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

Improving Artificial-Immune-System-based computing by exploiting intrinsic features of computer architectures

Biological systems have become highly significant for traditional computer architectures as examples of highly complex self-organizing systems that perform tasks in parallel with no centralized control. However, few researchers have compared the suitability of different computing approaches for the unique features of Artificial Immune Systems (AIS) when trying to introduce novel computing architectures, and few consider the practicality of their solutions for real world machine learning problems. We propose that the efficacy of AIS-based computing for tackling real world datasets can be improved by the exploitation of intrinsic features of computer architectures. This paper reviews and evaluates current existing implementation solutions for AIS on different computing paradigms and introduces the idea of “C Principles” and “A Principles”. Three Artificial Immune Systems implemented on different architectures are compared using these principles to examine the possibility of improving AIS through taking advantage of intrinsic hardware features

"Going back to our roots": second generation biocomputing

Researchers in the field of biocomputing have, for many years, successfully "harvested and exploited" the natural world for inspiration in developing systems that are robust, adaptable and capable of generating novel and even "creative" solutions to human-defined problems. However, in this position paper we argue that the time has now come for a reassessment of how we exploit biology to generate new computational systems. Previous solutions (the "first generation" of biocomputing techniques), whilst reasonably effective, are crude analogues of actual biological systems. We believe that a new, inherently inter-disciplinary approach is needed for the development of the emerging "second generation" of bio-inspired methods. This new modus operandi will require much closer interaction between the engineering and life sciences communities, as well as a bidirectional flow of concepts, applications and expertise. We support our argument by examining, in this new light, three existing areas of biocomputing (genetic programming, artificial immune systems and evolvable hardware), as well as an emerging area (natural genetic engineering) which may provide useful pointers as to the way forward.Comment: Submitted to the International Journal of Unconventional Computin

Cooperative strategies for the detection and localization of odorants with robots and artificial noses

En este trabajo de investigación se aborda el diseño de una plataforma robótica orientada a la implementación de estrategias de búsqueda cooperativa bioinspiradas. En particular, tanto el proceso de diseño de la parte electrónica como hardware se han enfocado hacia la validación en entornos reales de algoritmos capaces de afrontar problemas de búsqueda con incertidumbre, como lo es la búsqueda de fuentes de olor que presentan variación espacial y temporal. Este tipo de problemas pueden ser resueltos de forma más eficiente con el empleo de enjambres con una cantidad razonable de robots, y por tanto la plataforma ha sido desarrollada utilizando componentes de bajo coste. Esto ha sido posible por la combinación de elementos estandarizados -como la placa controladora Arduino y otros sensores integrados- con piezas que pueden ser fabricadas mediante una impresora 3D atendiendo a la filosofía del hardware libre (open-source). Entre los requisitos de diseño se encuentran además la eficiencia energética -para maximizar el tiempo de funcionamiento de los robots-, su capacidad de posicionamiento en el entorno de búsqueda, y la integración multisensorial -con la inclusión de una nariz electrónica, sensores de luminosidad, distancia, humedad y temperatura, así como una brújula digital-. También se aborda el uso de una estrategia de comunicación adecuada basada en ZigBee. El sistema desarrollado, denominado GNBot, se ha validado tanto en los aspectos de eficiencia energética como en sus capacidades combinadas de posicionamiento espacial y de detección de fuentes de olor basadas en disoluciones de etanol. La plataforma presentada -formada por el GNBot, su placa electrónica GNBoard y la capa de abstracción software realizada en Python- simplificará por tanto el proceso de implementación y evaluación de diversas estrategias de detección, búsqueda y monitorización de odorantes, con la estandarización de enjambres de robots provistos de narices artificiales y otros sensores multimodales.This research work addresses the design of a robotic platform oriented towards the implementation of bio-inspired cooperative search strategies. In particular, the design processes of both the electronics and hardware have been focused towards the real-world validation of algorithms that are capable of tackling search problems that have uncertainty, such as the search of odor sources that have spatio-temporal variability. These kind of problems can be solved more efficiently with the use of swarms formed by a considerable amount of robots, and thus the proposed platform makes use of low cost components. This has been possible with the combination of standardized elements -as the Arduino controller board and other integrated sensors- with custom parts that can be manufactured with a 3D printer attending to the open-source hardware philosophy. Among the design requirements is the energy efficiency -in order to maximize the working range of the robots-, their positioning capability within the search environment, and multiple sensor integration -with the incorporation of an artificial nose, luminosity, distance, humidity and temperature sensors, as well as an electronic compass-. Another subject that is tackled is the use of an efficient wireless communication strategy based on ZigBee. The developed system, named GNBot, has also been validated in the aspects of energy efficiency and for its combined capabilities for autonomous spatial positioning and detection of ethanol-based odor sources. The presented platform -formed by the GNBot, the GNBoard electronics and the abstraction layer built in Python- will thus simplify the processes of implementation and evaluation of various strategies for the detection, search and monitoring of odorants with conveniently standardized robot swarms provided with artificial noses and other multimodal sensors

Human Swarm Interaction: An Experimental Study of Two Types of Interaction with Foraging Swarms

In this paper we present the first study of human-swarm interaction comparing two fundamental types of interaction, coined intermittent and environmental. These types are exemplified by two control methods, selection and beacon control, made available to a human operator to control a foraging swarm of robots. Selection and beacon control differ with respect to their temporal and spatial influence on the swarm and enable an operator to generate different strategies from the basic behaviors of the swarm. Selection control requires an active selection of groups of robots while beacon control exerts an influence on nearby robots within a set range. Both control methods are implemented in a testbed in which operators solve an information foraging problem by utilizing a set of swarm behaviors. The robotic swarm has only local communication and sensing capabilities. The number of robots in the swarm range from 50 to 200. Operator performance for each control method is compared in a series of missions in different environments with no obstacles up to cluttered and structured obstacles. In addition, performance is compared to simple and advanced autonomous swarms. Thirty-two participants were recruited for participation in the study. Autonomous swarm algorithms were tested in repeated simulations. Our results showed that selection control scales better to larger swarms and generally outperforms beacon control. Operators utilized different swarm behaviors with different frequency across control methods, suggesting an adaptation to different strategies induced by choice of control method. Simple autonomous swarms outperformed human operators in open environments, but operators adapted better to complex environments with obstacles. Human controlled swarms fell short of task-specific benchmarks under all conditions. Our results reinforce the importance of understanding and choosing appropriate types of human-swarm interaction when designing swarm systems, in addition to choosing appropriate swarm behaviors

The Problem of Adhesion Methods and Locomotion Mechanism Development for Wall-Climbing Robots

This review considers a problem in the development of mobile robot adhesion methods with vertical surfaces and the appropriate locomotion mechanism design. The evolution of adhesion methods for wall-climbing robots (based on friction, magnetic forces, air pressure, electrostatic adhesion, molecular forces, rheological properties of fluids and their combinations) and their locomotion principles (wheeled, tracked, walking, sliding framed and hybrid) is studied. Wall-climbing robots are classified according to the applications, adhesion methods and locomotion mechanisms. The advantages and disadvantages of various adhesion methods and locomotion mechanisms are analyzed in terms of mobility, noiselessness, autonomy and energy efficiency. Focus is placed on the physical and technical aspects of the adhesion methods and the possibility of combining adhesion and locomotion methods