based autonomous military systems design in the context of Australia, which may serve as a blueprint for other countries. Finally, we provide suggestions on how to operationalise values in systems design, as this is underrepresented in the current literature. [ABSTRACT FROM AUTHOR]

This article presents a comprehensive overview of the integration of robotic systems and artificial intelligence (AI) in military applications, focusing on land-based robotic platforms. It highlights the mobility mechanisms of military robots, including wheeled, tracked, and biomechanical (legged) systems, as well as the power sources and control technologies that enable their operation. The analysis extends to the leading nations in military robotics development, such as the United States, Japan, South Korea, and key European nations, and emphasizes the importance of emerging players like Singapore, India, and the UAE. The challenges related to AI integration, cybersecurity, and sustainability are discussed, along with the ethical considerations surrounding the future deployment of autonomous robotic systems in military contexts. Finally, the article points to the need for international collaboration and the establishment of regulatory frameworks to ensure the responsible and effective use of military robotics in a rapidly evolving technological landscape. [ABSTRACT FROM AUTHOR]

Object tracking is one of the major tasks for mobile robots in many real-world applications. Also, artificial intelligence and automatic control techniques play an important role in enhancing the performance of mobile robot navigation. In contrast to previous simulation studies, this paper presents a new intelligent mobile robot for accomplishing multi-tasks by tracking red-green-blue (RGB) colored objects in a real experimental field. Moreover, a practical smart controller is developed based on adaptive fuzzy logic and custom proportional-integral-derivative (PID) schemes to achieve accurate tracking results, considering robot command delay and tolerance errors. The design of developed controllers implies some motion rules to mimic the knowledge of experienced operators. Twelve scenarios of three colored object combinations have been successfully tested and evaluated by using the developed controlled image-based robot tracker. Classical PID control failed to handle some tracking scenarios in this study. The proposed adaptive fuzzy PID control achieved the best accurate results with the minimum average final error of 13.8 cm to reach the colored targets, while our designed custom PID control is efficient in saving both average time and traveling distance of 6.6 s and 14.3 cm, respectively. These promising results demonstrate the feasibility of applying our developed image-based robotic system in a colored object-tracking environment to reduce human workloads. [ABSTRACT FROM AUTHOR]

Simultaneous Localization and Mapping (SLAM) is a crucial function for most autonomous systems, allowing them to both navigate through and create maps of unfamiliar surroundings. Traditional Visual SLAM, also commonly known as VSLAM, relies on frame-based cameras and structured processing pipelines, which face challenges in dynamic or low-light environments. However, recent advancements in event camera technology and neuromorphic processing offer promising opportunities to overcome these limitations. Event cameras inspired by biological vision systems capture the scenes asynchronously, consuming minimal power but with higher temporal resolution. Neuromorphic processors, which are designed to mimic the parallel processing capabilities of the human brain, offer efficient computation for real-time data processing of event-based data streams. This paper provides a comprehensive overview of recent research efforts in integrating event cameras and neuromorphic processors into VSLAM systems. It discusses the principles behind event cameras and neuromorphic processors, highlighting their advantages over traditional sensing and processing methods. Furthermore, an in-depth survey was conducted on state-of-the-art approaches in event-based SLAM, including feature extraction, motion estimation, and map reconstruction techniques. Additionally, the integration of event cameras with neuromorphic processors, focusing on their synergistic benefits in terms of energy efficiency, robustness, and real-time performance, was explored. The paper also discusses the challenges and open research questions in this emerging field, such as sensor calibration, data fusion, and algorithmic development. Finally, the potential applications and future directions for event-based SLAM systems are outlined, ranging from robotics and autonomous vehicles to augmented reality. [ABSTRACT FROM AUTHOR]

Copyright of Araucaria is the property of Araucaria-Revista Iberoamericana de Filosofia, Politica y Humanidades and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Technological advancements are radically transforming the conduct of military conflicts. While these innovations offer significant operational advantages, they also raise major ethical and legal challenges. This study examines the impact of emerging technologies on the legitimacy of military actions and the decision-making process, emphasizing the necessity for robust legal and ethical frameworks to ensure compliance with international norms. The military system must align with technological requirements to meet training needs and the dynamics of the operational environment. The integration of Extended Reality (xR) into the decision-making process is essential for a robust and future-ready defence system. The study calls for a nuanced approach to integrating these technologies, balancing technological advantages with ethical obligations. [ABSTRACT FROM AUTHOR]

Autonomous systems, such as drones and rescue robots, are increasingly used during emergencies. They deliver services and provide situational awareness that facilitate emergency management and response. To do so, they need to interact and cooperate with humans in their environment. Human behaviour is uncertain and complex, so it can be difficult to reason about it formally. In this article, we propose IDEA: an adaptive software architecture that enables cooperation between humans and autonomous systems, by leveraging the social identity approach. This approach establishes that group membership drives human behaviour. Identity and group membership are crucial during emergencies, as they influence cooperation among survivors. IDEA systems infer the social identity of surrounding humans, thereby establishing their group membership. By reasoning about groups, we limit the number of cooperation strategies the system needs to explore. IDEA systems select a strategy from the equilibrium analysis of game-theoretic models that represent interactions between group members and the IDEA system. We demonstrate our approach using a search-and-rescue scenario, in which an IDEA rescue robot optimises evacuation by collaborating with survivors. Using an empirically validated agent-based model, we show that the deployment of the IDEA system can reduce median evacuation time by 13.6%. [ABSTRACT FROM AUTHOR]

This paper outlines the design and development of an advanced robotic system that integrates hardware implementation with theoretical simulation to address the need for versatile and user-friendly robotic solutions in various environments. Addressing the issue of limited adaptability in existing robotic systems, we propose a wireless, voice and gesture-controlled robot car with an integrated robotic arm capable of performing complex tasks such as line following, obstacle avoidance, object manipulation, and autonomous navigation over one-kilometer range. To improve operational efficiency and user involvement, this paper designs a multifunctional robotic platform that integrates user-friendly control interfaces with inexpensive, state-of-the-art sensor technologies. To achieve this, we integrate a variety of sensors, including ultrasonic sensors for precise distance measurement, infrared sensors for object detection and line following, an L298 motor driver for controlling geared motors, servo motors for controlling robotic arms, a flex sensor for claw control, and an mpu6050 accelerometer for gesture recognition. The system also uses a custom-made Bluetooth app for remote control, nRF24L01+ for long-range wireless control, and Arduino Mega and Nano for processing and control functions. The results demonstrate the robot functions well in dynamic conditions, and it can be used in hospitals to assist healthcare professionals, in restaurants for food delivery, and in industrial settings for object manipulation. The system's design proves robust in real-world scenarios, offering significant improvements in accessibility and operational efficiency. This study aligns with Sustainable Development Goals (SDGs) 3 (Good Health and Well-being), 9 (Industry, Innovation, and Infrastructure), and 17 (Partnerships for the Goals). The robotic arm's potential application in healthcare settings advances SDG 3, its contribution to industrial productivity advances SDG 9, and collaborations with tech companies to expand and improve the robot's capabilities promote SDG 17. [ABSTRACT FROM AUTHOR]

The increasing prevalence of Autonomous Systems (AS) powered by Artificial Intelligence (AI) in society and their expanding role in ensuring safety necessitate the assessment of their trustworthiness. The verification and development community faces the challenge of evaluating the trustworthiness of AI-powered AS in a comprehensive and objective manner. To address this challenge, this study conducts a semi-structured interview with experts to gather their insights and perspectives on the trustworthiness of AI-powered autonomous systems in healthcare. By integrating the expert insights, a comprehensive framework is proposed for assessing the trustworthiness of AI-powered autonomous systems in the domain of healthcare. This framework is designed to contribute to the advancement of trustworthiness assessment practices in the field of AI and autonomous systems, fostering greater confidence in their deployment in healthcare settings.

Construction sites often involve the manual transportation of heavy objects, posing risks to workers’ safety and efficiency. In this paper, it was proposed a design, development, and evaluation of a service robot specifically created for carrying heavy objects in the construction field. The robot is equipped with robust locomotion capabilities, intelligent perception systems, and efficient control mechanisms to navigate through complex environments and handle various types of loads. It was presented the technical design of the robot, including its hardware components, sensing modalities, and software architecture. Furthermore, it was discussed the experimental validation of the robot’s performance in real-world construction scenarios, highlighting its effectiveness in improving productivity, reducing labor costs, and enhancing workplace safety. Through this research, the aim is to demonstrate the potential of service robots as valuable assets in the construction industry, paving the way for future advancements in robotic assistance technologies.