1,166 research outputs found
A Decentralized Mobile Computing Network for Multi-Robot Systems Operations
Collective animal behaviors are paradigmatic examples of fully decentralized
operations involving complex collective computations such as collective turns
in flocks of birds or collective harvesting by ants. These systems offer a
unique source of inspiration for the development of fault-tolerant and
self-healing multi-robot systems capable of operating in dynamic environments.
Specifically, swarm robotics emerged and is significantly growing on these
premises. However, to date, most swarm robotics systems reported in the
literature involve basic computational tasks---averages and other algebraic
operations. In this paper, we introduce a novel Collective computing framework
based on the swarming paradigm, which exhibits the key innate features of
swarms: robustness, scalability and flexibility. Unlike Edge computing, the
proposed Collective computing framework is truly decentralized and does not
require user intervention or additional servers to sustain its operations. This
Collective computing framework is applied to the complex task of collective
mapping, in which multiple robots aim at cooperatively map a large area. Our
results confirm the effectiveness of the cooperative strategy, its robustness
to the loss of multiple units, as well as its scalability. Furthermore, the
topology of the interconnecting network is found to greatly influence the
performance of the collective action.Comment: Accepted for Publication in Proc. 9th IEEE Annual Ubiquitous
Computing, Electronics & Mobile Communication Conferenc
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
Development of a miniature robot for swarm robotic application
Biological swarm is a fascinating behavior of nature that has been successfully applied to solve human problem especially for robotics application. The high economical cost and large area required to execute swarm robotics scenarios does not permit experimentation with real robot. Model and simulation of the mass number of these robots are extremely complex and often inaccurate. This paper describes the design decision and presents the development of an autonomous miniature mobile-robot (AMiR) for swarm robotics research and education. The large number of robot in these systems allows designing an individual AMiR unit with simple perception and mobile abilities. Hence a large number of robots can be easily and economically feasible to be replicated. AMiR has been designed as a complete platform with supporting software development tools for robotics education and researches in the Department of Computer and Communication Systems Engineering, UPM. The experimental results demonstrate the feasibility of using this robot to implement swarm robotic applications
Cooperative object transport with a swarm of e-puck robots: robustness and scalability of evolved collective strategies
Cooperative object transport in distributed multi-robot systems requires the coordination and synchronisation of pushing/pulling forces by a group of autonomous robots in order to transport items that cannot be transported by a single agent. The results of this study show that fairly robust and scalable collective transport strategies can be generated by robots equipped with a relatively simple sensory apparatus (i.e. no force sensors and no devices for direct communication). In the experiments described in this paper, homogeneous groups of physical e-puck robots are required to coordinate and synchronise their actions in order to transport a heavy rectangular cuboid object as far as possible from its starting position to an arbitrary direction. The robots are controlled by dynamic neural networks synthesised using evolutionary computation techniques. The best evolved controller demonstrates an effective group transport strategy that is robust to variability in the physical characteristics of the object (i.e. object mass and size of the longest object’s side) and scalable to different group sizes. To run these experiments, we designed, built, and mounted on the robots a new sensor that returns the agents’ displacement on a 2D plane. The study shows that the feedback generated by the robots’ sensors relative to the object’s movement is sufficient to allow the robots to coordinate their efforts and to sustain the transports for an extended period of time. By extensively analysing successful behavioural strategies, we illustrate the nature of the operational mechanisms underpinning the coordination and synchronisation of actions during group transport
Cooperative Control for Localization of Mobile Sensor Networks
In this paper, we consider the problem of cooperatively control a formation of networked mobile robots/vehicles to optimize the relative and absolute localization performance in 1D and 2D space. A framework for active perception is presented utilizing a graphical representation of sensory information obtained from the robot network. Performance measures are proposed that capture the estimate quality of team localization. We show that these measures directly depend on the sensing graph and shape of the formation. This dependence motivates implementation of a gradient based control scheme to adapt the formation geometry in order to optimize team localization performance. This approach is illustrated through application to a cooperative target localization problem involving a small robot team. Simulation results are presented using experimentally validated noise models
Cooperative object transport with a swarm of e-puck robots: robustness and scalability of evolved collective strategies
Cooperative object transport in distributed multi-robot systems requires the coordination and synchronisation of pushing/pulling forces by a group of autonomous robots in order to transport items that cannot be transported by a single agent. The results of this study show that fairly robust and scalable collective transport strategies can be generated by robots equipped with a relatively simple sensory apparatus (i.e. no force sensors and no devices for direct communication). In the experiments described in this paper, homogeneous groups of physical e-puck robots are required to coordinate and synchronise their actions in order to transport a heavy rectangular cuboid object as far as possible from its starting position to an arbitrary direction. The robots are controlled by dynamic neural networks synthesised using evolutionary computation techniques. The best evolved controller demonstrates an effective group transport strategy that is robust to variability in the physical characteristics of the object (i.e. object mass and size of the longest object’s side) and scalable to different group sizes. To run these experiments, we designed, built, and mounted on the robots a new sensor that returns the agents’ displacement on a 2D plane. The study shows that the feedback generated by the robots’ sensors relative to the object’s movement is sufficient to allow the robots to coordinate their efforts and to sustain the transports for an extended period of time. By extensively analysing successful behavioural strategies, we illustrate the nature of the operational mechanisms underpinning the coordination and synchronisation of actions during group transport
Development of IR-based short-range communication techniques for swarm robot applications
This paper proposes several designs for a reliable infra-red based communication techniques for swarm robotic applications. The communication system was deployed on an autonomous miniature mobile robot (AMiR), a swarm robotic platform developed earlier. In swarm applications, all participating robots must be able to communicate and share data. Hence a suitable communication medium and a reliable technique are required. This work uses infrared radiation for transmission of swarm robots messages. Infrared transmission methods such as amplitude and frequency modulations will be presented along with experimental results. Finally the effects of the modulation techniques and other parameters on collective behavior of swarm robots will be analyzed
Cooperative Avoidance Control-based Interval Fuzzy Kohonen Networks Algorithm in Simple Swarm Robots
A novel technique to control swarm robot’s movement is presented and analyzed in this paper. It allows a group of robots to move as a unique entity performing the following function such as obstacle avoidance at group level. The control strategy enhances the mobile robot’s performance whereby their forthcoming decisions are impacted by its previous experiences during the navigation apart from the current range inputs. Interval Fuzzy-Kohonen Network (IFKN) algorithm is utilized in this strategy. By employing a small number of rules, the IFKN algorithms can be adapted to swarms reactive control. The control strategy provides much faster response compare to Fuzzy Kohonen Network (FKN) algorithm to expected events. The effectiveness of the proposed technique is also demonstrated in a series of practical test on our experimental by using five low cost robots with limited sensor abilities and low computational effort on each single robot in the swarm. The results show that swarm robots based on proposed technique have the ability to perform cooperative behavior, produces minimum collision and capable to navigate around square shapes obstacles
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Multi-robot system control using artificial immune system
For the successful deployment of task-achieving multi-robot systems (MRS), the interactions must be coordinated among the robots within the MRS and between the robots and the task environment. There have been a number of impressive experimentally demonstrated coordinated MRS. However it is still of a premature stage for real world applications. This dissertation presents an MRS control scheme using Artificial Immune Systems (AIS). This methodology is firmly grounded in the biological sciences and provides robust performance for the intertwined entities involved in any task-achieving MRS. Based on its formal foundation, it provides a platform to characterize interesting relationships and dependencies among MRS task requirements, individual robot control, capabilities, and the resulting task performance. The work presented in this dissertation is a first of its kind wherein the principles of AIS have been used to model and organize the group behavior of the MRS. This has been presented in the form of a novel algorithm. In addition to the above, generic environments for computer simulation and real experiment have been realized to demonstrate the working of an MRS. These could potentially be used as a test bed to implement other algorithms onto the MRS. The experiment in this research is a bomb disposal task which involves a team of three heterogeneous robots with different sensors and actuators. And the algorithm has been tested practically through computer simulations.Mechanical Engineerin
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