Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms

The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications

Path Planning and Energy Efficiency of Heterogeneous Mobile Robots Using Cuckoo–Beetle Swarm Search Algorithms with Applications in UGV Obstacle Avoidance

In this paper, a new meta-heuristic path planning algorithm, the cuckoo–beetle swarm search (CBSS) algorithm, is introduced to solve the path planning problems of heterogeneous mobile robots. Traditional meta-heuristic algorithms, e.g., genetic algorithms (GA), particle swarm search (PSO), beetle swarm optimization (BSO), and cuckoo search (CS), have problems such as the tenancy to become trapped in local minima because of premature convergence and a weakness in global search capability in path planning. Note that the CBSS algorithm imitates the biological habits of cuckoo and beetle herds and thus has good robustness and global optimization ability. In addition, computer simulations verify the accuracy, search speed, energy efficiency and stability of the CBSS algorithm. The results of the real-world experiment prove that the proposed CBSS algorithm is much better than its counterparts. Finally, the CBSS algorithm is applied to 2D path planning and 3D path planning in heterogeneous mobile robots. In contrast to its counterparts, the CBSS algorithm is guaranteed to find the shortest global optimal path in different sizes and types of maps

Swarming Reconnaissance Using Unmanned Aerial Vehicles in a Parallel Discrete Event Simulation

Current military affairs indicate that future military warfare requires safer, more accurate, and more fault-tolerant weapons systems. Unmanned Aerial Vehicles (UAV) are one answer to this military requirement. Technology in the UAV arena is moving toward smaller and more capable systems and is becoming available at a fraction of the cost. Exploiting the advances in these miniaturized flying vehicles is the aim of this research. How are the UAVs employed for the future military? The concept of operations for a micro-UAV system is adopted from nature from the appearance of flocking birds, movement of a school of fish, and swarming bees among others. All of these natural phenomena have a common thread: a global action resulting from many small individual actions. This emergent behavior is the aggregate result of many simple interactions occurring within the flock, school, or swarm. In a similar manner, a more robust weapon system uses emergent behavior resulting in no weakest link because the system itself is made up of simple interactions by hundreds or thousands of homogeneous UAVs. The global system in this research is referred to as a swarm. Losing one or a few individual unmanned vehicles would not dramatically impact the swarms ability to complete the mission or cause harm to any human operator. Swarming reconnaissance is the emergent behavior of swarms to perform a reconnaissance operation. An in-depth look at the design of a reconnaissance swarming mission is studied. A taxonomy of passive reconnaissance applications is developed to address feasibility. Evaluation of algorithms for swarm movement, communication, sensor input/analysis, targeting, and network topology result in priorities of each model\u27s desired features. After a thorough selection process of available implementations, a subset of those models are integrated and built upon resulting in a simulation that explores the innovations of swarming UAVs

Swarm autonomic agents with self-destruct capability

Systems, methods and apparatus are provided through which in some embodiments an autonomic entity manages a system by generating one or more stay alive signals based on the functioning status and operating state of the system. In some embodiments, an evolvable synthetic neural system is operably coupled to one or more evolvable synthetic neural systems in a hierarchy. The evolvable neural interface receives and generates heartbeat monitor signals and pulse monitor signals that are used to generate a stay alive signal that is used to manage the operations of the synthetic neural system. In another embodiment an asynchronous Alice signal (Autonomic license) requiring valid credentials of an anonymous autonomous agent is initiated. An unsatisfactory Alice exchange may lead to self-destruction of the anonymous autonomous agent for self-protection

Flight coordination solutions for multirotor unmanned aerial vehicles

[EN] As the popularity and the number of Unmanned Aerial Vehicles (UAVs) increases, new protocols are needed to coordinate them when flying without direct human control, and to avoid that these UAVs collide with each other. Testing such novel protocols on real UAVs is a complex procedure that requires investing much time, money and research efforts. Hence, it becomes necessary to first test the developed solutions using simulation. Unfortunately, existing tools present significant limitations: some of them only simulate accurately the flight behavior of a single UAV, while some other simulators can manage several UAVs simultaneously, but not in real time, thus losing accuracy regarding the mobility pattern of the UAV. In this work we address such problem by introducing Arducopter Simulator (ArduSim), a novel simulation platform that allows controlling in soft real-time the flight and communications of multiple UAVs, being the developed protocols directly portable to real devices. Moreover, ArduSim includes a realistic model for the WiFi communications link between UAVs, which was proposed based on real experiments. The chances that two UAVs get close to each other during their flights is increasing as more and more of them populate our skies, causing concerns regarding potential collisions. Therefore, this thesis also proposes the Mission Based Collision Avoidance Protocol (MBCAP), a novel UAV collision avoidance protocol applicable to all types of multicopters flying autonomously. It relies on wireless communications in order to detect nearby UAVs, and to negotiate the procedure to avoid any potential collision. Experimental and simulation results demonstrate the validity and effectiveness of the proposed solution, which typically introduces a small overhead in the range of 15 to 42 seconds for each risky situation successfully handled. The previous solution aims at UAVs performing independent flights, but they can also form a swarm, where more constraints have to be met to avoid collisions among them, and to allow them to complete their task efficiently. Deploying an UAV swarm instead of a single UAV can provide additional benefits when, for example, cargo carrying requirements exceed the lifting power of a single UAV, or when the deployment of several UAVs simultaneously can accelerate the accomplishment of the mission, and broaden the covered area. To this aim, in this work we present the Mission-based UAV Swarm Coordination Protocol (MUSCOP), a solution that allows multiple UAVs to perfectly coordinate their flight when performing planned missions. Experimental results show that the proposed protocol is able to achieve a high degree of swarm cohesion independently of the flight formation adopted, and even in the presence of very lossy channels, achieving minimal synchronization delays and very low position offsets with regard to the ideal case. Currently, there are some other scenarios, such as search and rescue operations, where the deployment of manually guided swarms of UAVs can be necessary. In such cases, the pilot's commands are unknown a priori (unpredictable), meaning that the UAVs must respond in near real-time to the movements of the leader UAV in order to maintain swarm consistency. Hence, in this thesis we also propose the FollowMe protocol for the coordination of UAVs in a swarm where the swarm leader is controlled by a real pilot, and the other UAVs must follow it in real-time to maintain swarm cohesion. Simulation results show the validity of the proposed swarm coordination protocol, detailing the responsiveness limits of our solution, and finding the minimum distances between UAVs to avoid collisions.[ES] A medida que la popularidad de los Vehículos Aéreos No Tripulados (VANTs) se incrementa, también se hacen necesarios nuevos protocolos para coordinarlos en vuelos sin control humano directo, y para evitar que colisionen entre sí. Probar estos nuevos protocolos en VANTs reales es un proceso complejo que requiere invertir mucho tiempo, dinero y esfuerzo investigador. Por lo tanto, es necesario probar en simulación las soluciones previamente implementadas. Lamentablemente, las herramientas actuales tienen importantes limitaciones: algunas simulan con precisión el vuelo de un único VANT, mientras que otros simuladores pueden gestionar varios VANTs simultáneamente aunque no en tiempo real, perdiendo por lo tanto precisión en el patrón de movilidad del VANT. En este trabajo abordamos este problema introduciendo Arducopter Simulator (ArduSim), una nueva plataforma de simulación que permite controlar en tiempo real el vuelo y la comunicación entre múltiples VANTs, permitiendo llevar los protocolos desarrollados a dispositivos reales con facilidad. Además, ArduSim incluye un modelo realista de un enlace de comunicaciones WiFi entre VANTs, el cual está basado en el resultado obtenido de experimentos con VANTs reales. La posibilidad de que dos VANTs se aproximen entre sí durante el vuelo se incrementa a medida que hay más aeronaves de este tipo surcando los cielos, introduciendo peligro por posibles colisiones. Por ello, esta tesis propone Mission Based Collision Avoidance Protocol (MBCAP), un nuevo protocolo de evitación de colisiones para VANTs aplicable a todo tipo de multicópteros mientras vuelan autónomamente. MBCAP utiliza comunicaciones inalámbricas para detectar VANTs cercanos y para negociar el proceso de evitación de la colisión. Los resultados de simulaciones y experimentos reales demuestran la validez y efectividad de la solución propuesta, que introduce un pequeño aumento del tiempo de vuelo de entre 15 y 42 segundos por cada situación de riesgo correctamente resuelta. La solución anterior está orientada a VANTs que realizan vuelos independientes, pero también pueden formar un enjambre, donde hay que cumplir más restricciones para evitar que colisionen entre sí, y para que completen la tarea de forma eficiente. Desplegar un enjambre de VANTs en vez de uno solo proporciona beneficios adicionales cuando, por ejemplo, la necesidad de carga excede la capacidad de elevación de un único VANT, o cuando al desplegar varios VANTs simultáneamente se acelera la misión y se cubre un área mayor. Con esta finalidad, en este trabajo presentamos el protocolo Mission-based UAV Swarm Coordination Protocol (MUSCOP), una solución que permite a varios VANTs coordinar perfectamente el vuelo mientras realizan misiones planificadas. Los resultados experimentales muestran que el protocolo propuesto permite al enjambre alcanzar un grado de cohesión elevado independientemente de la formación de vuelo adoptada, e incluso en presencia de un canal de comunicación con muchas pérdidas, consiguiendo retardos en la sincronización insignificantes y desfases mínimos en la posición con respecto al caso ideal. Actualmente hay otros escenarios, como las operaciones de búsqueda y rescate, donde el despliegue de enjambres de VANTs guiados manualmente puede ser necesario. En estos casos, las órdenes del piloto son desconocidas a priori (impredecibles), lo que significa que los VANTs deben responder prácticamente en tiempo real a los movimientos del VANT líder para mantener la consistencia del enjambre. Por ello, en esta tesis proponemos el protocolo FollowMe para la coordinación de VANTs en un enjambre donde el líder es controlado por un piloto, y el resto de VANTs lo siguen en tiempo real para mantener la cohesión del enjambre. Las simulaciones muestran la validez del protocolo de coordinación de enjambres propuesto, detallando los límites de la solución, y definiendo la distancia mínima entre VANTs para evita[CA] A mesura que la popularitat dels Vehicles Aeris No Tripulats (VANTs) s'incrementa, també es fan necessaris nous protocols per a coordinar-los en vols sense control humà directe, i per a evitar que col·lisionen entre si. Provar aquests nous protocols en VANTs reals és un procés complex que requereix invertir molt de temps, diners i esforç investigador. Per tant, és necessari provar en simulació les solucions prèviament implementades. Lamentablement, les eines actuals tenen importants limitacions: algunes simulen amb precisió el vol d'un únic VANT, mentre que altres simuladors poden gestionar diversos VANTs simultàniament encara que no en temps real, perdent per tant precisió en el patró de mobilitat del VANT. En aquest treball abordem aquest problema introduint Arducopter Simulator (ArduSim), una nova plataforma de simulació que permet controlar en temps real el vol i la comunicació entre múltiples VANTs, permetent portar els protocols desenvolupats a dispositius reals amb facilitat. A més, ArduSim inclou un model realista d'un enllaç de comunicacions WiFi entre VANTs, que està basat en el resultat obtingut d'experiments amb VANTs reals. La possibilitat que dues VANTs s'aproximen entre si durant el vol s'incrementa a mesura que hi ha més aeronaus d'aquest tipus solcant els cels, introduint perill per possibles col·lisions. Per això, aquesta tesi proposa Mission Based Collision Avoidance Protocol (MBCAP), un nou protocol d'evitació de col·lisions per a VANTs aplicable a tota mena de multicòpters mentre volen autònomament. MBCAP utilitza comunicacions sense fils per a detectar VANTs pròxims i per a negociar el procés d'evitació de la col·lisió. Els resultats de simulacions i experiments reals demostren la validesa i efectivitat de la solució proposada, que introdueix un xicotet augment del temps de vol de entre 15 i 42 segons per cada situació de risc correctament resolta. La solució anterior està orientada a VANTs que realitzen vols independents, però també poden formar un eixam, on cal complir més restriccions per a evitar que col·lisionen entre si, i perquè completen la tasca de forma eficient. Desplegar un eixam de VANTs en comptes d'un només proporciona beneficis addicionals quan, per exemple, la necessitat de càrrega excedeix la capacitat d'elevació d'un únic VANT, o quan en desplegar diversos VANTs simultàniament s'accelera la missió i es cobreix una àrea major. Amb aquesta finalitat, en aquest treball presentem el protocol Mission-based UAV Swarm Coordination Protocol (MUSCOP), una solució que permet a diversos VANTs coordinar perfectament el vol mentre realitzen missions planificades. Els resultats experimentals mostren que el protocol proposat permet a l'eixam aconseguir un grau de cohesió elevat independentment de la formació de vol adoptada, i fins i tot en presència d'un canal de comunicació amb moltes pèrdues, aconseguint retards en la sincronització insignificants i desfasaments mínims en la posició respecte al cas ideal. Actualment hi ha altres escenaris, com les operacions de cerca i rescat, on el desplegament d'eixams de VANTs guiats manualment pot ser necessari. En aquests casos, les ordres del pilot són desconegudes a priori (impredictibles), el que significa que els VANTs han de respondre pràcticament en temps real als moviments del VANT líder per a mantindre la consistència de l'eixam. Per això, en aquesta tesi proposem el protocol FollowMe per a la coordinació de VANTs en un eixam on el líder és controlat per un pilot, i la resta de VANTs ho segueixen en temps real per a mantindre la cohesió de l'eixam. Les simulacions mostren la validesa del protocol de coordinació d'eixams proposat, detallant els límits de la solució, i definint la distància mínima entre VANTs per a evitar col·lisions.Fabra Collado, FJ. (2020). Flight coordination solutions for multirotor unmanned aerial vehicles [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/147857TESI

Swarm autonomic agents with self-destruct capability

Systems, methods and apparatus are provided through which in some embodiments an autonomic entity manages a system by generating one or more stay alive signals based on the functioning status and operating state of the system. In some embodiments, an evolvable synthetic neural system is operably coupled to one or more evolvable synthetic neural systems in a hierarchy. The evolvable neural interface receives and generates heartbeat monitor signals and pulse monitor signals that are used to generate a stay alive signal that is used to manage the operations of the synthetic neural system. In another embodiment an asynchronous Alice signal (Autonomic license) requiring valid credentials of an anonymous autonomous agent is initiated. An unsatisfactory Alice exchange may lead to self-destruction of the anonymous autonomous agent for self-protection

Nanorobotics in Medicine: A Systematic Review of Advances, Challenges, and Future Prospects

Nanorobotics offers an emerging frontier in biomedicine, holding the potential to revolutionize diagnostic and therapeutic applications through its unique capabilities in manipulating biological systems at the nanoscale. Following PRISMA guidelines, a comprehensive literature search was conducted using IEEE Xplore and PubMed databases, resulting in the identification and analysis of a total of 414 papers. The studies were filtered to include only those that addressed both nanorobotics and direct medical applications. Our analysis traces the technology's evolution, highlighting its growing prominence in medicine as evidenced by the increasing number of publications over time. Applications ranged from targeted drug delivery and single-cell manipulation to minimally invasive surgery and biosensing. Despite the promise, limitations such as biocompatibility, precise control, and ethical concerns were also identified. This review aims to offer a thorough overview of the state of nanorobotics in medicine, drawing attention to current challenges and opportunities, and providing directions for future research in this rapidly advancing field

Evolvable synthetic neural system

An evolvable synthetic neural system includes an evolvable neural interface operably coupled to at least one neural basis function. Each neural basis function includes an evolvable neural interface operably coupled to a heuristic neural system to perform high-level functions and an autonomic neural system to perform low-level functions. In some embodiments, the evolvable synthetic neural system is operably coupled to one or more evolvable synthetic neural systems in a hierarchy