Autonomous Unmanned Aerial Vehicle (UAV) swarms navigating complex environments require energy-efficient pathfinding algorithms to optimize performance and extend operational lifespan. To address the NP-hard problem of Multi-Agent Path Finding (MAPF), this paper introduces the Adaptive Algorithmic Determination for Advanced Pathfinding (AADAP) approach. AADAP is a white-box approach for a self-adaptive system that dynamically selects the most suitable pathfinding approaches from a diverse portfolio—including specific deterministic methods (e.g., Dijkstra’s), heuristic techniques (e.g., A*), bio-inspired algorithms (e.g., Ant Colony Optimization), evolutionary strategies (e.g., Genetic Algorithms), and probabilistic methods (e.g., Rapidly-Exploring Random Trees)—based on real-time environmental conditions and UAV swarm characteristics.By integrating with Geographic Resources Analysis Support System (GRASS GIS) for enhanced geospatial data synchronization, AADAP employs a decision-making mechanism that evaluates algorithm performance metrics in relation to environmental complexity and swarm dynamics. We evaluated AADAP using the Webots simulation environment with simulated DJI Matrice 300 RTK UAVs. The results indicate that AADAP excels in structured environments with a limited number of UAVs, outperforming deep learning-based methods and static algorithm selection approaches by approximately 14% in energy efficiency. Conversely, in complex terrains, deep learning methods demonstrate superior adaptability due to their data-driven learning capabilities.

Teaching in the area of embedded systems is challenging because both software and hardware development must be considered in an integrated manner. The experience gained through the practical hardware-related implementation of prototypes cannot simply be replaced by simulations. The question is, however, how can students gain as much practical experience as possible while taking into account real laboratory constraints? We present an approach that allows students to gain practical experience via virtual access to laboratory setups, which allows them to work in groups remotely and then continue their work in the labs, providing a similar experience as under normal lab conditions. For educators, we introduce a flexible environment that can be adopted to specialized embedded courses due to the used components and methodology of the ADDIE teaching model.

Machine learning models are typically evaluated directly on data, in simulated environments or in real conditions. Because smart farming involves cyber physical systems, location information, environment conditions, hardware configurations and timing issues matter. Therefore, it is desirable to perform system testing in real conditions. However, in the agricultural domain, this is often not feasible due to economic constraints or due to the fact that one would have to wait for the crops to grow before conducting the evaluation. Therefore, we propose an architecture and a prototypical implementation for lab-based system testing of machine learning based cyber physical systems in the agricultural domain.

The analysis of real-time properties is crucial in safety critical areas. Systems have to work in a timely manner to offer correct services. The analysis of timing properties is particularly difficult for distributed systems when complex interferences between individual tasks can occur. Considering only critical instances, as analytic approaches do, may deliver pessimistic results leading to higher production costs. In previous works we introduced a state-based approach to validate task-and end-to-end deadlines for distributed systems. To improve scalability and reduce the analysis time, the approach computes the state spaces of the individual resources in a compositional fashion. For this, abstraction and composition operations were defined to remove those parts of the inputs of resources which have no influence on the response times of the allocated tasks. In this work, a new abstraction technique is introduced for scenarios where event bursts occur. Further, we extend our approach for systems with cyclic dependencies among the resources. We evaluate our approach on a set of example scenarios and compare the results with the state-of-the-art tool Uppaal.

The analysis of real-time properties is crucial in safety critical areas, and is particularly difficult for distributed systems as complex interferences between tasks of different priorities can occur. In previous works we have introduced a state-based analysis approach to validate end-to-end deadlines for distributed systems, where the state spaces of all resources, such as processors and buses, are computed in a compositional fashion. For this, abstraction and composition operations were defined to adequately handle task and resource dependencies. During the design process of a system changes occur typically on both the specification and implementation level, such that already performed analyses of the system have to be repeated. In this work, we define a methodology to adequately handle such changes and to determine the minimal part of the affected architecture. For this, we define an appropriate refinement relation between state spaces of the resources. We use contracts to further reduce the re-validation effort. This check takes place at a higher design level, where only the specification is considered.

In this work we address evolving systems, which are basically collaborative and distributed systems building up a larger scale system of system (SoS). These systems are able to adapt the current architecture to some changes in the environment. Constituent systems of a SoS, which represent the basic elements of our modeling approach, operate with different degrees of freedom and as a result the self-adaptation and cooperation between a set of constituent systems is driven by local needs. Based on our former work [11], we propose a well-defined modelling approach for SoS capturing both static and dynamic aspects. The aim is to address on the one hand the required flexibility to adapt the systems during run-time, and on the other hand to guarantee that the SoS reacts still in a safe manner. For this, we will use the contract paradigm for both the specification of legal configurations of the SoS, and to specify the dynamicity model, describing how the SoS architecture can change during run-time. Further, we depict how to adapt a system level analysis technique in order to check the dynamicity model against the invariants of the SoS. With this, we are able to determine, whether the SoS can reach some critical configurations. This enables us to modify the dynamicity model in an adequate manner.

We present a Tool Suite which supports the (re-)construction of a behavioral model of a legacy component based on a learning approach by exploiting knowledge of known models of the existing component environment. This in turn enables to check whether the legacy component can be integrated correctly into its environment.

The next generation of advanced mechatronic systems is expected to enhance their functionality and improve their performance by context-dependent behavior. Therefore, these systems require to represent information about the complex environment and changing sets of collaboration partners internally. This requirement is in contrast to the usually assumed static structures for embedded systems. In this paper, we present a model-driven approach which overcomes this situation by supporting dynamic data structures while still guaranteeing that valid worst-case execution times can be derived. It supports a flexible resource management which avoids to operate with the prohibitive coarse worst-case boundaries but instead supports to run applications in different profiles which guarantee different resource requirements and put unused resources in a profile at other applications' disposal. By supporting the proper estimation of worst case execution time (WCET) and worst case number of iteration (WCNI) at runtime, we can further support to create new profiles, add or remove them at runtime in order to minimize the over-approximation of the resource consumption resulting from the dynamic data structures required for the outlined class of advanced systems.

Based on a well-defined component architecture the tool supports the synthesis of so-called real-time statecharts from timed sequence diagrams. The two step synthesis process addresses the existing scalability problems by a proper decomposition and allows the user to define particular restrictions on the resulting statecharts.

The next generation of advanced mechatronic systems is expected to use its software to exploit local and global networking capabilities to enhance their functionality and to adapt their local behavior when beneficial. Such systems will therefore include complex hard real-time coordination at the network level. This coordination is further reflected locally by complex reconfiguration in form of mode management and control algorithms. We present in this paper the integration of two tools which allow the integrated specification of real-time coordination and traditional control engineering specifically targeting the required complex reconfiguration of the local behavior.