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Swarm Intelligence and Cyber-Physical Systems: Concepts, Challenges and Future Trends

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Abstract

Swarm Intelligence (SI) is a popular multi-agent framework that has been originally inspired by swarm behaviors observed in natural systems, such as ant and bee colonies. In a system designed after swarm intelligence, each agent acts autonomously, reacts on dynamic inputs, and, implicitly or explicitly, works collaboratively with other swarm members without a central control. The system as a whole is expected to exhibit global patterns and behaviors. Although well-designed swarms can show advantages in adaptability, robustness, and scalability, it must be noted that SI system haven’t really found their way from lab demonstrations to real-world applications, so far. This is particularly true for embodied SI, where the agents are physical entities, such as in swarm robotics scenarios. In this paper, we start from these observations, outline different definitions and characterizations, and then discuss present challenges in the perspective of future use of swarm intelligence. These include application ideas, research topics, and new sources of inspiration from biology, physics, and human cognition. To motivate future applications of swarms, we make use of the notion of cyber-physical systems (CPS). CPSs are a way to encompass the large spectrum of technologies including robotics, internet of things (IoT), Systems on Chip (SoC), embedded systems, and so on. Thereby, we give concrete examples for visionary applications and their challenges representing the physical embodiment of swarm intelligence in autonomous driving and smart traffic, emergency response, environmental monitoring, electric energy grids, space missions, medical applications, and human networks. We do not aim to provide new solutions for the swarm intelligence or CPS community, but rather build a bridge between these two communities. This allows us to view the research problems of swarm intelligence from a broader perspective and motivate future research activities in modeling, design, validation/verification, and human-in-the-loop concepts.

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... While significant progress has been made in understanding the above dynamics, there remains a gap in the full translation of these principles into practical, scalable solutions for UAVs, robotic systems and other autonomous organizational structures. Existing work focuses on isolated elements of swarm behaviour that have not been comprehensively integrated into broader, real-world applications in autonomous systems (Kolling et al., 2015;Schranz et al., 2021;Araujo et al., 2023). This gap in the literature highlights the need for research that not only examines these behaviours holistically, but also tests their potential impact on system resilience, efficiency and adaptability in complex, dynamic environments. ...
... The concept of swarm intelligence offers a new paradigm for designing decentralized autonomous systems that can outperform traditional hierarchical structures, especially in environments that require flexibility and dynamic responses to unpredictable challenges. These systems have a wide range of applications, from military drones and robot fleets to disaster response, environmental monitoring, logistics and even business management processes (Schranz et al., 2021). ...
... Multi-agent systems theory extends the basic principles of swarm intelligence by focusing on how agents can co-operate, coordinate and negotiate tasks in a shared environment. In practical terms, multi-agent systems theory provides the computational tools and algorithms necessary to model swarm behavior in artificial systems such as resource allocation, task allocation and communication protocols (Schranz et al., 2021) Swarm intelligence has important implications for the development of unmanned systems, including drones, robotic swarms and autonomous vehicles. The application of swarm principles to these systems enables them to perform complex tasks more efficiently than individual agents or traditional centralized systems. ...
Article
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... [29] IoT and Cyber-Physical Systems Integration of Swarm Intelligence algorithms in IoT-based systems and cyberphysical systems to enhance performance, scalability, and adaptability. [30,31] Swarm Robotics Development and deployment of swarm robotics for various applications such as search and rescue, agriculture, space exploration, and autonomous systems. [4][5][6]9,[32][33][34][35][36][37][38][39][40][41][42][43][44][45][46][47] Environmental Applications Using Swarm Intelligence in environmental monitoring, pollution control, and sustainable management of natural resources. ...
... Environmental conditions can change rapidly, requiring the monitoring system to adapt [31]: ...
... • Habitat Construction [31]: Swarm robots can construct habitats and other structures on planetary surfaces. By coordinating their actions, the robots can build complex structures using local materials or pre-fabricated components. ...
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Swarm Intelligence (SI) represents a paradigm shift in artificial intelligence, leveraging the collective behavior of decentralized, self-organized systems to solve complex problems. This study provides a comprehensive review of SI, focusing on its application to multi-robot systems. We explore foundational concepts, diverse SI algorithms, and their practical implementations by synthesizing insights from various reputable sources. The review highlights how principles derived from natural swarms, such as those of ants, bees, and birds, can be harnessed to enhance the efficiency, robustness, and scalability of multi-robot systems. We explore key advancements, ongoing challenges, and potential future directions. Through this extensive examination, we aim to provide a foundational understanding and a detailed taxonomy of SI research, paving the way for further innovation and development in theoretical and applied contexts.
... Swarm intelligence encompasses a set of principles and behaviours observed in natural systems, which have inspired the development of computational models and algorithms for problem-solving and optimization tasks [9]. At its core, swarm intelligence is characterized by the collective behaviour of decentralized, self-organized entities interacting locally with one another and their environment [10]. This section aims to provide an overview of the fundamental principles underlying swarm intelligence. ...
... Through iterative feedback loops and local rules, individual agents adjust their behaviours based on the actions of neighbouring agents, leading to coordinated patterns of movement or decision-making at the swarm level [1]. This decentralized coordination allows swarm intelligence systems to exhibit adaptive and robust behaviours, capable of exploring solution spaces efficiently and converging toward optimal or nearoptimal solutions [10]. Additionally, self-organization promotes scalability and flexibility, as swarm dynamics can scale seamlessly from small to large populations of agents without requiring explicit coordination or communication overhead [19]. ...
... Schranz et al. [10] 2021 This paper likely explores the integration of swarm intelligence techniques into cyberphysical systems, discussing concepts, challenges, and potential future trends in leveraging swarm intelligence for enhanced system performance and autonomy. ...
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Swarm intelligence, inspired by the collective behaviour of natural swarms and social insects, represents a powerful paradigm for solving complex optimization and decision-making problems. In this review paper, we provide an overview of swarm intelligence, covering its definition, principles, algorithms, applications, performance evaluation, challenges, and future directions. We discuss prominent swarm intelligence algorithms, such as ant colony optimization, particle swarm optimization, and artificial bee colony algorithm, highlighting their applications in optimization, robotics, data mining, telecommunications, and other domains. Furthermore, we examine the performance evaluation and comparative studies of swarm intelligence algorithms, emphasizing the importance of metrics, comparative analysis, and case studies in assessing algorithmic effectiveness and practical applicability. Challenges facing swarm intelligence research, such as scalability, robustness, and interpretability, are identified, and potential future directions for addressing these challenges and advancing the field are outlined. In conclusion, swarm intelligence offers a versatile and effective approach to solving a wide range of optimization and decision-making problems, with applications spanning diverse domains and industries. By addressing current challenges, exploring new research directions, and embracing interdisciplinary collaborations, swarm intelligence researchers can continue to innovate and develop cutting-edge algorithms with profound implications for science, engineering, and society.
... Agent-based modeling and swarm intelligence are known for providing advantages in simulating complex systems with autonomous entities including adaptability, scalability and robustness. They utilize collective decision-making processes as observed in nature by swarms of insects, fish or birds [1]. Central to our approach is the integration of these autopoietic characteristics that include the emergent intelligence of self-organization, regeneration, and regulation. ...
... This entails the presence of a significant number of other swarm members (for instance, a single instance of an FPGA, existing in isolation, would not make a suitable swarm member). Additionally, the entity should exhibit an appropriate degree of abstraction to facilitate modeling, possess the capability to sense dynamic information from the immediate environment, respond to information originating from the local vicinity (such as making decisions), and be logically coherent and comprehensible, fostering trust in the proposed solution [1]. ...
Conference Paper
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The edge continuum presents a dynamic and evolving paradigm in the future's world of computing, offering a versatile and efficient solution for a wide range of applications and industries. The edge infrastructure is more challenged in its stability and performance because of more stringent latency and autonomy requirements, distribution across multiple sites, their local limited size, multi-tenancy and multi-operators, local management , with components being concurrent and asynchronous. This paper introduces an innovative framework that combines agent-based modeling and swarm intelligence to address complex challenges such as resource allocation, workload scheduling, and data management in the edge continuum. This framework, at the core of the architecture, enhances edge autonomy, reduces latency, improves energy efficiency, and optimizes cloud connectivity by applying agent-based modeling. By integrating autopoietic characteristics like self-organization, regeneration, and regulation, the system dynamically adapts to changing conditions. Two candidate algorithms, the hormone algorithm and ant algorithm, emulate decentralized decision-making processes observed in nature. The paper reviews related work in swarm intelligence for network optimization and emphasizes the need for distributed, agent-based solutions. This research paves the way for robust, adaptive, and scalable systems in the complex edge environment, promising emergent behaviors and enhanced efficiency. In this position paper, we propose the edge continuum with its characteristics and limitations as a novel field of application for swarm intelligence by conceptually proposing agent-based modeling and simulation.
... Some issues and problems require a swarm intelligence algorithm (SI), which is a particular branch of AI, in order to meditate on a specific issue. SI algorithms mimic natural creatures in which a group cooperates as an individual to achieve its goals [1]. Applying a swarm intelligence algorithm is still challenging and problematic for a specific set of concerns, as it consumes a considerable amount of time. ...
... The NMRA implementation of the first phase starts after the initialization of the initial generation, and the following steps are applied to produce the next generation using equation (1), where phase 1 represents the subtraction portion of equation (1), i.e., (wj twk t ) • Step 1: Two solutions produced out of the next generation using equation (1) are randomly selected, i.e., Sj and Sk. ...
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... For such a swarm-intelligent behavior, the individual agents of a swarm work without a central control using local rules and interactions from which a collective behavior emerges. Adaptability, scalability and robustness result from these interactions and produce resilient self-organized systems [2]. Specifically for UAVs, a technical prerequisite is that they need to be equipped with on-board processing units, communication and sensing capabilities. ...
... Electronic copy available at: https://ssrn.com/abstract=4644200 P r e p r i n t n o t p e e r r e v i e w e d rules, and interaction patterns that trigger these collective behaviors [2]. ...
... In [31], a survey on security and privacy issues was presented. The application of swarm optimization algorithms in the detection of cyberattacks in cloud-cyber physical systems was studied in [28,[32][33][34][35][36][37][38]. Table I summarizes key studies. ...
Article
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... El concepto de sistemas ciber-físicos, también conocido como CPS por sus siglas en inglés, fue introducido por primera vez a mediados de la década de 2000, y desde entonces ha evolucionado considerablemente (Kaynak, 2024). Estos sistemas se definen por su capacidad de integrar el mundo físico y el digital mediante el uso de sensores, actuadores y algoritmos avanzados de procesamiento de datos (Schranz et al., 2021). ...
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Introducción: Este estudio explora la integración de los sistemas ciber-físicos (CPS) en la educación del siglo XXI. Metodología: Mediante una revisión sistemática de la literatura en las bases de datos WoS y Scopus, se identificaron y analizaron 34 estudios. Resultados: Se evidencia que los CPS, incluyendo tecnologías como la realidad virtual y la robótica, personalizan el aprendizaje y mejoran la evaluación continua, la motivación y las competencias técnicas de los estudiantes. Sin embargo, la implementación enfrenta desafíos, como la necesidad de adaptar programas educativos y diseñar cursos interdisciplinares prácticos. Discusión: A pesar de estos retos, los CPS presentan oportunidades para enriquecer la pedagogía mediante inteligencia artificial y proporcionar un enfoque transversal. Conclusiones: Es necesario continuar investigando la integración de tecnologías emergentes en los CPS y evaluar su impacto en diferentes contextos educativos.
... Therefore, it becomes necessary to contemplate ways for humans to engage with and influence the behavior and decision-making processes of swarms. The task of crafting effective interactions between humans and swarms has been recognized and explored (Schranz et al., 2021). ...
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This book explores the transformative power of swarm intelligence and digital innovations in shaping the cities of the future. It presents a comprehensive analysis of how social learning, citizen engagement, advanced technology, design, construction, planning and public policies converge to create cities that are sustainable, resilient, and inclusive. The initial chapters stress the importance of collective intelligence in urban development, using technologies like Virtual Reality to increase citizen participation and democratise decision-making. Public policies play a key role in driving the digital transformation needed for greener cities, with frameworks and tools to enhance transparency and accountability. Digital technologies in policymaking ensure that policies are adaptive, data-driven, and responsive to real-time challenges. Internet of Things systems are explored for their role in improving public safety, urban resilience, and energy efficiency through digital twins, blockchain, and sensor networks. Swarm intelligence is highlighted for optimising energy management, reducing consumption, and promoting renewable resources. Healthcare integration into urban planning and sustainability is also discussed, with a comparative analysis of cities showing how tech innovation enhances resilience against climate change. Swarm intelligence beyond cities is also explored, such as for disaster response, healthcare, environmental conservation, and agriculture. Autonomous systems like drones and nanobots are shown to improve efficiency across various sectors. Overall, this book advocates for a holistic approach to urban development, integrating digital technologies and collective intelligence to create cities that are technologically advanced, socially equitable, and environmentally sustainable.
... Together with the successful adoption of embedded systems in our society, new paradigms such as cloud computing, Internet of Things, and edge computing have emerged that aim to modernise [2] the behaviour of digital interactions in our daily habits. In addition, the requirement to enable devices to operate independently has led to the development of new artificial intelligence techniques [3,4] to increase the reasoning capabilities of devices. ...
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Reducing carbon emissions is a critical issue for the near future as climate change is an imminent reality. To reduce our carbon footprint, society must change its habits and behaviours to optimise energy consumption, and the current progress in embedded systems and artificial intelligence has the potential to make this easier. The smart building concept and intelligent energy management are key points to increase the use of renewable sources of energy as opposed to fossil fuels. In addition, cyber-physical systems (CPSs) provide an abstraction of the management of services that allows the integration of both virtual and physical systems in a seamless control architecture. In this paper, we propose to use multiagent reinforcement learning (MARL) to model the CPS services control plane in a smart house, with the purpose of minimising, by shifting or shutdown services, the use of non-renewable energy (fuel generator) by exploiting solar production and batteries. Furthermore, our proposal dynamically adapts its behaviour in real time according to current and historic energy production, thus being able to handle occasional changes in energy production due to meteorological phenomena or unexpected energy consumption. In order to evaluate our proposal, we have developed an open-source smart building energy simulator and deployed our use case. Finally, several simulations with different configurations are evaluated to verify the performance. The simulation results show that the reinforcement learning solution outperformed the priority-based and the heuristic-based solutions in both power consumption and adaptability in all configurations.
... In this field, the integration of cyber-physical intelligence marks a significant advancement in the exploration of DTs. This integration combines statistical modeling to comprehend system behavior using historical data, predictive modeling to anticipate future behavior and outcomes, and adaptive behavior to autonomously adapt and learn from the environment (Schranz et al, 2021). The results of our study highlight the significant emphasis placed on statistical modeling, which was utilized in 23 (≈ 40%) out of 57 surveyed studies. ...
Preprint
These authors contributed equally to this work. Abstract The convergence of digital twin technology and cultural heritage signifies a dynamic research frontier, aiming to harness innovation for monitoring, preserving , disseminating, and enhancing cultural artifacts and sites. This synergy underscores a proactive approach to safeguarding and promoting our diverse cultural legacies through technological advancement. In this context, this systematic literature review explores the evolving landscape of digital twins in cultural heritage, examining their applications, development methodologies, and objectives across 57 selected papers. We address three primary research questions: firstly, examining the diverse applications of digital twin, particularly in enhancing visitor experiences; secondly, analyzing the practices governing the creation, implementation, deployment, and maintenance of digital twin for cultural heritage, alongside commonly utilized platforms and tools; and finally, evaluating the maturity level of digital twin within the cultural heritage context. Employing 20 parameters, we assess the current state of digital twin implementations. The main findings are then organized into three main categories , namely, interactivity and visualization, data management, and scalability and flexibility. This review contributes to the broader understanding of digital twin progress in cultural heritage, providing valuable insights for researchers. By identifying prevalent challenges within the literature, we aim to catalyze future research efforts and drive innovation in this field. 1
... Agent-based modeling has emerged as an effective approach to tackle the problem of task scheduling in distributed logistics [25]. It involves the use of autonomous agents, each representing individual entities like freighters, trucks, and orders, which interact within a defined environment according to specific rules. ...
... In nature, numerous biological populations exhibit remarkable group behavior [1][2][3]. For instance, when a flock of starlings flights, they can swiftly alter their overall direction and trajectory to evade predation by birds of prey [4][5][6]. Similarly, sardine schools effectively fend off predators through coordinated fast swimming and group defense actions in response to hunting threats [5,7,8]. Intriguingly, these complex group behaviors often arise from simple actions of many individuals [9,10]. ...
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The classic Vicsek model, while influential in understanding swarm behavior, has limitations in achieving motion consensus and convergence speed, especially under varying conditions of density and noise. This study aims to introduce a novel receptive field mechanism to the Vicsek model to enhance its performance in terms of motion consensus and convergence speed within swarms. The modified model divides a particle’s surrounding area into excitation and inhibition zones based on distinct functions. This structural modification is designed to enrich evolutionary behavior and improve consensus convergence capabilities. Experimental outcomes indicate that the proposed model achieves faster convergence rates towards motion consensus under various density and noise conditions compared to traditional models. Specifically, while classic Vicsek models fail to converge to an overall polarization state under high noise levels and exhibit quasi-periodic oscillations, the enhanced model demonstrates stable convergence without oscillatory behavior across both low- and high-noise environments. The findings highlight the superior evolutionary consistency characteristics of the improved model, offering new theoretical and practical insights into the stability and controllability of swarms. This advancement presents significant implications for the development of more robust swarm systems.
... Swarm robotics is the field in which a large number of relatively simple robots are coordinated to fulfil a common task in a cooperative manner [286]. Swarm robotic systems usually have unique features including: i) each robot's communication is limited in the sense that only the neighbouring agents communicate with one another; ii) all robots in a swarm follow the same set of rules and work together to achieve a common goal; and iii) the stability of a swarm system is not (or at most slightly) affected by the withdrawal of some of the agents [287]. Swarm robotics is being used to solve a range of challenges in real world, such as autonomous shepherding [288], dynamic mapping [289], and cooperative planetary exploration [290]. ...
Thesis
This thesis aims to develop unmanned aerial system solutions for stockpile volume estimation in both open and confined spaces. It starts with a comprehensive literature review that examines both traditional and recent stockpile volume estimation techniques employed in various environments. It was found that recently emerging aerial methods, such as drone-borne LiDAR sensors, can enable notable advantages including speed, safety, occlusion elimination, and enhanced accuracy compared to current typical industrial solutions. However, there is still a notable gap in research represented in the underdevelopment of cost-effective aerial solutions for safe and precise volume estimation within confined spaces. The research in this thesis starts with a detailed investigation of the state-of-the-art in utilising drones for operations within treacherous conditions, particularly within industrial confined spaces. It was found that existing studies have not thoroughly examined drone missions under operational constraints such as absence of GPS signals, dust-laden air, and poor/lack of visibility. These limitations defined the way for a relatively novel application where drones could be deployed to enhance inspection while augmenting safety measures. Following the establishment of this fresh perspective, mission planning, instrument development, and implementation of control and navigation strategies were assessed across diverse confined spaces and for various stockpile volume estimation missions in both simulated and real-world scenarios. A thorough cost-benefit analysis elucidated that drone-based solutions for stockpile volume estimation within confined spaces achieve a high Cost-Benefit Priority Factor (CPF) of 133-200. Moreover, this approach surpasses traditional industrial fixed sensor systems in flexibility, initial cost savings, and ability to serve multiple sites. Advancing further, a low-cost, yet effective approach was proposed that relies on actuating a single-point light detecting and ranging (1D LiDAR) sensor using a micro servo motor onboard of a drone. The collected LiDAR ranges were converted to a point cloud that allows the reconstruction of 3D stockpiles. The proposed approach was assessed via simulations of a wide range of mission operating conditions. The influences from modulating the drone flight trajectory, servo motion waveform, flight speed, and yawing speed on the mapping performance were all investigated. Comparing the volumetric error values, the average error from the proposed actuated 1D LiDAR system was 0.9% as opposed to 1% and 0.8% from the 2D and 3D LiDAR options, respectively. That said, compared to 2D and 3D LiDARs, the proposed system requires less scanning speed for data acquisition, is much lighter, and allows a substantial reduction in cost. Experimental tests on drone-based solutions for scanning a reference stockpile were conducted with either single or multiple drones equipped with 1D LiDAR sensors, achieving an average volumetric error of 2%. In contrast, the actuated single-point LiDAR system exhibited a higher volumetric error of 5% due to the significant number of outlier points involved. Finally, as the previously presented solutions required an external localisation system for their operation within confined spaces, this thesis paved the way to get rid of such requirement via applying an ICP (Iterative Closest Point) algorithm that can operate independently of such systems. The proposed algorithm uniquely employed a low-rate, low-dense LiDAR scan, specifically focusing on the horizontal layer of a 3D LiDAR for localisation and scan matching. Furthermore, a wall-following navigation strategy was employed for indoor navigation and path-planning to further streamline the mapping process. It was shown that the estimated volume of the reconstructed stockpiles has an average volumetric error of 3.7%, but this figure was enhanced to 0.4% when applying loop closure. Moreover, mapping using an actuated single-point LiDAR approach was processed using the ICP localisation method, resulting in a 1.4% volumetric error.
... This approach enhanced robustness against various disturbances, including measurement noise, environmental fluctuations, and modeling uncertainties. To motivate future applications of biological swarms, Melanie et al. 11 utilized the notion of cyber-physical systems (CPS) to demonstrate the physical embodiment of swarm intelligence. The physical embodiment included autonomous driving and intelligent transportation, emergency response, environmental monitoring, power grids, space missions, medical applications, and physical representations in human networks. ...
Preprint
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Movement of biological swarm exists widely in nature, and cooperative mechanism can improve the adaptability of the swarm to the environment. However, most current research studies free swarm movement and ignore analysis subject to environmental constraints like existence of tubes. In this paper, an experimental environment is set up such that Petitella georgiae swimming through a tube is studied. Based on the observation of position, speed, and direction of each fish, it is found out that each fish is affected by the distribution of fish swarm in its field of view. When a fish swarm swims through the tube, the ratio of speeds in middle region and edge region has a linear positive correlation with the cosine of angles the tube forms, and the average speed is larger within a specific angle range. When fish swarm passes through tubes, the area with a larger speed also corresponds to a larger density.
... For the optimal path search problem, the perturbation technique of path finding is effectively utilized (Owais and Osman 2018;Owais et al. 2016). Since the use of heuristic search methods and swarm intelligence algorithms to solve counterexamples in theory can significantly save time and space costs (Schranz et al. 2021), this will provide good support for solving counterexamples of the CPS model. ...
Article
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Cyber-Physical Systems (CPS) are complex systems that integrate information control devices with physical resources, which can be automatically and formalized verified by model checking according to the expected requirements in the formal specification. The counterexamples in model checking are witnesses to the violation of the specification properties of the system and can provide important diagnostic information for debugging, controlling, and synthesizing CPS. Designing a rational specification language for CPS and generating effective counterexamples allows security vulnerabilities to be detected and addressed early in the system development. However, CPS involve frequent interactions between cyber and physical systems and often operate in unreliable environments, which poses new challenges for comprehensive modeling and designing specification languages for CPSs with discrete, continuous, time, probabilistic, and concurrent behaviors. Moreover, finding the smallest counterexample of CPS with probabilistic behavior in the shortest possible time has been identified as a Non-Deterministic Polynomial-complete (NP-complete) problem. Although a number of heuristics have been devised to address this challenge, the accuracy and efficiency of the solved counterexamples need to be improved due to the difficulty in determining the heuristic functions. We first provide a comprehensive model for CPS by introducing the Hybrid Probabilistic Time Labeled Transition System (HPTLTS). Subsequently, we design a specification language HPTLTS Temporal Logic (HPTLTS-TL) that can describe the properties of CPS. In addition, we propose an optimization algorithm CACO-A, which combines the Ant Colony Optimization (ACO) algorithm and the A-algorithm to efficiently generate the counterexample of CPS, which is represented as the diagnostic subgraph. Finally, we discuss a typical CPS example to demonstrate the feasibility of our approach.
... In the design of a swarming MRS, few questions are as critical as the number of robots to use to achieve the expected results [86]. In fact, by using too few agents, a practitioner can completely miss the benefits of swarms, as "the advantages of swarm intelligence algorithms can only be exploited if a critical mass of swarm members is reached" [87]. In addition to the swarm's ability to coalesce into collective, other important parameters must be considered when selecting the number of agents to compose it. ...
Thesis
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Is it possible for multi-robot systems to efficiently search and track one or more fast- moving targets? While individual robots on their own are simply unable to mount a proper pursuit of a target moving faster than they can, leveraging swarm intelligence is key to achieving this goal. In this thesis, the question of how to optimize a swarm’s design to achieve such a task efficiently is raised and answered through the lens of balancing exploration— i.e., the gathering of information—and exploitation—i.e., the use of said information. Through the combination of simple movement rules, namely repulsion between agents, promoting exploration, and attraction towards a target, which generates exploitation, we generated the decentralized operation of swarms of agents. To com- municate, these agents rely on a k-nearest neighbors network, which can be ad- justed to calibrate the swarm’s exploration-exploitation balance. Other parameters, such as implementing short-term memory and the introduction of fast agents—i.e., heterogeneity—are also used to affect this balance with the ultimate goal of opti- mizing swarm performance. By adjusting the size of the search-space the swarm operates in, we vary its agent density, which is introduced as a novel method to study swarm behaviors and improve the optimization process. The identification of density phases characterized by a swarm’s performance re- veals the mechanisms behind the previously discovered critical minimum and maxi- mum densities. By exploring how to shift the ’transition’ phase, where performance is maximized for the number of agents present, a framework is provided to optimize swarm performance and harness swarm intelligence. Furthermore, using metrics to describe the swarm’s exploration-exploitation balance allows an optimum level of connectivity k to be found for each specific tracking scenario, revealing a path towards the design of swarms that perform over wider ranges of conditions. The replacement of agents by faster ones was also shown to be universally beneficial, al- though extra care must be given as the exploration-exploitation balance is affected. Thus, the development of truly adaptive strategies able to tune this balance accord- ing to local conditions, as was manually done throughout this work, is discussed as the next step toward the development of successful swarms.
... Therefore, one approach is to agentbased modeling to engineer the production plant as a swarm of self-organized agents [Umlauft et al., 2023b, Schranz et al., 2021b, Umlauft et al., 2022. As observed in the natural swarm behavior of fish, ants or birds, the agents use local rules and interactions in their neighborhood to reach a global goal like, e.g., foraging [Schranz et al., 2021a]. Using this approach, the result leads to an optimization of the production plant from the bottom-up instead of calculating a global optimization solution from the top-down. ...
... However, despite the growing interest in human-robot interaction, the complexities of human-swarm interaction (HSI) are still emerging and not yet fully understood. Core challenges dominating the humansswarm teaming and operation include cognitive overload on humans caused by swarm scaling, under/over-intervention by humans, mis-calibration of human-swarm trust, poor predictability of human-swarm team performance, tedious interaction interfaces [6,33,44,49]. These challenges affect human-in/on-the-loop swarm interaction and consequently any potential learning by demonstration for developing Artificial Intelligence (AI) to interact with the swarm. ...
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Human-swarm teaming combines the dynamic capabilities of robot swarms with the strategic cognition of humans to perform complex tasks. As robotic systems and unmanned vehicles gain enhanced autonomy, there is an imperative need for for a human-in-the-loop simulation framework to study both human action/cognition as well as swarm behaviors and their interaction within operational contexts. Such a framework is essential for developing and testing the coordination of human supervisor and robotic swarms with different level of autonomy and exploring the role of machine learning in enhancing their collective behaviors beyond basic functionalities. This paper introduces SHaSTA(Simulator for Human And Swarm Team Applications), a versatile, open-source simulation platform designed to advance research in swarm learning and human-swarm interaction. SHaSTA offers a comprehensive interface that allows both human operators and machine learning algorithms to direct and refine swarm tactics and behaviors. The platform incorporates a range of functionalities, including individual robot controls, swarm behavior primitives like formation control and path planning, and customizable interfaces for defining novel behavioral primitives. A standout feature of SHaSTA is its capability to integrate and synchronize physiological data from human operators with robotic swarm simulations. This integration provides a deeper understanding of human factors in swarm control and enhances the simulation’s realism and applicability. We demonstrate SHaSTA’s effectiveness and scalability through a case study in a search-andrescue scenario, showcasing its computational efficiency and the practical benefits of its human-swarm interaction model. Additionally, we present findings from our human subject study that illustrate how SHaSTA effectively captures and utilizes physiological features to inform and improve human-swarm interaction strategies.
... In which swarms of robotic drones can collaboratively work together to achieve tasks. SI is inspired by insects such as bees and ants that naturally coordinate to accomplish tasks that otherwise would not be possible to accomplish alone [68]. ...
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One of the characteristic features of the next-generation of Industry 4.0 is human-centricity, which in turn includes two technological advancements: Artificial Intelligence and the Industrial Metaverse. In this work, we assess the impact that AI played on the advancement of three technologies that emerged to be cornerstones in the fourth generation of industry: intelligent industrial robotics, unmanned aerial vehicles, and additive manufacturing. Despite the significant improvement that AI and the industrial metaverse can offer, the incorporation of many AI-enabled and Metaverse-based technologies remains under the expectations. Safety continues to be a strong factor that limits the expansion of intelligent industrial robotics and drones, whilst Cybersecurity is effectively a major limiting factor for the advance of the industrial metaverse and the integration of blockchains. However, most research works agree that the lack of the skilled workforce will no-arguably be the decisive factor that limits the incorporation of these technologies in industry. Therefore, long-term planning and training programs are needed to counter the upcoming shortage in the skilled workforce.
... recognition. These algorithms enable vehicles to identify and classify objects in their surroundings, distinguishing between pedestrians, vehicles, and other obstacles with high accuracy. Machine learning models can predict the behavior of other road users, anticipating their movements and intentions (Hansen, Güttel & Swart, 2019, Marwedel, 2021, Schranz, et. al., 2021). This predictive capability enhances the vehicle's ability to navigate complex traffic scenarios safely. ...
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The integration of embedded systems in autonomous vehicles represents a transformative paradigm shift in the automotive industry, offering unprecedented opportunities for enhanced safety, efficiency, and user experience. This comprehensive review explores the current landscape of embedded systems in autonomous vehicles, delving into emerging trends, persistent challenges, and future directions that shape the trajectory of this rapidly evolving field. The review begins by examining the foundational concepts of embedded systems in the context of autonomous vehicles, elucidating the intricate interplay between hardware and software components. It surveys the state-of-the-art technologies that empower these systems, including advanced sensors, actuators, and communication protocols, highlighting their pivotal roles in perception, decision-making, and control aspects of autonomous driving. One of the prominent trends discussed in this review is the increasing reliance on artificial intelligence (AI) and machine learning algorithms within embedded systems. The incorporation of these intelligent algorithms enables vehicles to adapt and learn from real-world scenarios, enhancing their ability to navigate diverse and dynamic environments. Additionally, the review sheds light on the growing emphasis on connectivity and edge computing, illustrating how embedded systems leverage these technologies to facilitate seamless communication between vehicles and their surrounding infrastructure. Despite the promising advancements, the review critically examines the persistent challenges that impede the widespread adoption of embedded systems in autonomous vehicles. Issues such as safety concerns, cybersecurity threats, and regulatory frameworks are analyzed, providing insights into the complex ecosystem in which these technologies operate. In addressing the future directions of embedded systems in autonomous vehicles, the review envisions a trajectory marked by continuous innovation and collaboration across industries. It anticipates the evolution of embedded systems towards more robust, adaptive, and fault-tolerant architectures, paving the way for increased autonomy and widespread deployment of autonomous vehicles. This comprehensive review provides a holistic understanding of embedded systems in autonomous vehicles, encapsulating current trends, challenges, and future directions. As the automotive landscape undergoes a paradigm shift, this review serves as a valuable resource for researchers, practitioners, and policymakers seeking to navigate the dynamic terrain of autonomous vehicle technology.
... Designing of walking robot leg-linkage with foot center tracing along straight-line has certain advantages, considering first of all energy efficiency and simplified control [41][42][43][44][45][46][47][48][49][50]. The prototype of horizontal propulsion mechanism designed for the legged robot is shown in Fig. 1a with foots F1 and F2 on support phase (on ground) and F3 and F4 on transfer phase (foot swing phase) [51][52]. ...
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In the ever-evolving domain of robotic locomotion, leg linkage design emerges not just as an intricate engineering puzzle, but also as a decisive element in realizing optimal horizontal propulsion. The research meticulously interrogates this pivotal concern, endeavoring to harmonize multiple design objectives that traditionally exist in tension. Leveraging the robust computational prowess of the Non-dominated Sorting Genetic Algorithm (NSGA) for multiobjective optimization, this study orchestrates a deliberate foray into the expansive and complex design space. The overarching aim is not merely to pinpoint a singular, universal design zenith, but to painstakingly chart a continuum of Pareto-optimal solutions, thereby accommodating the myriad, often contradictory, imperatives that animate robotic design—from the quest for energy efficiency to the pursuit of agility, speed, and robust structural integrity. This methodology yields a rich tapestry of insights: notable among them is the discernible predilection of specific linkage configurations towards distinct performance outcomes. While certain geometries resonate more profoundly with rapid, fluid motion, others evince a marked inclination towards stability or frugal energy consumption. By dissecting these intricate relationships, and presenting them within a structured framework, this study contributes profoundly to the literature, offering both theoretical depth and pragmatic design templates to the robotics community. This synergistic marriage of computational algorithms with nuanced design challenges holds the promise to significantly recalibrate and enhance contemporary paradigms in leg linkage design for horizontally propelling robots. This study marks a significant advancement in robotic locomotion by employing the Non-dominated Sorting Genetic Algorithm (NSGA) for the first time in the optimization of leg linkage design for walking robots, providing a more nuanced understanding of the balance between structural integrity, energy efficiency, and propulsion agility. Our research elucidates a spectrum of Pareto-optimal solutions, a novel approach that offers a comprehensive understanding of the trade-offs involved in leg linkage design. Specifically, the optimized designs achieved an improvement in propulsion efficiency by reducing the approximation error to less than 0.006, and enhancing force transmission angles to over 25 degrees. These experimental results validate the practical applicability of these designs, demonstrating a balance of improved efficiency and stability, thereby setting a new benchmark for leg linkage design in walking robots. The findings underscore the potential of NSGA in robotic design, offering a robust framework for future advancements in the field.
... By today's CPS we mean systems showing distinguishing characteristics, as expressed in [12]. For instance: (i) the dramatic increase in control variables and automation of tasks is confirmed in [21] by means of an open-source drone application; (ii) the usage of complex and ambitious functions are acknowledged by [22]; (iii) the need of managing large numbers of processes that require communication and coordination is studied in [23] through a smart traffic application; (iv) the adoption of built-in functionalities are investigated in [24] in the context of a smart city scenario for safe crowd monitoring and control; (v) the need for dynamic scheduling is analyzed in [25,26]. ...
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The design of cyber-physical systems (CPS) is challenging due to the heterogeneity of software and hardware components that operate in uncertain environments (e.g., fluctuating workloads), hence they are prone to performance issues. Software performance antipatterns could be a key means to tackle this challenge since they recognize design problems that may lead to unacceptable system performance. This manuscript focuses on modeling and analyzing a variegate set of software performance antipatterns with the goal of quantifying their performance impact on CPS. Starting from the specification of eight software performance antipatterns, we build a baseline queuing network performance model that is properly extended to account for the corresponding bad practices. The approach is applied to a CPS consisting of a network of sensors and experimental results show that performance degradation can be traced back to software performance antipatterns. Sensitivity analysis investigates the peculiar characteristics of antipatterns, such as the frequency of checking the status of resources, that provides quantitative information to software designers to help them identify potential performance problems and their root causes. Quantifying the performance impact of antipatterns on CPS paves the way for future work enabling the automated refactoring of systems to remove these bad practices.
Chapter
This chapter presents an in-depth analysis of the transformative role of artificial intelligence (AI) in the fields of industrial robotics and drone technology, offering a comprehensive overview of the integration and evolution of these technologies. Beginning with a historical perspective, the study traces the development of robotics and drones within industrial contexts, laying the foundation for understanding the significant impact of AI. The analysis reveals how the advent of AI has revolutionized these technologies, shifting from basic mechanization to advanced, intelligent systems capable of complex tasks, and decision-making. The study explores the progression from initial AI applications in robotics to the current state-of-the-art implementations, demonstrating the profound changes in efficiency, capability, and functionality. By examining the interplay between AI, robotics, and drones, the study provides insights into the future trajectory of these technologies and their potential to redefine industrial processes.
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This paper introduces the Surrogate-assisted Multi-objective Grey Wolf Optimizer (SMOGWO) as a novel methodology for addressing the complex problem of empty-heavy train allocation, with a focus on line utilization balance. By integrating surrogate models to approximate the objective functions, SMOGWO significantly improves the efficiency and accuracy of the optimization process. The effectiveness of this approach is evaluated using the CEC2009 multi-objective test function suite, where SMOGWO achieves a superiority rate of 76.67% compared to other leading multi-objective algorithms. Furthermore, the practical applicability of SMOGWO is demonstrated through a case study on empty and heavy train allocation, which validates its ability to balance line capacity, minimize transportation costs, and optimize the technical combination of heavy trains. The research highlights SMOGWO’s potential as a robust solution for optimization challenges in railway transportation, offering valuable contributions toward enhancing operational efficiency and promoting sustainable development in the sector.
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The potential of emerging technology to transform disaster response is examined in this review study. We investigate how, by overcoming the constraints of conventional cloud-based processing in disaster areas, edge computing enables insect robots for real-time data gathering and analysis at the network edge. We examine studies on KubeEdge for reliable network deployment using insect robots and Social Sensing-based Edge Computing (SSEC) for processing social media data. We explore network processing methods that leverage Mobile Cloud Computing (MCC) and show how they overcome issues such as bandwidth limitations and low battery life. This study examines how developments in edge computing, network processing, resource management, and multi-robot systems might benefit disaster response by highlighting the potential of insect robots as indispensable instruments that will ultimately result in quicker and more efficient reaction times.
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Activity based costing (ABC) models for cost management and big data analytics are deeply rooted in academia and practice to efficiently manage operational costs. Inspired by cost management and deep learning (DL) theories we develop a multilayer perceptron (MLP) using a nested particle swarm optimized (PSO) neural network algorithm for self-controlled architecture and weight optimization. This innovative approach enables to mimic ABC while increasing prediction accuracy. In a real-world case study we use wheel manufacturing data provided by a large original equipment manufacturer (OEM) and follow a full factorial experimental design. Furthermore, we benchmark our novel DL approach with results derived from a traditional ABC analysis. We demonstrate that this intelligent cost estimation model can mimic ABC using few cost drivers contributing to efficient and transparent interorganizational cost management. Major quantitative findings within a conducted field experiment demonstrate a high forecast accuracy with low absolute cost percentage error (CPE) deviation of the novel cost estimation approach in industry. The results extend the scope of PSO for the simultaneous optimization of the architecture and weights of neural networks. Additionally, we prove that the PSO algorithm is a suitable alternative to traditional optimization methods in DL.
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The demand for advanced automation solutions has surged in today’s rapidly evolving production and technology landscape. Disruptions, including changing production processes, supply chain challenges, and changing workforce dynamics, emphasize the necessity of adaptable, efficient automation. The push for advanced automation is commonly associated with the concepts known as "intelligent production" or Industry 4.0. Within these concepts, cyber-physical production systems assume a pivotal role, harnessing data-enhanced technologies and machine learning to optimize manufacturing processes, with a particular emphasis on their control. This thesis embarks on the development of a model predictive control framework grounded in grey-box models to enhance process control in industrial production. The primary goal is to improve the prediction accuracy and to adapt to changing conditions, to improve the overall control performance. Real-world implementation and simulated testing are employed to offer practical insights and contribute to the development and advancement of cyber-physical production systems. The research specifically explores the integration of white-box and black-box models within model predictive control, focusing on enabling cyber-physical production systems. The research methodology involves implementing and evaluating the control framework in a real-world model factory. This includes sensor and actuator integration the establishment of communication interfaces and a data management system. Furthermore, the methods relevant to the control framework are evaluated in a simulated chemical process, serving as a benchmark in the chemical industry. Moreover, the study aims to develop adaptive and efficient control systems, reducing the dependence on expert knowledge. The findings underscore the importance of selecting an appropriate grey-box model structure for control systems and accordingly suited hyperparameters. Gaussian process regression models and recurrent neural networks are explored as part of the grey-box model structure. The data-enhanced practical nonlinear model predictive control is introduced, to efficiently include the advantages of grey-box models to enhance control performance, while being computationally efficient. Real-world testing reveals that the data-enhanced practical non-linear model predictive control outperforms linear model predictive control in all use cases, and can achieve better control performance than a model predictive control with additional disturbance observer in most use cases. Furthermore, the study shows that data-enhanced practical nonlinear model predictive control can be integrated into cyber-physical production systems for autonomous operations and self-adaption during runtime. Moreover, the research underscores the potential for black-box optimization methods such as Bayesian optimization with regard to control concepts such as the data-enhanced practical nonlinear model predictive control framework. Future research avenues include exploring correlations between open-loop prediction accuracy and closed-loop control performance.
Chapter
“Spatial-temporal” (ST) data is time series data from multiple locations. Data is unpredictable, making predictions difficult. For a variety of urban tasks, such as estimating the speed of traffic and the demand for taxis, an accurate prediction that is based on this kind of data is required. Because of this assumption, the methods that are currently being used that are based on deep learning can only produce one possible outcome. As a consequence of this, they are unable to comprehend how the future will comprise a wide variety of components and how these components will interact with one another. Also, the current method operates under the presumption that information that is spatial and that which is temporal are essentially distinct, and as a result, each must be investigated separately. The chapter introduces a novel approach that utilises spatial-temporal convolutional neural networks in conjunction with Bi-LSTM and enhanced generative adversarial networks (E-GAN) to effectively capture non-linear correlations present in the data distribution. This is achieved through the use of inverse mapping from the forecast distribution. The spatio-temporal correlation network is a modelling technique that captures the distribution of pixels in both space and time. This approach enables the random sampling of latent variables to generate multiple future scenarios. This is accomplished by modelling the spatial distribution of pixels. This sampling can be carried out for a very wide variety of different possible outcomes (STCN). It is a stochastic adversarial network that learns to perform variational inference on data and generate data together with other people through implicit distribution modelling. Education is the means by which one can accomplish both of these goals. E-GAN also allows the combination of external factors, which further improves model learning, and it does this without any additional work. E-GAN outperforms the baseline models and significantly improves performance, as shown by extensive testing on two datasets derived from the real world.
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This study investigates the incorporation of swarm robotics into the control mechanism of electric vehicles (EVs), introducing an innovative intelligent control framework that utilizes the concepts of decentralized decision-making. The research entails a methodical inquiry that encompasses the design of system architecture, the creation of a model for swarm robotics, the modeling of electric vehicle drive, the integration of swarm robotics with EV control, the development of algorithms for intelligent control, and the execution of real-world tests. The fleet of electric cars, propelled by a collective of independent robotic entities, displayed remarkable flexibility in adjusting to fluctuating surroundings. Findings demonstrated disparities in operating duration, distance traversed, mean speed, and energy expenditure during several iterations, highlighting the system’s adeptness in promptly reacting to instantaneous inputs. Significantly, the swarm-propelled electric cars successfully attained varied operating durations, showcasing the system’s adaptability in accommodating environmental dynamics. The swarm-driven system demonstrated its navigation effectiveness by effectively covering various distances, highlighting its versatility and extensive coverage capabilities. The system’s ability to effectively balance energy economy and performance is shown by the collective regulation of average velocity. The energy consumption study demonstrated the system’s efficacy in optimizing energy use, with certain experiments showing significant savings. Percentage change studies have yielded valuable insights into the comparative enhancements or difficulties seen in each indicator, so illustrating the influence of decentralized decision-making on operational results. This study is a valuable contribution to the ever-changing field of intelligent transportation systems, providing insight into the immense potential of swarm-driven electric cars to completely transform sustainable and adaptable transportation. The results highlight the remarkable flexibility and optimization skills of swarm robotics in the management of electric vehicles, paving the way for future advancements in the quest for intelligent, energyefficient, and dynamically responsive transportation solutions.
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Social insects, such as ants, termites, and honeybees, have evolved sophisticated societies where collaboration and division of labor enhance survival of the whole colony, and are thus considered “superorganisms”. Historically, studying behaviors involving large groups under natural conditions posed significant challenges, often leading to experiments with a limited number of organisms under artificial laboratory conditions that incompletely reflected the animals’ natural habitat. A promising approach to exploring animal behaviors, beyond observation, is using robotics that produce stimuli to interact with the animals. However, their application has predominantly been constrained to small groups in laboratory conditions. Here we present the design choices and development of a biocompatible robotic system intended to integrate with complete honeybee colonies in the field, enabling exploration of their collective thermoregulatory behaviors via arrays of thermal sensors and actuators. We tested the system’s ability to capture the spatiotemporal signatures of two key collective behaviors. A 121-day observation revealed thermoregulation activity of the broodnest area during the foraging season, followed by clustering behavior during winter. Then we demonstrated the system’s ability to influence the colony by guiding a cluster of bees along an unnatural trajectory, via localized thermal stimuli emitted by two robotic frames. These results showcase a system with the capability to experimentally modulate honeybee colonies from within, as well as to unobtrusively observe their dynamics over extended periods. Such biohybrid systems uniting complete societies of thousands of animals and interactive robots can be used to confirm or challenge the existing understanding of complex animal collectives.
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In the industrial revolution 4.0 the applicability of cyber physical systems (CPS) extensively increased in various sectors including logistics, health, and manufacturing. The integration of CPS in logistics has revolutionized the way goods are transported, managed, and monitored. CPS combines digital and physical components to enhance the efficiency and effectiveness of logistics operations. However, to ensure the reliable and continuous operation of CPS, optimizing the availability of its subsystems is paramount. Hence, in present study, an effort is made to explore the applicability of metaheuristic approaches in the performance evaluation of cyber physical systems and a comprehensive evaluation framework is also proposed to compare the performance of metaheuristic algorithms. For this purpose, a Markov model of cyber physical system developed by considering constant failure and repair rates for all components. The failure and repair rates are followed exponential distribution. The concept of cold standby redundancy is utilized for two components including analog components and sensors & actuators unit. All components of cyber physical system can face failure during the working process and are perfectly repairable. The mathematical expression of system is derived and optimized by using metaheuristic approaches namely grey wolf optimization (GWO), cuckoo search algorithm (CS), dragonfly optimization (DA), grasshopper optimization algorithm (GOA) and cat swarm optimization (CSO). The numerical expression of the system availability obtained at various population sizes and estimated parameters derived. It is revealed from the numerical investigation that GWO and CSO outperform all optimization techniques. GWO attains its maximum availability 0.9983290 at population size 140 over 500 iterations and CSO attains its maximum availability 0.9983297 at population size 120 over 1000 iterations. The results of present study provide valuable insights into the strengths and weaknesses of each metaheuristic approach in the context of CPS subsystem optimization. This comparative analysis can guide logistics professionals and researchers in selecting the most suitable optimization algorithm for their specific CPS applications.
Conference Paper
To reach a low-emission future it is necessary to change our behaviour and habits, and advances in embedded systems and artificial intelligence can help us. The smart building concept and energy management are key points to increase the use of renewable sources as opposed to fossil fuels. In addition, Cyber-Physical Systems (CPS) provide an abstraction of the management of services that allows the integration of both virtual and physical systems. In this paper, we propose to use Multi-Agent Reinforcement Learning (MARL) to model the CPS services control plane in a smart house, with the aim of minimising, by shifting or shutdown services, the use of non-renewable energy (fuel generator) by exploiting solar production and batteries. Moreover, our proposal is able to dynamically adapt its behaviour in real time according to the current and historical energy production, thus being able to address occasional changes in energy production due to meteorological phenomena or unexpected energy consumption. In order to evaluate our proposal, we have developed an open-source smart building energy simulator and deployed our use case. Finally several simulations are evaluated to verify the performance, showing that the reinforcement learning solution outperformed the heuristic-based solution in both power consumption and adaptability.
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Relying only on behaviors that emerge from simple responsive controllers; swarms of robots have been shown capable of autonomously aggregate themselves or objects into clusters without any form of communication. We push these controllers to the limit, requiring robots to sort themselves or objects into different clusters. Based on a responsive controller that maps the current reading of a line-of-sight sensor to a pair of speeds for the robots’ differential wheels, we demonstrate how multiple tasks instances can be accomplished by a robotic swarm. Using the dividing rectangles approach and physics simulation, a training step optimizes the parameters of the controller guided by a fitness function. We conducted a series of systematic trials in physics-based simulation and evaluate the performance in terms of dispersion and the ratio of clustered robots/objects. Across 20 trials where 30 robots cluster themselves into 3 groups, an average of 99.83% of them were correctly clustered into their group after 300 s. Across 50 trials where 15 robots cluster 30 objects into 3 groups, an average of 61.20%, 82.87%, and 97.73% of objects were correctly clustered into their group after 600 s, 900 s, and 1800 s, respectively. The object cluster behavior scales well while the aggregation does not, the latter due to the requirement of control tuning based on the number of robots.
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A novel control method for the artificial swarm system is proposed in this paper. To reflect the biological swarm performance (aggregation), a kinematic model of the artificial swarm system is established by introducing a special potential function. Then, the dynamic model is further obtained to satisfy more accurate dynamic swarm performances — collision avoidance and compact formation. The former can be realized by diffeomorphism transformation approach and a safety zone is guaranteed accordingly. As for the compact formation, we creatively convert it into a constraint-following problem between leader and followers. Based on the dynamic model, a novel control consisting of two parts is designed, including the model-based control and the adaptive robust control. On one hand, the trajectory tracking problem of the artificial swarm system is solved. On the other hand, the uncertainties in the artificial swarm system can be compensated perfectly. The deterministic performance, uniform boundedness and uniform ultimate boundedness, is guaranteed. The effectiveness of the collaborative control is verified by the proof based on Lyapunov approach and simulations of the artificial swarm system.
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In swarm robotics multiple robots collectively solve problems by forming advantageous structures and behaviors similar to the ones observed in natural systems, such as swarms of bees, birds, or fish. However, the step to industrial applications has not yet been made successfully. Literature is light on real-world swarm applications that apply actual swarm algorithms. Typically, only parts of swarm algorithms are used which we refer to as basic swarm behaviors. In this paper we collect and categorize these behaviors into spatial organization, navigation, decision making, and miscellaneous. This taxonomy is then applied to categorize a number of existing swarm robotic applications from research and industrial domains. Along with the classification, we give a comprehensive overview of research platforms that can be used for testing and evaluating swarm behavior, systems that are already on the market, and projects that target a specific market. Results from this survey show that swarm robotic applications are still rare today. Many industrial projects still rely on centralized control, and even though a solution with multiple robots is employed, the principal idea of swarm robotics of distributed decision making is neglected. We identified mainly following reasons: First of all, swarm behavior emerging from local interactions is hard to predict and a proof of its eligibility for applications in an industrial context is difficult to provide. Second, current communication architectures often do not match requirements for swarm communication, which often leads to a system with a centralized communication infrastructure. Finally, testing swarms for real industrial applications is an issue, since deployment in a productive environment is typically too risky and simulations of a target system may not be sufficiently accurate. In contrast, the research platforms present a means for transforming swarm robotics solutions from theory to prototype industrial systems.
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Automatic cruise control of a platoon of multiple connected vehicles in an automated highway system has drawn significant attention of the control practitioners over the past two decades due to its ability to reduce traffic congestion problems, improve traffic throughput and enhance safety of highway traffic. This paper proposes a two-layer distributed control scheme to maintain the string stability of a heterogeneous and connected vehicle platoon moving in one dimension with constant spacing policy assuming constant velocity of the lead vehicle. A feedback linearization tool is applied first to transform the nonlinear vehicle dynamics into a linear heterogeneous state-space model and then a distributed adaptive control protocol has been designed to keep equal inter-vehicular spacing between any consecutive vehicles while maintaining a desired longitudinal velocity of the entire platoon. The proposed scheme utilizes only the neighbouring state information (i.e. relative distance, velocity and acceleration) and the leader is not required to communicate with each and every one of the following vehicles directly since the interaction topology of the vehicle platoon is designed to have a spanning tree rooted at the leader. Simulation results demonstrated the effectiveness of the proposed platoon control scheme. Moreover, the practical feasibility of the scheme was validated by hardware experiments with real robots.
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Today, 992 million people still do not have access to electricity globally. Most live in rural areas of the developing world. In 2018, the electrification rate for sub-Saharan Africa was only 27%. Furthermore, off-grid systems are projected to provide 65% of the newly electrified population in sub-Saharan Africa. Current estimations show that the average connection cost per technology in rural areas of sub-Saharan Africa is 2000–3000 USD for grid extension, 500–1200 USD for a microgrid solution, and 150–500 USD for a solar home system. The most recent studies for real-world microgrids installed in sub-Saharan Africa show that the average split of capital expenditure (CAPEX) spending on distribution versus generation in microgrids is at 50%/50%. This is the result of the significant cost reduction of photovoltaics, batteries, and power electronics, in comparison with the practically stable unchanged cost of poles and cables. Even if the business model is chosen by the investor—usually a pay-as-you-go implementation—there is still the difficult decision to make on whether to go for a microgrid or solar home systems. Taking inspiration from multispecies swarms, a Multispecies Swarm Electrification approach is developed that is able to meet the real-world needs of the developing world in terms of rural electrification.
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Logistic industry is experiencing its golden era for development due to its supportive role of electronic commerce operation. Big data retrieved from electronic business information system is becoming one of core competitive enterprise resources. Data analytics is playing a pivotal role to enhance effectiveness and efficiency of operation management. Generally, a well-designed delivery routing plan can reduce logistics cost and improve customer satisfaction for online business to a large extent. According to this, literatures on improvement of delivery efficiency are reviewed in this research. In existing literatures, for instance, ant colony algorithm, genetic algorithm and other combined algorithm are quite popular for such a kind of problem. Even though some algorithms are quite advanced, they are still difficult for implementation due to different constraints and larger-scale of raw electronic commerce data obtained from information system. In this paper, an advanced ant colony algorithm, as a heuristic algorithm, is implemented to optimize planning for an asymmetric capacitated vehicle routing problem. This paper not only emphasizes on ACO algorithm improvement and avoiding premature convergence, but also implementation in a real-world e-commerce delivery, which has more practical meaning for big data analytics and operation management.
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Plant growth is a self-organized process incorporating distributed sensing, internal communication and morphology dynamics. We develop a distributed mechatronic system that autonomously interacts with natural climbing plants, steering their behaviours to grow user-defined shapes and patterns. Investigating this bio-hybrid system paves the way towards the development of living adaptive structures and grown building components. In this new application domain, challenges include sensing, actuation and the combination of engineering methods and natural plants in the experimental setup. By triggering behavioural responses in the plants through light spectra stimuli, we use static mechatronic nodes to grow climbing plants in a user-defined pattern at a two-dimensional plane. The experiments show successful growth over periods up to eight weeks. Results of the stimuli-guided experiments are substantially different from the control experiments. Key limitations are the number of repetitions performed and the scale of the systems tested. Recommended future research would investigate the use of similar bio-hybrids to connect construction elements and grow shapes of larger size.
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Today most of the electrification grids in sub-Saharan Africa (SSA) are found in urban areas. However, these grids experience erratic and frequent power outages for long hours, on average 4.6 h in a day. Due to this problem, many of the African population rely on cheaper but unclean options like backup diesel/petrol generators for lighting, phone charging and other electrical appliances. In Nigeria, millions of people own power generators. These generators are not only noisy but the fuel they use is also costly and result into emissions that pollute the environment. In order to optimize fuel consumption and gradually reduce use of backup generators while increasing share of renewables, a strategy is proposed in this paper to interconnect the existing backup infrastructure to form a bottom-up swarm electrification grid with step by step integration of alternative storages and renewable energy sources. In the swarm-grid excess energy can be generated, sold among grid participants and even at later stage to the national grid. This study focused on a swarm grid hybrid node consisting of a solar PV system integrated with the existing individual backup generators for households and retail shop end users. The hybrid system designed was found to be a suitable system with fuel savings of 39%, excess energy of 27% and reduced cost of backup electricity by 34% for the household end user. For the retail shop end user, the hybrid system was found to be a suitable system with a fuel cost saving of 53%, excess energy generation of 28% and reduced cost of backup electricity by 45%. The study showed that integration of a solar PV system has a high potential to reduce fuel costs for backup generator end users and presents a great opportunity for hybrid swarm electrification approach.
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Fake News has been around for decades and with the advent of social media and modern day journalism at its peak, detection of media-rich fake news has been a popular topic in the research community. Given the challenges associated with detecting fake news research problem, researchers around the globe are trying to understand the basic characteristics of the problem statement. This paper aims to present an insight on characterization of news story in the modern diaspora combined with the differential content types of news story and its impact on readers. Subsequently, we dive into existing fake news detection approaches that are heavily based on text-based analysis, and also describe popular fake news data-sets. We conclude the paper by identifying 4 key open research challenges that can guide future research.
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This paper presents the research done in the field of robotic cultural evolution in challenging real world environments. We hereby present these efforts, as part of project subCULTron, where we will create an artificial society of three cooperating sub-cultures of robotic agents operating in a challenging real-world habitat. We introduce the novel concept of “cultural learning”, which will allow a swarm of agents to locally adapt to a complex environment and exchange the information about this adaptation with other subgroups of agents. Main task of the presented robotic system is autonomous environmental monitoring including self organised task allocation and organisation of swarm movement processes. One main focus of the project is on the development and implementation of bio-inspired controllers, as well as novel bio-inspired sensor systems, communication principles, energy harvesting and morphological designs. The main scientific objective is to enable and study the emergence of a collective long-term autonomous cognitive system in which information survives the operational lifetime of individuals, allowing cross-generation learning of the society by self-optimising.
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Voice assistants are software agents that can interpret human speech and respond via synthesized voices. Apple’s Siri, Amazon’s Alexa, Microsoft’s Cortana, and Google’s Assistant are the most popular voice assistants and are embedded in smartphones or dedicated home speakers. Users can ask their assistants questions, control home automation devices and media playback via voice, and manage other basic tasks such as email, to-do lists, and calendars with verbal commands. This column will explore the basic workings and common features of today’s voice assistants. It will also discuss some of the privacy and security issues inherent to voice assistants and some potential future uses for these devices. As voice assistants become more widely used, librarians will want to be familiar with their operation and perhaps consider them as a means to deliver library services and materials.
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Particle swarm optimization (PSO) is a population-based, stochastic search algorithm inspired by the flocking behaviour of birds. The PSO algorithm has been shown to be rather sensitive to its control parameters, and thus, performance may be greatly improved by employing appropriately tuned parameters. However, parameter tuning is typically a time-intensive empirical process. Furthermore, a priori parameter tuning makes the implicit assumption that the optimal parameters of the PSO algorithm are not time-dependent. To address these issues, self-adaptive particle swarm optimization (SAPSO) algorithms adapt their control parameters throughout execution. While there is a wide variety of such SAPSO algorithms in the literature, their behaviours are not well understood. Specifically, it is unknown whether these SAPSO algorithms will even exhibit convergent behaviour. This paper addresses this lack of understanding by investigating the convergence behaviours of 18 SAPSO algorithms both analytically and empirically. This paper also empirically examines whether the adapted parameters reach a stable point and whether the final parameter values adhere to a well-known convergence criterion. The results depict a grim state for SAPSO algorithms; over half of the SAPSO algorithms exhibit divergent behaviour while many others prematurely converge. © 2017 Springer Science+Business Media, LLC, part of Springer Nature
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