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Spearheading the adoption of trustworthy AI and cyber security across the triad of automotive, transportation, and logistics embark on rigorous evaluations of applications rooted in open hardware/software ecosystems. Open innovations at chip, embedded and/or system level foster research on security-, safety-, privacy-, and accountability-by-design. Cyber-physical system of systems amplifies the fortress of AI-powered solutions and cyber-physical security, with particular emphasis on open-source frameworks. This paper presents an integrated design flow methodology that incorporates chip, embedded and system-level design and development. The proposed methodology is aligned with the recent trends and state-of-the-art dealing with open-source hardware and software development. 

Multi-UAV mission control requires a software solution that helps the operator to supervise individual UAVs and coordinate groups of units. This paper explores the experience and methodology of a User Interface for multi-UAVs systems developed for supporting a European project to aggregate heterogeneous UAVs in farming domain. The aim of the proposed system is to support the operator with the extra workload required for supervision of several UAVs while keeping their Situational Awareness (SA) high. The solution is evaluated with respect to workload and SA. A text-to-speech (TTS) approach is also evaluated to assess its effect on these metrics. The results show that while a TTS solution reduces the operator's workload, over-reliance on the system can cause loss of SA for properties which are not directly presented by TTS.

The advent of powerful control units and the widespread availability of cheap computers have significantly increased the role of artificial intelligence (AI) in various sectors. In the field of maritime applications, this progress has led to the emergence of Edge AI as an important technology. This research focuses on the application of Edge AI to maritime vessels, addressing key aspects of maritime operations. Using Edge AI, we aim to improve the situation awareness and operational efficiency of marine vessels. This study explores Edge AI integration into marine environments and emphasizes its potential to improve on-board safety, navigation and decision-making processes. Our approach shows how smart units decentralized in large central systems can lead to more efficient and adaptive maritime operations and paving the way for a new era of technologically advanced and environmentally conscious maritime practices.

Mission planning for multi-agent autonomous systems aims to generate feasible and optimal mission plans that satisfy the given requirements. In this article, we propose a mission-planning methodology that combines (i) a path-planning algorithm for synthesizing path plans that are safe in environments with complex road conditions, and (ii) a task-scheduling method for synthesizing task plans that schedule the tasks in the right and fastest order, taking into account the planned paths. The task-scheduling method is based on model checking, which provides means of automatically generating task execution orders that satisfy the requirements and ensure the correctness and efficiency of the plans by construction. We implement our approach in a tool named MALTA, which offers a user-friendly GUI for configuring mission requirements,  a module for path planning, an integration with the model checker UPPAAL, and functions for automatic generation of formal models, and parsing of the execution traces of models. Experiments with the tool demonstrate its applicability and performance in various configurations of an industrial case study of an autonomous quarry. We also show the adaptability of our tool by employing it on a special case of the industrial case study.

The purpose of the article is to describe and problematise the practice-initiated idea of developing a digital tool for children in child welfare investigations and whether and how this welfare technology is useful for social workers. The results include interview data and descriptions of the research process. The social workers are of the opinion that the digital application increases the possibilities for children’s participation in child investigations, even though their main focus is to create an alliance with the parents. During the research process the digital tool has developed from an empirical idea to a conversation tool and been tested with different user groups. However, the law on procurement limits the possibilities for data storage if the digital tool is to be used in the future. In sum, in order to develop child protection work further, more practice-based research needs to be conducted so that researchers can develop the practice’s ideas and identify the obstacles, opportunities, organisational conditions and development needs. The social workers in this study believe that the digital tool is useful for accessing children's perspectives and experiences, even though relational work with children is not their main task in child welfare investigations. 

With the rapidly growing use of Multi-Agent Systems (MASs), which can exponentially increase the system complexity, the problem of planning a mission for MASs became more intricate. In some MASs, human operators are still involved in various decision-making processes, including manual mission planning, which can be an ineffective approach for any non-trivial problem. Mission planning and re-planning can be represented as a combinatorial optimization problem. Computing a solution to these types of problems is notoriously difficult and not scalable, posing a challenge even to cutting-edge solvers. As time is usually considered an essential resource in MASs, automated solvers have a limited time to provide a solution. The downside of this approach is that it can take a substantial amount of time for the automated solver to provide a sub-optimal solution. In this work, we are interested in the interplay between a human operator and an automated solver and whether it is more efficient to let a human or an automated solver handle the planning and re-planning problems, or if the combination of the two is a better approach. We thus propose an experimental setup to evaluate the effect of having a human operator included in the mission planning and re-planning process. Our tests are performed on a series of instances with gradually increasing complexity and involve a group of human operators and a metaheuristic solver based on a genetic algorithm. We measure the effect of the interplay on both the quality and structure of the output solutions. Our results show that the best setup is to let the operator come up with a few solutions, before letting the solver improve them.

Multi-agent systems have received a tremendous amount of attention in many areas of research and industry, especially in robotics and computer science. With the increased number of agents in missions, the problem of allocation of tasks to agents arose, and it is one of the most fundamental classes of problems in robotics, formally known as the Multi-Robot Task Allocation (MRTA) problem. MRTA encapsulates numerous problem dimensions, and it aims at providing formulations and solutions to various problem configurations, i.e., complex multi-agent missions. One dimension of the MRTA problem has not caught much of the research attention. In particular, problem configurations including Multi-Task (MT) robots have been neglected. However, the increase in computational power, in robotic systems, has allowed the utilization of parallel task execution. This in turn had the benefit of allowing the creation of more complex robotic missions; however, it came at the cost of increased problem complexity. Our contribution to the aforementioned domain can be grouped into three categories. First, we model the problem using two different approaches, Integer Linear Programming and Constraint Programming. With these models, we aim at filling the gap in the literature related to the formal definition of MT robot problem configuration. Second, we introduce the distinction between physical and virtual tasks and their mutual relationship in terms of parallel task execution. This distinction allows the modeling of a wider range of missions while exploiting possible parallel task execution. Finally, we provide a comprehensive performance analysis of both models, by implementing and validating them in CPLEX and CP Optimizer on the set of problems. Each problem consists of the same set of test instances gradually increasing in complexity, while the percentage of virtual tasks in each problem is different. The analysis of the results includes exploration of the scalability of both models and solvers, the effect of virtual tasks on the solvers' performance, and overall solution quality.

Emerging precision agriculture techniques rely on the frequent collection of high-quality data which can be acquired efficiently by unmanned aerial systems (UAS). The main obstacle for wider adoption of this technology is related to UAS operational costs. The path forward requires a high degree of autonomy and integration of the UAS and other cyber physical systems on the farm into a common Farm Management System (FMS) to facilitate the use of big data and artificial intelligence (AI) techniques for decision support. Such a solution has been implemented in the EU project AFarCloud (Aggregated Farming in the Cloud). The regulation of UAS operations is another important factor that impacts the adoption rate of agricultural UAS. An analysis of the new European UAS regulations relevant for autonomous operation is included. Autonomous UAS operation through the AFarCloud FMS solution has been demonstrated at several test farms in multiple European countries. Novel applications have been developed, such as the retrieval of data from remote field sensors using UAS and in situ measurements using dedicated UAS payloads designed for physical contact with the environment. The main findings include that (1) autonomous UAS operation in the agricultural sector is feasible once the regulations allow this; (2) the UAS should be integrated with the FMS and include autonomous data processing and charging functionality to offer a practical solution; and (3) several applications beyond just asset monitoring are relevant for the UAS and will help to justify the cost of this equipment.

How do children (aged 6-12 years) understand and make use of a digital tool that is under development? This article builds on an ongoing interdisciplinary research project in which children, social workers (the inventers of this social innovation) and researchers together develop an interactive digital tool (application) to strengthen children's participation during the planning and process of welfare assessments. Departing from social constructionism, and using a discursive narrative approach with visual ethnography, the aim of the article is to display how the children co-construct the application and contribute with "stories of life situations" by drawing themselves as characters and the places they frequent. The findings show that the children improved the application by suggesting more affordances so that they could better create themselves/others, by discovering bugs, and by showing how it could appeal to children of various ages. The application helped the children to start communicating and bonding when creating themselves in detail, drawing places/characters and describing events associated with them, and sharing small life stories. The application can help children and social workers to connect and facilitate children's participation by allowing them to focus on their own perspectives when drawing and sharing stories.

In this paper, a novel path planning algorithm for multiple robots using congestion analysis and control is presented. The algorithm ensures a safe path planning solution by avoiding collisions among robots as well as among robots and humans. For each robot, alternative paths to the goal are realised. By analysing the travelling time of robots on different paths using Petri Nets, the optimal configuration of paths is selected. The prime objective is to avoid congestion when routing many robots into a narrow area. The movements of robots are controlled at every intersection by organising a one-by-one passing of the robots. Controls are available for the robots which are able to communicate and share information with each other. To avoid collision with humans and other moving objects (i.e. robots), a dipole field integrated with a dynamic window approach is developed. By considering the velocity and direction of the dynamic obstacles as sources of a virtual magnetic dipole moment, the dipole-dipole interaction between different moving objects will generate repulsive forces proportional to the velocity to prevent collisions. The whole system is presented on the widely used platform Robot Operating System (ROS) so that its implementation is extendable to real robots. Analysis and experiments are demonstrated with extensive simulations to evaluate the effectiveness of the proposed approach.