Robot Swarms

The Sentience-Field Hypothesis (SFH) proposes that consciousness is a fundamental, non-local field that generates discrete experiential configurations-qualic partitions-through a coherence-fertility optimisation principle. Qualic partitions refer to quantized "screens" of subjective experience, akin to the redness of a sunset or the qualia of pain, projected by the sentient field as interfaces for self-exploration. Here we (i) formalise the underlying mathematics using Hardy-Ramanujan partition asymptotics and smooth-convex optimisation, providing intuitive analogies for interdisciplinary readers; (ii) present three falsifiable experimental protocols (Bose-Einstein-condensate coherence anomalies, neural fractal-dimension shifts during insight, and cosmological fine-tuning outliers), each with preregistered power analyses; and (iii) demonstrate interdisciplinary applications in ecology, cosmology, and computation, grounded in empirical data. All theoretical claims are accompanied by open-source Python implementations (GitHub repository https://github.com/urmt/SFH_Python_CODE/tree/main/SFH_JCS_ Article/Sentient-Field_Tools). The manuscript satisfies contemporary peer-review standards for methodological transparency, statistical power, and reproducibility, bridging mathematics with neuroscience and philosophy.

Using a number of small flying drones does not change the fundamentals of swarming tactics. In essence, with the large-scale fielding of small weapon carrying drones, the two altering dynamics are: (1) Humans are physically distanced from the weapons they are using; (2) Small drone use changes Swarming Tactics Geometry, as various attacks are able to fully exploit the 3D space encompassing an opponent. That being said, the phenomenon of a number of tactical entities (in this case small drones) able to buzz an opponent, attacking from multiple vectors complicating the opponent’s ability to counter these varied incoming threats remains a core fundamental of swarming tactics.

(Spanish)

Desde que tuvo lugar la aparición de la aviación militar, el combate aéreo ha sufrido una constante evolución en términos tácticos y tecnológicos. Hoy en día, tanto la tecnología de localización y seguimiento de objetivos como la destinada a evitar la detección y el derribo ante un lanzamiento son fundamentales. Esta obra analiza de forma razonada y escalonada la tecnología armamentística de combate aéreo empleada en el Ejército del Aire, ahondando tanto en los sistemas de autoprotección y seguimiento de objetivos, como en la tecnología y capacidad de nuestro armamento.

Modern warfare is rapidly evolving, propelled by technological advancements such as drones, hypersonic missiles, cyber weapons, autonomous systems and now silent Submarines. These innovations have increased the speed and complexity of conflict, heightening risks of inadvertent escalation and civilian harm (Scharre, 2018), (Scharre, 2025)

Drones, often called unmanned aerial systems (UAS), are changing the way the military works, as well as how we handle surveillance, logistics, and civil applications. The rise of autonomous capabilities, AI integration, and swarming technologies brings both strategic benefits and new risks. NATO needs to accept new drone technology and come up with a single plan to stop their use by enemies, defend infrastructure, and keep air dominance. This paper talks about what will happen in the future, the biggest threats, and what NATO and its member states should do about them.

This paper explores the transformative role of artificial intelligence (AI) in modern military operations and the urgent need for a legally binding international treaty to govern its use. While AI enhances decision-making, logistics, cybersecurity, and training, its deployment in autonomous weapons systems raises profound ethical, legal, and humanitarian concerns. The paper highlights challenges related to accountability, transparency, bias, and the erosion of human oversight. It examines current governance efforts—such as those by the UN, OECD, and EU—but argues that fragmented and non-binding approaches are insufficient to prevent misuse, escalation, and arms races. A comprehensive global treaty is proposed to establish universal standards, ensure compliance with international humanitarian law, and safeguard human rights. Such a treaty would promote responsible innovation, prevent destabilizing military applications, and foster ethical, transparent, and accountable AI governance. Ultimately, the paper calls for coordinated international action to ensure AI remains a tool for peace and security, not conflict and instability.

En marzo de 2016, la Fuerza Aérea de Estados Unidos (United States Air Force, USAF) publicó el concepto inicial para la nube de combate o Combat Cloud (CC), definido como «... una red de malla global para la distribución de datos y el intercambio de información dentro de un espa­cio de batalla, donde cada usuario, plataforma o nodo autorizado contri­buye y recibe de manera transparente información esencial y puede utili­zarla en toda la gama de operaciones militares». La CC persigue alcanzar la superioridad en la toma de deci­siones y el mando y control multido­minio (Multi-Domain Command and Control, MDC2), para lo que requiere una serie de características como redundancia, capacidad para auto­formarse, resolución de problemas de forma autónoma, continuar operando cuando una parte ha sido anulada, así como capacidad de recopila­ción de información e integración en un sistema de información abierto y adaptable, con el fin de mejorar el mando y control (Command and Control, C2) y la agilidad operacional en toda la gama de operaciones mili­tares (Range of Military Operations, ROMO) (Kiser, 2017).

Development of advanced hybrid algorithms, based on a MA of evolutionary optimization and search of states, added to a machine learning algorithm SVM/ANN, defining an artificial intelligence machine, in combination with cooperative game theory,
calculating the Shapley and Banzhaf values for sub-swarms, as well as the theory of noncooperative game for the entire swarm, determining new optimized solutions with respect to the Nash equilibrium, supported by innovative concepts such as statistical modal analysis, collision avoidance technology and drone swarm entropy variation, able to make the best operational decision within an Air Combat Cloud.

Resumen Una tecnología de las más innovadoras son los enjambres de RPAs; sin embargo, aún requiere de avanzados desarrollos en diversas áreas científicas, con el fin de anticipar y prever el comportamiento de un grupo de RPAs, estableciendo un enlace de comunicaciones seguro entre ellos, identificando y evitando obstáculos en tiempo real, y reconfigurando las trayectorias de estos agentes de forma automática con el fin de acometer misiones operativas de altas prestaciones. Aunque actualmente la tecnología de los RPAs se encuentra suficientemente consolidada, aún es necesario resolver el problema de dotar al enjambre de un comportamiento colaborativo y de coordinación, donde estos agentes combinen sus capacidades, de tal modo que una conducta cooperativa, organizada, sinérgica e inteligente brote de la adición del comportamiento de un conjunto de RPAs individuales. No obstante, adquirir esta capacidad exige del desarrollo de algoritmos complejos de inteligencia artificial que permitan implementar una red cognitiva entre estas plataformas remotas, capaz de tomar la mejor decisión en cada momento. Así, mediante la aplicación de una serie de algoritmos estructurados en programas computacionales que comprenden desde un EPSO de optimización evolutiva y búsqueda de estados, en combinación con una SVM de aprendizaje automático, se proyecta, diseña y desarrolla un artefacto de inteligencia artificial que explota el concepto de colaboración, coordinación y cooperación, con alto carácter de reciprocidad y apoyo mutuo, entre RPAs de un enjambre, capaz de tomar la mejor decisión entre varias posibilidades, en misiones operativas SEAD, basándose en un conjunto de nuevos conceptos desarrollados en este trabajo, entre los que cabe destacar el de entropía del enjambre y tecnologías avanzadas de elusión de colisiones, de tal suerte de razonamiento que partiendo de un estado inicial el enjambre va progresando hacia estados superiores, con entropía incrementada, que le permita cumplir la misión SEAD, y donde cada RPA evoluciona por sí mismo, todo ello evitando colisionar.

Applying a set of algorithms related to an EPSO of evolutionary optimization and search of states in combination with a machine learning algorithm SVM, it is defined an artificial intelligence tool which makes use of the concept of coordination, cooperation and collaboration, with a high character of reciprocity and mutual aid, among RPAs of a swarm, able to make the best decision among several possibilities, in operational missions of air defense saturation and ISR, based on new concepts developed specifically for this work as the entropy of a swarm and advanced technologies of collision avoidance.

La capacidad de un grupo de RPA de actuar coordinadamente como un enjambre requiere que cada aero-nave remota sea capaz de volar en las proximidades de otros RPA y no colisionar con ellos. Esto implica que cada RPA/agente conozca su posición en el espacio en todo momento, mediante las denominadas técnicas de localización. Si cada agente envía una señal discreta de control/coordinación al resto del enjambre, el número de se-ñales transmitidas es N*(N-1), que en el caso de un elevado número-N de nano-RPA, supone un ingente trabajo de procesado/CPU para cada vehículo remoto, fuertemente limitado por su pequeño tamaño. Para aliviar este trabajo computacional, en este artículo se plantea un método de análisis de las imágenes captadas por la cámara de cada RPA del enjambre. Generalmente las tecnologías de reconocimiento de patrones se basan en la comparación de imágenes, o partes de la misma, con patrones predefinidos, pixel a pixel, con un coste computacional considerable. Sin embargo, una alternativa viable sería utilizar en lugar de cada imagen, una función estadística representativa de dicha imagen digital, como el histogra-ma. Mediante técnicas de análisis no-paramétricas se define un estadístico de contraste, que aplicado a una secuencia de histogramas de las imágenes captadas por la cámara de un RPA, permite establecer un pa-rámetro de medida, de tal modo, que mientras este parámetro se encuentre dentro de margen, el agente no necesitará comunicarse con el enjambre, reduciendo estas transmisiones a aquellas situaciones donde este parámetro se salga de rango. Palabras clave Remotely Piloted Aircraft System (RPAS); Ground Control Station (GCS); imagen digital; histograma; Kolmogorov-Smirnov.

This article investigates the growing use of robots and automation in military operations, emphasizing the ethical challenges posed to international humanitarian law. The Iraq War marked a key shift, transforming robots from tools viewed skeptically to vital military assets. By 2006, robots had executed over 30,000 missions, and demand for unmanned aerial vehicles (UAVs) surged. These technologies span military branches, including the navy's use of unmanned submarines. The focus is on Lethal Autonomous Weapon Systems (LAWS), which can independently make combat decisions. Nations like the U.S., China, and Russia are advancing LAWS, raising ethical concerns about autonomous warfare. The study aims to clarify issues surrounding LAWS, examine international arms control discourse, and propose regulatory strategies. Key areas of discussion include defining LAWS, reviewing debates under the Convention on Certain Conventional Weapons (CCW), addressing regulatory challenges, and suggesting regulation methods for dual-use technology weapons. The article stresses the need for preemptive arms control to limit LAWS development and anticipates future ethical and military landscapes shaped by these technologies. It calls for aligning future LAWS regulations with existing frameworks to manage their impact effectively.

I would like to thank my advisers, Professor Susana Vieira and Professor Sancho Oliveira, for all the guidance and supervision given. A special thanks to Professor Anders Christensen that also advise and ensure the quality of this dissertation, being always available and with willingness to help. I am very grateful to all for providing me such great learning experience. I also would like to acknowledge IT-Instituto de Telecomunicações and FCT-Foundation of Science and Technology, which partly supported this study. To my family, in particular, my mother and father, for all the love and support, and for how much they care about my education. To my grandmother , which taught me the value of hard work ("when something has to be done, it deserves to be well done"). To my friends, for their encouragement and understanding the various skipped night-outs. And to my love, César Costa. There are no words to thank him enough, for how much he helped during the process, for enduring with me so many weekends at home, for being always there when needed.

The ongoing conflict between Russia and Ukraine has seen the emergence of new technologies and tactics, including the weaponization of artificial intelligence (AI). Russia has been accused of using AI-powered systems to assist in the deployment of drones, disrupt communications, and conduct cyberattacks. These AI-enabled tactics have allowed Russia to gain a tactical advantage in the war, as they are able to carry out complex and coordinated attacks with greater speed and precision. However, the use of AI in warfare raises ethical concerns and highlights the need for international regulations and guidelines on the development and use of AI in military applications. The conflict in Ukraine serves as a case study for the potential risks and benefits of AI in warfare and underscores the importance of responsible AI development and deployment.

Swarm robotics is one of the most fascinating and new research areas of recent decades, and one of the grand challenges of robotics is the design of swarm robots that are self-sufficient. This can be crucial for robots exposed to environments that are unstructured or not easily accessible for a human operator, such as the inside of a blood vessel, a collapsed building, the deep sea, or the surface of another planet. In this paper, we present a comprehensive study on hardware architecture and several other important aspects of modular swarm robots, such as self-reconfigurability, self-replication, and self-assembly. The key factors in designing and building a group of swarm robots are cost and miniaturization with robustness, flexibility, and scalability. In robotics intelligence, self-assembly and self-reconfigurability are among the most important characteristics as they can add additional capabilities and functionality to swarm robots. Simulation and model design for swarm robotics is highly complex and expensive, especially when attempting to model the behavior of large swarm robot groups.

The development of flexible swarm robotics systems capable of adapting to the task and environmental changes is a serious challenge. The main motivations of Swarm robotics are decentralized control, stability, adaptivity, and flexibility. Usually, ad-hoc approaches are employed to design a controller capable of meeting the problem specifications. However, these methods cannot be easily verified, and in some cases, it is not even shown that they meet the specifications. Moreover, the controller source code has to be developed separately, primarily when formal methods are employed; As a result, it cannot be guaranteed that the implementation matches the design. This paper proposes a new method-probabilistic timed supervisory control (ptSCT)-to formally design a controller from systems specifications. The proposed ptSCT has several advantages: 1) the automatic generation of the controller source code utilizable in ARGoS platform, 2) formal designing capability using the implemented software tool, 3) set of powerful design components like probabilistic decisions and time constraints, and 4) the reusability of formally designed modules among different scenarios and multiple robotic platforms. Two case studies are considered to investigate various aspects of the proposed system. Firstly, the synchronization case study is implemented for a comparison between SCT and ptSCT in terms of design capabilities and memory consumption. Secondly, the foraging case study as a complex and medium-sized problem is modeled using ptSCT step by step. More than 2400 experiments with a varying number of obstacles, targets, and robots are executed in ARGoS platform in order to show the performance of the automatically generated source code.

In this study, we implement a controller, based on Supervisory Control Theory on a pneumatic assembly system. We use local modular approach, which exploits modular structure of the plant and the specifications. An implementation methodology using a hierarchical control structure where PLCs are utilized is introduced. Local modular approach, together with the implementation methodology presented in this study provides an effective way for synthesizing and realizing supervisors for DES control problems. The resulting PLC program is also modular in structure, making it easy to modify and detect errors.

The number of real-world scenarios where the use of an unmanned aerial vehicle (UAV) swarm is beneficial has greatly increased in recent years. From precision agriculture to forest fire monitoring, post-disaster search and rescue applications, to military use, the applications are widespread. While it is a perceived requirement that all UAV swarms be inherently resilient, in reality, it is often not so. The incorporation of resilient mechanisms depends on an application usage scenario. This study examines a comprehensive range of application scenarios for UAV swarms to bring forward the multitude of components that work together to provide a measure of resilience to the overall swarm. A three-category scheme is used to classify swarm applications. While systemic resilience is an interconnected concept, most real-world applications of UAV swarm research focus on making certain components resilient to disturbances. A broad categorization of UAV swarm applications, categorized by recog...

The behaviour of social insects such as bees and ants has influenced the development of swarm robots. To enable robots to cooperate together, swarm robotics employs principles such as communication, coordination, and collaboration. Collaboration among multiple robots can lead to a faster task completion time compared to the utilisation of a single, complex robot. One of the key aspects of swarm robotics is that control is distributed uniformly across the robots in the swarm, which boosts the system’s resilience and fault-tolerance. Through the use of the robots’ embodied sensors and actuators, this distributed control often facilitates the emergence of collective behaviours through the interaction of the robots with one another and with the environment. The purpose of this survey is to examine the reasons behind the lack of utilisation of swarm robots in multi-tasking applications, which will be accomplished by studying previous research works in the field. We examine the literature...

Events in recent years, particularly since 2014, have tested, if not undermined, the strategic assumptions underpinning NATO's posture. To Europe's east, Russia occupied Crimea and launched a separatist war in eastern Ukraine, is modernising its conventional and nonconventional forces, and launched an invasion of Ukraine on 24 February, a "special military operation" to protect the civilian population and to "demilitarize" and "de-Nazify" Ukraine. The United States and its allies and partners have taken steps to accelerate the delivery and maintain an effective assistance package in order to support Ukraine to deny Russia's offensive that is more limited in terms of territory, but not in terms of brutality and, finally, the achievement of conditions for Ukraine's offensive actions. Ukraine has repeatedly requested more advanced weapons, including LAWS, aircrafts and air defense systems to increase the range, distance, and lethality of its military actions. The LAWS proved to be among the most effective weapons in the fight, for both sides. Russia also increased its military activities in Europe's north, particularly by adding nuclear-capable missiles to Kaliningrad (a Russian territory on the Baltic Sea that is not contiguous with Russia itself) and Belarus, enhancing its air patrolling activities close to other states' airspace, and increasing its naval presence in the Baltic Sea, the Arctic Ocean, and the North Sea. Taken together, these moves have heightened NATO concerns about Russian aggression and its implications for NATO territories, particularly Central and Eastern European NATO allies. To Europe's south, instability resulting in part from the "Arab Spring" led to collapse of states, civil war in some instances, and significant refugee flows into Europe. This has led to political tensions across the broader European Union, as well as to concerns about terrorists inserting within refugee flows. The renewal of super power status competition with Russia, which was underscored by seizure and annexation of Crimea in March 2014 and subsequent actions in Ukraine, has led to a renewed focus in U.S. and NATO defense planning on strengthening military capabilities for countering potential Russian aggression in Europe.

In this study, we implement a controller, based on Supervisory Control Theory on a pneumatic assembly system. We use local modular approach, which exploits modular structure of the plant and the specifications. An implementation methodology using a hierarchical control structure where PLCs are utilized is introduced. Local modular approach, together with the implementation methodology presented in this study provides an effective way for synthesizing and realizing supervisors for DES control problems. The resulting PLC program is also modular in structure, making it easy to modify and detect errors.

The number of real-world scenarios where the use of an unmanned aerial vehicle (UAV) swarm is beneficial has greatly increased in recent years. From precision agriculture to forest fire monitoring, post-disaster search and rescue applications, to military use, the applications are widespread. While it is a perceived requirement that all UAV swarms be inherently resilient, in reality, it is often not so. The incorporation of resilient mechanisms depends on an application usage scenario. This study examines a comprehensive range of application scenarios for UAV swarms to bring forward the multitude of components that work together to provide a measure of resilience to the overall swarm. A three-category scheme is used to classify swarm applications. While systemic resilience is an interconnected concept, most real-world applications of UAV swarm research focus on making certain components resilient to disturbances. A broad categorization of UAV swarm applications, categorized by recognized components and modules, is presented, and prevalent approaches for novel resilience mechanisms in each category are discussed.

If even senior defense officials with responsibility for autonomous weapons programs fail to understand the core issues, then we cannot expect the general public and their elected representatives to make appropriate decisions. 1 Slaughterbots, a video that went viral on YouTube shortly after its release in November 2017, may be one of the most influential drone imaginaries to date. 2 Within a few days it had received more than two million views, even though it was not a Hollywood science fiction trailer but a science communication by arms control advocates. In Black Mirror style, 3 the video pictures the dangerous potential of the deployment of autonomous swarms of self-flying mini-drones equipped with artificial intelligence (AI) capabilities, cameras, sensors, face recognition and explosives. The video was released by the Future of Life Institute 4 together with AI expert Stuart Russell, a professor of computer science at the University of California, Berkeley. Russell explains at the end of the video that the capabilities of autonomous weapons shown in the film are a very near-future possibility, the 'results of integrating and militarizing technologies that we already have' 5and that this development needs to be stopped: 'Allowing machines to choose to kill humans will be devastating

The self-organizing bio-hybrid collaboration of robots and natural plants allows for a variety of interesting applications. As an example we investigate how robots can be used to control the growth and motion of a natural plant, using LEDs to provide stimuli. We follow an evolutionary robotics approach where task performance is determined by monitoring the plant's reaction. First, we do initial plant experiments with simple, predetermined controllers. Then we use image sampling data as a model of the dynamics of the plant tip xy position. Second, we use this approach to evolve robot controllers in simulation. The task is to make the plant approach three predetermined, distinct points in an xy-plane. Finally, we test the evolved controllers in real plant experiments and find that we cross the reality gap successfully. We shortly describe how we have extended from plant tip to many points on the plant, for a model of the plant stem dynamics. Future work will extend to two-axes image sampling for a 3-d approach.

This paper presents an implementation environment applied to reconfigurable processes in automated manufacturing systems. This environment is based on a methodology which consists of a cyclic three-stage development-modeling, synthesis and implementation-until the real system accomplishes the required specification, resulting in the project of the automated system. This kind of development allows a continuous result revision of each stage. The present paper describes the methodology and details of components of the proposed environment.

I would like to thank my advisers, Professor Susana Vieira and Professor Sancho Oliveira, for all the guidance and supervision given. A special thanks to Professor Anders Christensen that also advise and ensure the quality of this dissertation, being always available and with willingness to help. I am very grateful to all for providing me such great learning experience. I also would like to acknowledge IT-Instituto de Telecomunicações and FCT-Foundation of Science and Technology, which partly supported this study. To my family, in particular, my mother and father, for all the love and support, and for how much they care about my education. To my grandmother , which taught me the value of hard work ("when something has to be done, it deserves to be well done"). To my friends, for their encouragement and understanding the various skipped night-outs. And to my love, César Costa. There are no words to thank him enough, for how much he helped during the process, for enduring with me so many weekends at home, for being always there when needed.

Automated environmental monitoring in marine environments is currently carried out either by smallscale robotic systems, composed of one or few robots, or static sensor networks. In this paper, we propose the use of swarm robotics systems to carry out marine environmental monitoring missions. In swarm robotics systems, each individual unit is relatively simple and inexpensive. The robots rely on decentralized control and local communication, allowing the swarm to scale to hundreds of units and to cover large areas. We study the application of a swarm of aquatic robots to environmental monitoring tasks. In the first part of the study, we synthesize swarm control for a temperature monitoring mission and validate our results with a real swarm robotics system. Then, we conduct a simulation-based evaluation of the robots' performance over large areas and with large swarm sizes, and demonstrate the swarm's robustness to faults. Our results show that swarm robotics systems are suited for environmental monitoring tasks by efficiently covering a target area, allowing for redundancy in the data collection process, and tolerating individual robot faults.

Events in recent years, particularly since 2014, have tested, if not undermined, the strategic assumptions underpinning NATO's posture. To Europe's east, Russia occupied Crimea and launched a separatist war in eastern Ukraine, is modernising its conventional and nonconventional forces, and launched an invasion of Ukraine on 24 February, a "special military operation" to protect the civilian population and to "demilitarize" and "de-Nazify" Ukraine. The United States and its allies and partners have taken steps to accelerate the delivery and maintain an effective assistance package in order to support Ukraine to deny Russia's offensive that is more limited in terms of territory, but not in terms of brutality and, finally, the achievement of conditions for Ukraine's offensive actions. Ukraine has repeatedly requested more advanced weapons, including LAWS, aircrafts and air defense systems to increase the range, distance, and lethality of its military actions. The LAWS proved to be among the most effective weapons in the fight, for both sides. Russia also increased its military activities in Europe's north, particularly by adding nuclear-capable missiles to Kaliningrad (a Russian territory on the Baltic Sea that is not contiguous with Russia itself) and Belarus, enhancing its air patrolling activities close to other states' airspace, and increasing its naval presence in the Baltic Sea, the Arctic Ocean, and the North Sea. Taken together, these moves have heightened NATO concerns about Russian aggression and its implications for NATO territories, particularly Central and Eastern European NATO allies. To Europe's south, instability resulting in part from the "Arab Spring" led to collapse of states, civil war in some instances, and significant refugee flows into Europe. This has led to political tensions across the broader European Union, as well as to concerns about terrorists inserting within refugee flows. The renewal of super power status competition with Russia, which was underscored by seizure and annexation of Crimea in March 2014 and subsequent actions in Ukraine, has led to a renewed focus in U.S. and NATO defense planning on strengthening military capabilities for countering potential Russian aggression in Europe.

Nowadays, many kinds of robots are used in industries to help in manufacturing or placing objects. However, teaching young people and children about robot design and work can be difficult, turning this into a complicated area for them. This chapter provides a detailed description of the design and implementation of a robotic arm mounted on a mobile robot using the LEGO Mindstorms NXT kit® and the starter kit DaNI 2.0, designed by National Instruments®. The mobile palletizer robot takes a box from place A and navigates in the indoor environment until it reaches a predefined place B. The characterization of the robotic arm is based on a parallel structure considering that the end-effector has only two points to hold the object; the gripper is also built using LEGO®. The robot performs the path computed using an A-star algorithm; moreover, actions like moving up and down, opening and closing the gripper and picking up the box and putting it down are executed by the robotic arm using the central unit of the NXT kit. Each stage of the robot design and implementation is explained in detail using diagrams and 3D graphical views with the aim of illustrating the implementation step by step for educational purposes (mainly for young people or children).

Events in recent years, particularly since 2014, have tested, if not undermined, the strategic assumptions underpinning NATO's posture. To Europe's east, Russia occupied Crimea and launched a separatist war in eastern Ukraine, is modernising its conventional and nonconventional forces, and launched an invasion of Ukraine on 24 February, a "special military operation" to protect the civilian population and to "demilitarize" and "de-Nazify" Ukraine. The United States and its allies and partners have taken steps to accelerate the delivery and maintain an effective assistance package in order to support Ukraine to deny Russia's offensive that is more limited in terms of territory, but not in terms of brutality and, finally, the achievement of conditions for Ukraine's offensive actions. Ukraine has repeatedly requested more advanced weapons, including LAWS, aircrafts and air defense systems to increase the range, distance, and lethality of its military actions. The LAWS proved to be among the most effective weapons in the fight, for both sides. Russia also increased its military activities in Europe's north, particularly by adding nuclear-capable missiles to Kaliningrad (a Russian territory on the Baltic Sea that is not contiguous with Russia itself) and Belarus, enhancing its air patrolling activities close to other states' airspace, and increasing its naval presence in the Baltic Sea, the Arctic Ocean, and the North Sea. Taken together, these moves have heightened NATO concerns about Russian aggression and its implications for NATO territories, particularly Central and Eastern European NATO allies. To Europe's south, instability resulting in part from the "Arab Spring" led to collapse of states, civil war in some instances, and significant refugee flows into Europe. This has led to political tensions across the broader European Union, as well as to concerns about terrorists inserting within refugee flows. The renewal of super power status competition with Russia, which was underscored by seizure and annexation of Crimea in March 2014 and subsequent actions in Ukraine, has led to a renewed focus in U.S. and NATO defense planning on strengthening military capabilities for countering potential Russian aggression in Europe.

The decreased size and cost of Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs) has enabled the use of swarms of unmanned autonomous vehicles to accomplish a variety of tasks. By utilizing swarming behaviors, it is possible to efficiently accomplish coordinated tasks while minimizing per-drone computational requirements. Some drones rely on decentralized protocols that exhibit emergent behavior across the swarm. While fully decentralized algorithms remove obvious attack vectors their susceptibility to external influence is less understood. This work investigates the influences that can compromise the functionality of an autonomous swarm leading to hazardous situations and cascading vulnerabilities. When a swarm is tasked with missions involving the safety or health of humans, external influences could have serious consequences. The adversarial swarm in this work utilizes an attack vector embedded within the decentralized movement algorithm of a previously defined autonomous swarm designed to create a perimeter sentry swarm. Various simulations confirm the adversarial swarm's ability to capture significant portions (6-23%) of the perimeter.

Being aware of the fact that the architecture and construction industry is responsible for almost half (48%) of the world's energy consumption (Lechner, 2014), sustainable design and manufacturing strategies will become a serious requirement in contemporary built environment research and practice. Taking advantage of the recent advancements in computational design technologies and evolutionary algorithms for optimization and simulation can put an end to the trial-and-error system of design and construction and help us in our journey to decarbonize the built environment. Additive and subtractive methods of digital fabrication also will facilitate achieving this goal by improving the design-manufacture communications. This research is a review and comparison between a hybrid (additive + subtractive) fabricated clay brick and a regular one, in terms of the amount of used material, material distribution, and as their result, energy consumption as well as GHG emission in the stage of construction of a building's life cycle. In this process, an evolutionary algorithm called ant colony optimization (ACO) will be used as the experimental methodology for multi-agent fabricated clay brick. The result is a theoretical and practical method of optimization and fabrication of multi-agent fabricated clay brick to be used in a bottom-up approach to manufacturing the environment. Clay is one of the oldest, most used, and most accessible materials, especially in developing countries. Finding a more environmentally friendly and sustainable way to use it innovatively in the built environment is what this paper is researching by benefiting from advanced science and technology.

This paper presents the results of a comprehensive investigation of the current state of swarm robotics research, organizing and classifying that research into a preliminary taxonomy. We aim to provide an analysis of existing swarm systems in an attempt to define the starting point of potential algorithms leading to the development of a new swarm system platform and software design. In other words, we provide a detailed summary of systems that have been classified under four main categories of the general multi-robot system platforms, namely: selfreconfigurable, modular, self-replicating, and swarm systems. We present a preliminary taxonomy for swarm robotics and classify existing studies into this taxonomy. In later sections of this survey, we do not only address the fact that there is a shortage of available software packages and interfaces that are integrated with capabilities to distribute decentralized algorithms over the swarm system, but also we introduce the challenges of ha...

This study addresses an integral sliding mode control method for discrete time systems. The underlying continuous system is affected by both matched and unmatched uncertainties. The past value of the disturbance signal is taken as the estimate of its present value. The sliding mode controller is designed to ensure the existence of sliding mode in the presence of uncertainties. The proportional part is designed based on the analysis of closedloop stability conditions. An illustrative example is given at the end to prove the efficiency of proposed methods.

In this paper, we present a consensus-based decentralized multi-robot approach to reconstruct a discrete distribution of features, modeled as an occupancy grid map, that represent information contained in a bounded planar environment, such as visual cues used for navigation or semantic labels associated with object detection. The robots explore the environment according to a random walk modeled by a discrete-time discrete-state (DTDS) Markov chain and estimate the feature distribution from their own measurements and the estimates communicated by neighboring robots, using a distributed Chernoff fusion protocol. We prove that under this decentralized fusion protocol, each robot's feature distribution converges to the actual distribution in an almost sure sense. We verify this result in numerical simulations that show that the Hellinger distance between the estimated and actual feature distributions converges to zero over time for each robot. We also validate our strategy through Software-In-The-Loop (SITL) simulations of quadrotors that search a bounded square grid for a set of visual features distributed on a discretized circle.

Very few studies have been carried out to test multi-robot task allocation swarm algorithms in real time systems, where each task must be executed before a deadline. This paper presents a comparative study of several swarmlike algorithms and auction based methods for this kind of scenarios. Moreover, a new paradigm called pseudo-probabilistic swarm-like, is proposed, which merges characteristics of deterministic and probabilistic classical swarm approaches. Despite that this new paradigm can not be classified as swarming, it is closely related with swarm methods. Pseudo-probabilistic swarm-like algorithms can reduce the interference between robots and are particularly suitable for real time environments. This work presents two pseudo-probabilistic swarm-like algorithms: distance pseudo-probabilistic and robot pseudoprobabilistic. The experimental results show that This work has been partially supported by project DPI2008-06548-C03-02 and FEDER funding.

It seems that the future of warfare is in the hands of technology: drones and machines in general are likely to be among the major players in tomorrow’s battlefield. However, those who believe that human beings will be completely deprived of the art of war are mistaken. In the future, humans will not be mere players from behind the scenes. The human factor will be decisive, just as it was in the days of Alexander III, Julius Caesar, and Napoleon Bonaparte. Obviously a “new military figure” is needed, one who is capable of combining different ideas in an efficient and successful way. This article seeks to outline this figure on the basis of two fundamental concepts. Consideration will be given to swarming, a tactic used since antiquity and now used primarily for drones, and human-machine teaming, a concept that is gaining momentum in the fields of military science and technology. The intent here is to understand if these ideas can generate a new player capable of interpreting—and win...

Very few studies have been carried out to test multi-robot task allocation swarm algorithms in real time systems, where each task must be executed before a deadline. This paper presents a comparative study of several swarmlike algorithms and auction based methods for this kind of scenarios. Moreover, a new paradigm called pseudo-probabilistic swarm-like, is proposed, which merges characteristics of deterministic and probabilistic classical swarm approaches. Despite that this new paradigm can not be classified as swarming, it is closely related with swarm methods. Pseudo-probabilistic swarm-like algorithms can reduce the interference between robots and are particularly suitable for real time environments. This work presents two pseudo-probabilistic swarm-like algorithms: distance pseudo-probabilistic and robot pseudoprobabilistic. The experimental results show that This work has been partially supported by project DPI2008-06548-C03-02 and FEDER funding.

Real life implementations of distributed swarm robotics are rare. The standardization of a general purpose swarm robotics platform could greatly accelerate swarm robotics towards real life implementations. The SwarmUS platform is an open-source hardware and software on-board embedded system designed to be added onto existing robots while providing them with swarm features, thus proposing a new take on the platform standardization problem. These features include a distributed relative localization system based on Ultra-Wideband, a local communication system based on Wi-Fi and a distributed coordination system based on the Buzz programming language between robots connected within a SwarmUS platform. Additionally, a human-swarm interaction mobile application and an emulation of the platform in the Robot Operating System (ROS) is presented. Finally, an implementation of the system was realized and tested on two types of robots : a TurtleBot3 Burger and two Pioneer 2DX.

Swarm robotics is a promising approach for the coordination of large numbers of robots. While previous studies have shown that evolutionary robotics techniques can be applied to obtain robust and efficient self-organized behaviors for robot swarms, most studies have been conducted in simulation, and the few that have been conducted on real robots have been confined to laboratory environments. In this paper, we demonstrate for the first time a swarm robotics system with evolved control successfully operating in a real and uncontrolled environment. We evolve neural network-based controllers in simulation for canonical swarm robotics tasks, namely homing, dispersion, clustering, and monitoring. We then assess the performance of the controllers on a real swarm of up to ten aquatic surface robots. Our results show that the evolved controllers transfer successfully to real robots and achieve a performance similar to the performance obtained in simulation. We validate that the evolved cont...

The availability of relatively capable and inexpensive hardware components has made it feasible to consider large-scale systems of autonomous aquatic drones for maritime tasks. In this paper, we present the HANCAD and CORATAM projects, which focus on the fundamental challenges related to communication and control in swarms of aquatic drones. We argue for (i) the adoption of a heterogeneous approach to communication in which a small subset of the drones have long-range communication capabilities while the majority carry only short-range communication hardware, and (ii) the use of decentralized control based on self-organization to facilitate inherent robustness and scalability. A heterogeneous communication system and decentralized control allow for the average drone to be kept relative simple and therefore inexpensive. To assess the proposed methodology, we are currently building 25 prototype drones from off-theshelf components. We present the current hardware designs and discuss th...

Artificial Intelligence (AI) has brought about revolutionary changes in the history of mankind as did Combustion Engine or Electricity centuries ago. It is more an enabler than a mere invention. It has enabled technology to take giant leap towards incorporating innovation in every sphere; be it commercial or military purpose. Throughout the history, technology has played vital role in shaping the global politics and eventually the balance of power. World leaders, military commanders, corporate trailblazers unanimously agree that AI is going to be the next determinant in global power. The leading nations are therefore putting in unprecedented effort to remain in the forefront of the race. In this era of information warfare and hybrid warfare, AI is more pronounced than ever. Sixth generation drones and autonomous weapons are already being used in the recent conflicts. While nations are busy in the competition, they cannot ignore the ethical concern involved with its maximum military use. How much autonomy should be ethical in the use of AI in military technology remains as a matter of thoughtful consideration. While there are many dimensions of discussing AI, this article is going to give an insight how AI is reshaping balance of power and what should be the global approach to embrace this technological wonder in the wake of ethical concern.

This article presents the implementation and use of a two-wheel autonomous robot and its effectiveness as a tool for studying the recently discovered use of grid cells as part of mammalian’s brains space-mapping circuitry (specifically the medial entorhinal cortex). A proposed discrete-time algorithm that emulates the medial entorhinal cortex is programed into the robot. The robot freely explores a limited laboratory area in the manner of a rat or mouse and reports information to a PC, thus enabling research without the use of live individuals. Position coordinate neural maps are achieved as mathematically predicted although for a reduced number of implemented neurons (i.e., 200 neurons). However, this type of computational embedded system (robot’s microcontroller) is found to be insufficient for simulating huge numbers of neurons in real time (as in the medial entorhinal cortex). It is considered that the results of this work provide an insight into achieving an enhanced embedded s...

The availability of relatively capable and inexpensive hardware components has made it feasible to consider large-scale systems of autonomous aquatic drones for maritime tasks. In this paper, we present the HANCAD and CORATAM projects, which focus on the fundamental challenges related to communication and control in swarms of aquatic drones. We argue for (i) the adoption of a heterogeneous approach to communication in which a small subset of the drones have long-range communication capabilities while the majority carry only short-range communication hardware, and (ii) the use of decentralized control based on self-organization to facilitate inherent robustness and scalability. A heterogeneous communication system and decentralized control allow for the average drone to be kept relative simple and therefore inexpensive. To assess the proposed methodology, we are currently building 25 prototype drones from off-theshelf components. We present the current hardware designs and discuss th...

We consider a scenario in which a group of quadrotors is tasked at tracking multiple stationary targets in an unknown, bounded environment. The quadrotors search for targets along a spatial grid overlaid on the environment while performing a random walk on this grid modeled by a discrete-time discrete-state (DTDS) Markov chain. The quadrotors can transmit their estimates of the target locations to other quadrotors that occupy their current location on the grid; thus, their communication network is time-varying and not necessarily connected. We model the search procedure as a renewal-reward process on the underlying DTDS Markov chain. To accommodate changes in the set of targets observed by each quadrotor as it explores the environment, along with uncertainties in the quadrotors' measurements of the targets, we formulate the tracking problem in terms of Random Finite Sets (RFS). The quadrotors use RFS-based Probability Hypothesis Density (PHD) filters to estimate the number of targets and their locations. We present a theoretical estimation framework, based on the Gaussian Mixture formulation of the PHD filter, and preliminary simulation results toward extending existing approaches for RFS-based multi-target tracking to a decentralized multi-robot strategy for multi-target tracking. We validate this approach with simulations of multi-target tracking scenarios with different densities of robots and targets, and we evaluate the average time required for the robots in each scenario to reach agreement on a common set of targets.

We consider a scenario in which a group of quadrotors is tasked at tracking multiple stationary targets in an unknown, bounded environment. The quadrotors search for targets along a spatial grid overlaid on the environment while performing a random walk on this grid modeled by a discrete-time discrete-state (DTDS) Markov chain. The quadrotors can transmit their estimates of the target locations to other quadrotors that occupy their current location on the grid; thus, their communication network is time-varying and not necessarily connected. We model the search procedure as a renewal-reward process on the underlying DTDS Markov chain. To accommodate changes in the set of targets observed by each quadrotor as it explores the environment, along with uncertainties in the quadrotors' measurements of the targets, we formulate the tracking problem in terms of Random Finite Sets (RFS). The quadrotors use RFS-based Probability Hypothesis Density (PHD) filters to estimate the number of targets and their locations. We present a theoretical estimation framework, based on the Gaussian Mixture formulation of the PHD filter, and preliminary simulation results toward extending existing approaches for RFS-based multi-target tracking to a decentralized multi-robot strategy for multi-target tracking. We validate this approach with simulations of multi-target tracking scenarios with different densities of robots and targets, and we evaluate the average time required for the robots in each scenario to reach agreement on a common set of targets.