Swarm Robotics

We can efficiently collect crops or minerals by operating multi-robot foraging. As foraging spaces become wider, control algorithms demand scalability and reliability. Swarm robotics is a state-of-the-art algorithm on wide foraging spaces due to its advantages, such as self-organization, robustness, and flexibility. However, high initial and operating costs are main barriers in performing multi-robot foraging system. In this paper, we propose a novel method to improve the energy efficiency of the system to reduce operating costs. The idea is to employ a new behavior model regarding role division in concert with the search space division.

The automation system has been tested in a variety of different situations for monitoring, searching and data mining, with good sensing capabilities, and with positive financial/ commercial results [iii], [i]. For the first time, under one roof, U. T. SEC will bring together manufacturers of unmanned aerial systems and other unmanned vehicles as well as systems for surveillance, communication, data transfer, drone defense and position sensing. The controller controls the position and guides the robot to perform the required rolling, pitching and yaw while maintaining a constant nominal thrust in the body frame [iv]. Secondly, hover regulators for maintaining, achieving the desired 3D-angular position [v-vi]. Thirdly, 3D tracking of the trajectory controlling the center of mass following the specified 3D path, preserving the specified and probably changing the angle of deviation, setting of angular acceleration distribution moving along the plane [vii], [iv], [viii]. To achieve high-...

Swarm intelligence is the emergent collective intelligence of groups of simple autonomous agents which are autonomous subsystems that interact with their environment. This paper presents a performance evaluation system for swarm robots. The model proposed includes a set of performance assessment criteria and performance assessment and monitoring system. The proposed approach is developed for swarm robots developed for health system responsible for delivery, guidance, monitoring, recognition, and delivery which a project in European 6 th Framework Research Program and carried out by several European nations.

Many real-world applications have been suggested in the swarm robotics literature. However, there is a general lack of understanding of what needs to be done for robot swarms to be useful and trusted by users in reality. This paper aims to investigate user perception of robot swarms in the workplace, and inform design principles for the deployment of future swarms in real-world applications. Three qualitative studies with a total of 37 participants were done across three sectors: fire and rescue, storage organization, and bridge inspection. Each study examined the users' perceptions using focus groups and interviews. In this paper, we describe our findings regarding: the current processes and tools used in these professions and their main challenges; attitudes toward robot swarms assisting them; and the requirements that would encourage them to use robot swarms. We found that there was a generally positive reaction to robot swarms for information gathering and automation of simple processes. Furthermore, a human in the loop is preferred when it comes to decision making. Recommendations to increase trust and acceptance are related to transparency, accountability, safety, reliability, ease of maintenance, and ease of use. Finally, we found that mutual shaping, a methodology to create a bidirectional relationship between users and technology developers to incorporate societal choices in all stages of research and development, is a valid approach to increase knowledge and acceptance of swarm robotics. This paper contributes to the creation of such a culture of mutual shaping between researchers and users, toward increasing the chances of a successful deployment of robot swarms in the physical realm.

This paper presents a novel robot swarming navigation algorithm in order to find the odor sources in an unknown environment, based on the ability of each swarm member to sense the odor. Each robot in the swarm has a cooperative localization system which uses wireless network as a mean of measuring the distance from the other robots. In this method, at least three robots act as stationary measurement beacons while the other robots of the swarm navigate in the environment towards the odor source. In the next step, the roles of the robots will be switched and some other robots will act as beacons. The experimental tests report a good result in finding the odor source and also the accuracy of localization system 1 .

The paper describes the Swarm Modelling in the context of the CPSwarm project, The project proposes a new science of system integration and tools to support engineering of CPS swarms.CPSwarm tools will ease development and integration of complex herds of heterogeneous CPS that collaborate based on local policies and that exhibit a collective behavior capable of solving complex, industrial-driven, real-world problems.

Mixed reality framework for collective motion patterns of swarms with delay coupling 1 KLEMENTYNA SZWAYKOWSKA, IRA SCHWARTZ, Naval Research Lab -The formation of coherent patterns in swarms of interacting self-propelled autonomous agents is an important subject for many applications within the field of distributed robotic systems. However, there are significant logistical challenges associated with testing fully distributed systems in real-world settings. In this paper, we provide a rigorous theoretical justification for the use of mixed-reality experiments as a stepping stone to fully physical testing of distributed robotic systems. We also model and experimentally realize a mixed-reality largescale swarm of delay-coupled agents. Our analyses, assuming agents communicating over an Erdos-Renyi network, demonstrate the existence of stable coherent patterns that can be achieved only with delay coupling and that are robust to decreasing network connectivity and heterogeneity in agent dynamics. We show how the bifurcation structure for emergence of different patterns changes with heterogeneity in agent acceleration capabilities and limited connectivity in the network as a function of coupling strength and delay. Our results are verified through simulation as well as preliminary experimental results of delay-induced pattern formation in a mixed-reality swarm.

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A mobile robot that determines the shape and size of an unknown workspace is presented in this paper. The robot is wheeled, and equipped with a controller and cliff sensors. The main goal of this paper is to demonstrate the potential use of neural networks for workspace mapping. A workspace can be a table or any other surface with cliff boundaries, assuming that the robot can reach all of its boundaries. The detection of the workspace cliff boundaries is accomplished using infrared sensors that are pointed downwards. The body of the robot is designed and manufactured using a 3D printer. Robot uses differential drive to move, and odometry for position sensing. The motion control algorithm is explained in detail. Controller is based around Raspberry Pi 3. The description of the neural network that determines the shape and size of the workspace is presented, along with test results. The robot is tested for determining the size and shape of a rectangular workspace with the dimensions of...

El envejecimiento progresivo de la población es un tema que está llamando la atención de muchos países. Los ambientes asistidos han surgido como una filosofía que proporciona un apoyo considerable a la vida cotidiana de las personas con necesidades específicas (edad avanzada, así como las personas con discapacidad), ayudándolas a mantener una vida independiente y digna. Al ser sistemas críticos por llevar el cuidado de la salud, es conveniente usar metodologías o herramientas que puedan mejorar la fiabilidad de los sistemas. Las ontologías, como herramientas de representación del conocimiento, aportan numerosas ventajas, tales como dar una semántica formal de los datos intercambiados, proporcionando un mecanismo para la representación y el intercambio de dicha información estructurada. Este artículo expone una aplicación de ontologías diseñadas para apoyar el desarrollo de entornos asistidos.

Para contrarrestar el ataque de un enjambre de drones, la solución planteada en este trabajo es repeler la agresión mediante masas de enjambres contra-enjambre de drones. Esta solución es escalable y actúa organizándose en subenjambres. Inicialmente cada subenjambre se modela por medio de un juego cooperativo donde los drones del subenjambre se consideran jugadores en un juego, que buscan en su evolución el máximo beneficio. El valor de Shapley o/y Banzhaf estima un reparto de beneficios como la ganancia de una magnitud del subenjambre, la entropía, donde cada dron contribuye a su maximización con su aporte individual. Este aumento de entropía incrementa la supervivencia del enjambre en zona de operaciones. Asimismo, para optimizar este aumento de entropía se ha hecho uso de redes neuronales artificiales para encontrar entre diferentes parámetros aquellos valores de entropía más alta.
Para el enjambre completo, se ha diseñado un juego no cooperativo donde la matriz de pagos se calcula en base a dos parámetros: la entropía de cada subenjambre y la distancia de cada subenjambre a la zona de operaciones. Alcanzado el equilibrio de Nash en este juego, donde el pago es máximo, se han definido unos valores próximos al equilibrio de Nash VCNE donde, aunque estos valores estén Pareto dominados por el equilibrio de Nash, la renuncia a una parte del pago del equilibrio de Nash al seleccionar los valores VCNE, proporciona a cambio un incremento del tiempo que el enjambre es capaz de actuar bajo fuego enemigo, incrementando su supervivencia.

Para que un enjambre de RPA pueda actuar coordinadamente, combinando su número para realizar misiones, los investigadores se encuentran con dos grandes dificultades. En primer lugar, cada aeronave remota debe volar en las inmediaciones de otros RPA sin colisionar con ellos. La segunda es mantener la topología del enjambre durante la misión para potenciar la acción cooperativa entre agentes/RPA. Respecto al problema de las colisiones, lo más habitual es considerar al enjambre como un grupo descentralizado donde cada agente maniobra autónomamente para evitar colisionar con otros RPA cercanos. Mediante sucesivas simulaciones se puede determinar matemáticamente una zona de seguridad para cada RPA de tal modo que mientras se guarden estas distancias las colisiones prácticamente se eliminan. No obstante, esta zona de seguridad no es suficiente para mantener la cohesión del enjambre, siendo necesario estimar un parámetro de proximidad al centroide del enjambre, impidiendo de esta forma que los agentes se dispersen durante la misión y pierdan su eficacia sinérgica de apoyo mutuo entre RPA. Asimismo, en el desempeño de ciertas misiones operativas se hace necesario disponer de una función que mida el grado de disgregación del enjambre, para lo cual se ha definido una función de entropía del enjambre, evaluando el número de estados posibles en cuando a número de vehículos remotos con velocidades diferentes tanto en módulo como en velocidad. Otra técnica complementaria a tener en cuenta en la elusión de colisiones en enjambres es la navegación proporcional. Así, mediante el empleo de la velocidad de rotación del vector velocidad del agente, como proporcional a la velocidad de rotación de la recta de la aeronave remota con respecto al RPA cuya colisión se pretende evitar, se puede definir un algoritmo de evitación de colisiones manteniendo la topología del enjambre.

This paper outlines experiences in bringing up a course with significant sensing and actuating capabilities, on top of the already existing infrastructure support for training embedded wireless system design. The role of the suitable development platform, design kits and the lab experiments is outlined, and the expected outcomes are highlighted. We emphasize the role of scaffolding principle, which now does not only apply to the single course, but also to our overall experience in developing such courses.

This paper describes the architecture of KVORUM, the agent-based modeling environment developed by the authors to explore certain design decisions and facilitate simulation usage in the field of group robotics, specifically for research of robotic systems with elements of a social structure. KVORUM is a prototype of a simulation system that should adequately abstract away complexities of a physical system while providing convenient interfaces and library modules for modeling groups of mobile land robots and individual agent's intrinsic structures. It was built from the ground up as a modular, highly extensible system focused on speed of calculations that is to be achieved by using proper simplification of physical and other effects, and by the ability to perform simulations using parallel computing. In KVORUM the developed abstractions and limitations of a parallel architecture are tested without fully adhering to parallel computing requirements. It is shown that the proposed method of simulating such systems is applicable to a wide variety of problems from the field of group robotics with elements of a social structure (and swarm robotics in general). Some features of the architecture and models used in KVORUM allow for it to be extended in next iterations to fully support simulations on parallel computing systems.

Emergence is a concept that is not easy to grasp, since it contradicts our idea of central control and planning. In this work, we use a swarm of robots as a tangible tool to visualize interactions as the underlying principle of emergence. We utilize phosphorescent sheet (i.e. glow-in-the-dark foil) that can be activated with UV LEDs to visualize local information transfer between the robots in form of fading luminescent trails. The robots are specially designed to be both easy-to-understand and easy-to-build. They are a lowcost kit that can allow non-professionals to explore collective behaviour. By playing with the robots, they can get an understanding of complex systems such as emergence or Ant Colony Optimization algorithms in an automatic and playful way.

This paper reports a new process which can be used to repurpose a robot at the end of its primary life. Repurposing a robot enables it to continue delivering value beyond the point at which it might have been otherwise recycled or scrapped. The repurposing process was created using the future thinking forecasting methodology known as a Delphi Study. This paper shows there are some similarities between a process for repurposing, to processes for developing new systems and remanufacturing systems. However this new process contains unique elements, highlighting the value and potential of challenging accepted norms for the management of robot systems when they reach the end of their useful life and could otherwise be considered e-waste.

Human literature has traditionally been steeped in anthropocentrism, emphasizing human experience, agency, and language as central to meaning-making. This paradigm, deeply rooted in the Enlightenment and modernist traditions, views literature as a vessel for the expression of human subjectivity. Philosophers such as Immanuel Kant and Jean-Paul Sartre have championed this perspective, framing humans as the primary agents of creative and interpretive processes. However, this focus on human agency becomes problematic when applied to posthuman literature. Posthumanism seeks to decenter the human, emphasizing the entanglement of humans with nonhuman entities, networks, and technologies. Rosi Braidotti describes posthumanism as "a rejection of individualism, anthropocentrism, and species hierarchy," challenging the assumption that literature must revolve around human experience. Language itself becomes a site of tension in the transition from human to posthuman literature.

Robots are used to make human life easier. In our daily life, the work done with classical machines is done with autonomous robots by reducing human intervention. Thanks to the network infrastructure, remote command operation and communication with each other can be provided. It is also possible to do the work done with only one robot in a shorter time with many robots. In this case, it is necessary for the robots to communicate and coordinate with each other. Various algorithms have been developed on this situation, which is evaluated within the scope of swarm robot studies. Coordinated movement is envisaged in studies with more than one robot of the same type. In some cases, the swarm may also consist of heterogeneous robots. In this case, it is necessary to coordinate according to the abilities of robots with different characteristics. In this study, information will be given on swarm robots, their usage purposes, and structures. Physical robot applications used to test academic studies will be mentioned.

Whereas naturally occurring swarms thrive when crowded, physical interactions in robotic swarms are either avoided or carefully controlled, thus limiting their operational density. Here we present a mechanical design rule that allows robots to act in a collision-dominated environment. We introduce the Morphobots -a robotic swarm platform developed to implement embodied computation through a morpho-functional design. By engineering a 3D-printed exoskeleton we encode a re-orientation response to an external body force (such as gravity) or a surface force (such as a collision). We show that the force-orientation response is generic, and can augment existing swarmrobotic platforms (e.g Kilobots) as well as custom robots even 10 times larger. At the individual level, the exoskeleton improves the motility and stability, and also allows to encode two contrasting dynamical behaviors in response to an external force or a collision (including collision with a wall or a movable obstacle, and on a dynamically tilting plane). This force-orientation response adds a mechanical layer to the robot's sense-act cycle at the swarm level, leveraging steric interactions for collective phototaxis when crowded. Enabling collisions also promotes information flow, facilitating online distributed learning. Each robot runs an embedded algorithm that ultimately optimizes collective performance. We identify an effective parameter that controls the force-orientation response and explore its implications in swarms that transition from dilute to crowded. Experimenting with both physical swarms (of up to 64 robots), and simulated swarms (of up to 8192 agents) show that the effect of morphological-computation increases with growing swarm size.

Whereas naturally occurring swarms thrive when crowded, physical interactions in robotic swarms are either avoided or carefully controlled, thus limiting their operational density. Here we present a mechanical design rule that allows robots to act in a collision-dominated environment. We introduce the Morphobots -a robotic swarm platform developed to implement embodied computation through a morpho-functional design. By engineering a 3D-printed exoskeleton we encode a re-orientation response to an external body force (such as gravity) or a surface force (such as a collision). We show that the force-orientation response is generic, and can augment existing swarmrobotic platforms (e.g Kilobots) as well as custom robots even 10 times larger. At the individual level, the exoskeleton improves the motility and stability, and also allows to encode two contrasting dynamical behaviors in response to an external force or a collision (including collision with a wall or a movable obstacle, and on a dynamically tilting plane). This force-orientation response adds a mechanical layer to the robot's sense-act cycle at the swarm level, leveraging steric interactions for collective phototaxis when crowded. Enabling collisions also promotes information flow, facilitating online distributed learning. Each robot runs an embedded algorithm that ultimately optimizes collective performance. We identify an effective parameter that controls the force-orientation response and explore its implications in swarms that transition from dilute to crowded. Experimenting with both physical swarms (of up to 64 robots), and simulated swarms (of up to 8192 agents) show that the effect of morphological-computation increases with growing swarm size.

This paper provides an overview of the SWARM-BOTS project, a robotic project sponsored by the Future and Emerging Technologies program of the European Commission. The paper illustrates the goals of the project, the robot prototype and the 3D simulator we built. It also reports on the results of experimental work in which distributed adaptive controllers are used to control a group of real, or simulated, robots so that they perform a variety of tasks which require cooperation and coordination. Marco Dorigo et al.

This paper presents the design, evaluation, and field experiment of the innovative Adapt- able Human-Swarm Teaming ( α -SWAT) interface developed to support military field operations. Human-swarm teaming requires collaboration between a team of humans and a team of robotic agents for strategic decision-making and task performance. α -SWAT allows multiple human team members with different roles, physical capabilities, or preferences to interact with the swarm via a configurable, multimodal user interface (UI). The system has an embedded task allocation algorithm for the rapid assignment of tasks created by the mission planner to the swarm. The multimodal UI supports swarm visualization via a mixed reality display or a conventional 2D display, human gesture inputs via a camera or an electromyography device, tactile feedback via a vibration motor or implanted peripheral nerve interface, and audio feedback. In particular, the UI system interfacing with the implanted electrodes through a...

We investigate how reliable movement can emerge in aggregates of highly error-prone individuals. The individuals—robotic modules—move stochastically using vibration motors. By coupling them via elastic links, soft-bodied aggregates can be created. We present distributed algorithms that enable the aggregates to move and deform reliably. The concept and algorithms are validated through formal analysis of the elastic couplings and experiments with aggregates comprising up to 49 physical modules—among the biggest soft-bodied aggregates to date made of autonomous modules. The experiments show that aggregates with elastic couplings can shrink and stretch their bodies, move with a precision that increases with the number of modules, and outperform aggregates with no, or rigid, couplings. Our findings demonstrate that mechanical couplings can play a vital role in reaching coherent motion among individuals with exceedingly limited and error-prone abilities, and may pave the way for low-power...

Swarm robotics describes a multi-robot system characterized by the simplicity of its agents, homogeneous architecture, limited communication skills, local detection, execution of parallel tasks, robustness, scalability, flexibility and decentralized control. However, being a technology in development, the security and vulnerability of the swarm of robots against possible cybernetic attacks have been commonly overlooked. This is of major concern when executing mission-critical activities that inherently require an adequate management of security. In this work, a bio-inspired security mechanism applied to a swarm of robots is proposed. Through computer simulations, it is observed how the mechanism, when executing a homing towards a stationary landmark, allows the swarm of robots to identify abnormal behaviors, caused by a certain cyberattack; subsequently, it establishes a certain tolerance to it and allows to improve the level of availability that is required to continue executing the task at hand.

The task of locating a source based on the measurements of the signal emitted/emanating from it is called the source-seeking problem. In the past few years, there has been a lot of interest in deploying autonomous platforms for source-seeking. Some of the challenging issues with implementing autonomous source-seeking are the lack of a priori knowledge about the distribution of the emitted signal and presence of noise in both the environment and on-board sensor measurements. This paper proposes a planner for a swarm of robots engaged in seeking an electromagnetic source. The navigation strategy for the planner is based on Particle Swarm Optimization (PSO) which is a population-based stochastic optimization technique. An equivalence is established between particles generated in the traditional PSO technique, and the mobile agents in the swarm. Since the positions of the robots are updated using the PSO algorithm, modifications are required to implement the PSO algorithm on real robots to incorporate collision avoidance strategies. The modifications necessary to implement PSO on mobile robots, and strategies to adapt to real environments are presented in this paper. Our results are also validated on an experimental testbed. Note to Practitioners-This paper is inspired by the source seeking problem in which the signal emitted from the source is assumed to be very noisy, and the spatial distribution is assumed to be non-smooth. We focus our work specifically on electromagnetic sources. However, the strategies proposed in this paper are also applicable to other kinds of sources, for example, nuclear, radiological, chemical or biological. We develop a planner for a swarm of mobile agents that try to locate an unknown electromagnetic source. The mobile agents know their own positions and can measure the signal strength at their current location. They can share information among themselves, and plan for the next step. We propose a complete solution to ensure the effectiveness of PSO in complex environments where collisions may occur. We incorporate static and dynamic obstacle avoidance strategies in PSO to make it fully applicable to real-world scenario. We validate the proposed technique on an experimental testbed. As a part of our future work, we will extend the technique to locate multiple sources of different kinds.

Uno de los principales problemas para diseñar e implementar un sistema de supervisión y control para un proceso radica en la necesidad de establecer una solución ad-hoc para cada instalación. La interoperabilidad de los diferentes métodos utilizados para este fin es uno de los desafíos actuales relacionados con el desarrollo de Sistemas Inteligentes de Soporte a la Toma de Decisiones (IDSS), con el objetivo de garantizar la interacción y reutilización de los diferentes métodos basados en modelos, en conocimiento experto o en minería de datos.

Advancements in light detection and ranging (LiDAR) technology have significantly improved robotics and automated navigation. However, the high cost of traditional LiDAR sensors restricts their use in small-scale robotic projects. This paper details the development of a low-cost LiDAR prototype for small mobile robots, using time-of-flight (ToF) sensors as a cost-effective alternative. Integrated with an ESP32 microcontroller for real-time data processing and Wi-Fi connectivity, the prototype facilitates accurate distance measurement and environmental mapping, crucial for autonomous navigation. Our approach included hardware design and assembly, followed by programming the ToF sensors and ESP32 for data collection and actuation. Experiments validated the accuracy of the ToF sensors under static, dynamic, and varied lighting conditions. Results show that our low-cost system achieves accuracy and reliability comparable to more expensive options, with an average mapping error within acceptable limits for practical use. This work offers a blueprint for affordable LiDAR systems, expanding access to technology for research and education, and demonstrating the viability of ToF sensors in economical robotic navigation and mapping solutions. This is an open access article under the CC BY-SA license.

In this paper, we discuss the self-assembling capabilities of the swarm-bot, a distributed robotics concept that lies at the intersection between collective and self-reconfigurable robotics. A swarm-bot is comprised of autonomous mobile robots called s-bots. S-bots can either act independently or self-assemble into a swarm-bot by using their grippers. We report on experiments in which we study the process that leads a group of s-bots to self-assemble. In particular, we present results of experiments in which we vary the number of s-bots (up to 16 physical robots), their starting configurations, and the properties of the terrain on which self-assembly takes place. In view of the very successful experimental results, swarm-bot qualifies as the current state of the art in autonomous self-assembly.

Mobile mini-robots are commonly used for research, education and popularization of science. Often, commercially available mini-robots don't quite fit the characteristics needed for a particular task, and are difficult to adapt since they have proprietary software and hardware. Moreover, they are often quite expensive. In this work we present a relatively low-cost, reconfigurable robot equipped with a wide variety of sensors and enough processing power to allow the on-board execution of intelligent algorithms. We present the complete hardware architecture, and a modularized software architecture that makes full use of hardware interruptions and software processes to have a perfectly timed control of the robot. All these characteristics make the new mobile mini robot ExaBot a very malleable, multi task mini-robot.

The robots today become very powerful tools that support many human tasks. However, the trend of development has often been separated from its application. The use of swarm robots to search in collapsed environments today becomes in a real alternative solution to support rescue efforts in landslide and other disasters. However, these environments have special features (unknown and dynamic) under which rapid responses are required (little processing time) with a major limitation of sensors (problems for the use of cameras and GPS’s for example) and communication. In this paper, we analyze an alternative of navigation for swarm robots with very limited capabilities (very limited processing power, communication and sensing). Our minimalist approach seeks to solve the problem without requiring system identification, geometric map building, localization, or state estimation. Instead, we propose a strategy based on Brownian motion, in which each robot is modeled as a particle whose motion...

Swarm Robotics: Cooperative Navigation in Unknown Environments Swarm Robotics is garnering attention in the robotics field due to its substantial benefits. It has been proven to outperform most other robotic approaches in many applications such as military, space exploration and disaster search and rescue missions. It is inspired by the behavior of swarms of social insects such as ants and bees. It consists of a number of robots with limited capabilities and restricted local sensing. When deployed, individual robots behave according to local sensing until the emergence of a global behavior where they, as a swarm, can accomplish missions individuals cannot. In this research, we propose a novel exploration and navigation method based on a combination of Probabilistic Finite Sate Machine (PFSM), Robotic Darwinian Particle Swarm Optimization (RDPSO) and Depth First Search (DFS). We use V-REP Simulator to test our approach. We are also implementing our own cost effective swarm robot plat...

Work solutions are proposed for problems of leader definition and role distribution in homogeneous groups of robots. It was shown that transition from a swarm to a collective of robots with hierarchical organisation is possible using exclusively local interaction. The local re-voting algorithm is central to the procedure for choice of leader while distribution of roles can be achieved by a wave method. The basis for this approach is the static swarm model characterised by the absence of a set control centre; it represents the network fixed at some time interval as a set of locally interacting agents. A task of cooperative hunting by distributed mobile robots based on local interaction was considered. Two strategies were used for the hunting task solution: individual hunting and pack-hunting. Simulation results showed that symbiosis of leader election and role distribution procedures has advantages over the individual strategy.

Work solutions are proposed for problems of leader definition and role distribution in homogeneous groups of robots. It is shown that transition from a swarm to a collective of robots with hierarchical organization is possible using exclusively local interaction. The local revoting algorithm is central to the procedure for choice of leader while redistribution of roles can be achieved by a wave method. The basis for this approach is the static swarm model, which is characterized by the absence of a set control center and represents the network fixed at some time interval as a set of locally interacting agents.

Novelty search is an evolutionary approach which promotes phenotypic diversity in a population. Novelty search has been successfully applied to a wide range of domains and a number of variants have been proposed. Here we introduce Multiple Assessment Directed Novelty Search (MADNS), which exploits the notion that a diverse population optimised through phenotypic novelty may contain solutions to multiple conflicting objectives. We show that by utilising the MADNS algorithm, an evolutionary trajectory may be simultaneously directed towards conflicting objectives. We conclude that, through applying MADNS and MC-MADNS, a divergent evolutionary trajectory may be directed to provide simultaneous solutions to multiple conflicting problems in domains with large potential for exploration.

Swarm Robotics is a new approach to the coordination of a large number of robots inspired by nature. This approach aims to design collective behaviors for a large number of robots. Unfortunately, several researchers have tried to develop structured design methods but these methods are still limited. Today, swarm robotics are used in many fields, they are used in agriculture, medicine, industrial, etc. One of the most important fields that require swarm robots is surveillance. In this paper, we present in the first section some methods of designing swarm robot systems by identifying swarm engineering based on a model (MBSE) and multi-agent simulation. Then, we study the energy problem of these robotic systems and the solution proposed by the researchers. In the second section, we present an application for detecting oil in the sea and cleaning it using swarm robots. We will model this application using the MBSE method with the SysML language. Finally, we will simulate the model on a multi-agent tool to identify the functional and structural architecture of the system.

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We present a collective tracking problem in which the agents in a swarm collectively control a tracker to follow a target moving in two dimensional space. Tasks are represented by the four cardinal directions and task demand refers to differences between the target and tracker locations in each of the directions. In any given timestep, each agent can choose to address task demand in a single direction. Tracker movement in a timestep is generated by aggregating the choices of all of the agents in the swarm in that timestep. Thus, the agents in a swarm collectively move a tracker in response to a target path. This problem is representative of a general decentralized task allocation problem in which one or more tasks exhibit demand that is addressed by allocating an appropriate number of agents to each task. The tracking problem allows us to define dynamically changing task demands in a systematic way in the form of target paths. Because the variety of target paths that can be defined ...

One of nature's most intriguing and complicated phenomena is learning in biological systems, occurring over multiple scales of organisation and using different mechanisms that with the aid of billions of years have emerged in biological systems. In this comprehensive review, I assess the many faces of biological learning, ranging from the molecular finesse of cellular memory to the emergent properties of ecosystem wide adaptations.

This article studies the use of task partitioning as a way to reduce interference in a spatially constrained harvesting task. Interference is one of the key problems in large cooperating groups. We present a simple method to allocate individuals of a robotic swarm to a partitioned task, and show that task partitioning can increase system performance by reducing sources of interference. The method is experimentally studied, both in an environment with a narrow area and an environment without this constraint. The results are analyzed and compared to the case in which task partitioning is not employed.

Algorithms using swarming collective motion can solve coverage problems in unknown environments by reacting to unknown obstacles in real-time when they are encountered. However, these algorithms face two key challenges when deployed on real robots. First, hand-tuning efficient collective motion parameters is both time-consuming and difficult. Second, predicting the time required for a swarm to solve a particular problem is not straightforward. This paper introduces a novel evolutionary framework to address both problems by proposing a methodology that autonomously tunes collective motion parameters for coverage problems while predicting the time required for real robots to complete the task. Our approach utilizes a simulation–optimization framework that employs a genetic algorithm to optimize the parameters of a frontier-led swarming algorithm. Results indicate that the optimized parameters are transferable to real robots, achieving 100% coverage while maintaining 84% connectivity between them. Compared to state-of-the-art swarm methods, our system reduced turnaround time by 50% and 57% in different environments while maintaining collective motion. It also achieved a 55% reduction in turnaround time on average across five scenarios compared to budget-constrained path planning, with a 10% increase in coverage. Furthermore, our framework outperformed both hand-tuned and learned collective motion approaches, reducing turnaround time by 73% in non-collective motion scenarios and by 63% while maintaining 85% connectivity in collective motion scenarios. This approach effectively combines the adaptability of swarm behavior with the predictive reliability of planning methods.

The integration of artificial intelligence (AI) into
space exploration has ushered in a new era of discovery and
innovation. While AI offers immense potential for autonomous
decision-making, data analysis, and adaptive problem-solving, its
deployment in the harsh space environment poses significant
risks. This research paper investigates the multifaceted challenges
associated with AI in space, including the impact of cosmic
radiation, extreme temperatures, and communication disruptions
on AI system reliability and resilience. Through a comprehensive
review of literature, controlled experiments, and preliminary
findings, the paper highlights the need for specialized AI
development tailored to the unique demands of the cosmic
frontier. By understanding and mitigating these risks, we can
ensure the safe and effective utilization of AI in future space
missions, paving the way for continued exploration and
scientific advancement.