Relevant bibliographies by topics / Bio-inspired swarms

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Academic literature on the topic 'Bio-inspired swarms'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Bio-inspired swarms"

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Lewis, Michael, Michael Goodrich, Katia Sycara, and Mark Steinberg. "Human Factors issues for Interaction with Bio-Inspired Swarms." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 56, no. 1 (September 2012): 61–64. http://dx.doi.org/10.1177/1071181312561033.

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Petráček, Pavel, Viktor Walter, Tomáš Báča, and Martin Saska. "Bio-inspired compact swarms of unmanned aerial vehicles without communication and external localization." Bioinspiration & Biomimetics 16, no. 2 (December 18, 2020): 026009. http://dx.doi.org/10.1088/1748-3190/abc6b3.

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Thalamala, Ravi Chandran, A. Venkata Swamy Reddy, and B. Janet. "A Novel Bio-Inspired Algorithm Based on Social Spiders for Improving Performance and Efficiency of Data Clustering." Journal of Intelligent Systems 29, no. 1 (February 14, 2018): 311–26. http://dx.doi.org/10.1515/jisys-2017-0178.

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Abstract Since the last decade, the collective intelligent behavior of groups of animals, birds or insects have attracted the attention of researchers. Swarm intelligence is the branch of artificial intelligence that deals with the implementation of intelligent systems by taking inspiration from the collective behavior of social insects and other societies of animals. Many meta-heuristic algorithms based on aggregative conduct of swarms through complex interactions with no supervision have been used to solve complex optimization problems. Data clustering organizes data into groups called clusters, such that each cluster has similar data. It also produces clusters that could be disjoint. Accuracy and efficiency are the important measures in data clustering. Several recent studies describe bio-inspired systems as information processing systems capable of some cognitive ability. However, existing popular bio-inspired algorithms for data clustering ignored good balance between exploration and exploitation for producing better clustering results. In this article, we propose a bio-inspired algorithm, namely social spider optimization (SSO), for clustering that maintains a good balance between exploration and exploitation using female and male spiders, respectively. We compare results of the proposed algorithm SSO with K means and other nature-inspired algorithms such as particle swarm optimization (PSO), ant colony optimization (ACO) and improved bee colony optimization (IBCO). We find it to be more robust as it produces better clustering results. Although SSO solves the problem of getting stuck in the local optimum, it needs to be modified for locating the best solution in the proximity of the generated global solution. Hence, we hybridize SSO with K means, which produces good results in local searches. We compare proposed hybrid algorithms SSO+K means (SSOKC), integrated SSOKC (ISSOKC), and interleaved SSOKC (ILSSOKC) with K means+PSO (KPSO), K means+genetic algorithm (KGA), K means+artificial bee colony (KABC) and interleaved K means+IBCO (IKIBCO) and find better clustering results. We use sum of intra-cluster distances (SICD), average cosine similarity, accuracy and inter-cluster distance to measure and validate the performance and efficiency of the proposed clustering techniques.
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Fatès, Nazim, and Nikolaos Vlassopoulos. "A Robust Scheme for Aggregating Quasi-Blind Robots in an Active Environment." International Journal of Swarm Intelligence Research 3, no. 3 (July 2012): 66–80. http://dx.doi.org/10.4018/jsir.2012070105.

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The question of how to aggregate autonomous agents with limited abilities in the absence of centralized coordination is known as the Decentralized Gathering Problem. The authors present a bio-inspired aggregation scheme that solves this problem and study a first application of this scheme to a small team of robots. The robots (Alice and Khepera III) obey simple rules and have only a rudimentary perception of their environment. The collective behavior is based on stigmergic principles and uses an active environment to relay the communications between robots. This results in an aggregation process that shows good properties of robustness and that can in principle be extended to swarms of robots.
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Dogan, Rezarta Islamaj, Yolanda Gil, Haym Hirsh, Narayanan C. Krishnan, Michael Lewis, Cetin Mericli, Parisa Rashidi, et al. "Reports on the 2012 AAAI Fall Symposium Series." AI Magazine 34, no. 1 (December 17, 2012): 93. http://dx.doi.org/10.1609/aimag.v34i1.2457.

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The Association for the Advancement of Artificial Intelligence was pleased to present the 2012 Fall Symposium Series, held Friday through Sunday, November 2–4, at the Westin Arlington Gateway in Arlington, Virginia. The titles of the eight symposia were as follows: AI for Gerontechnology (FS-12-01), Artificial Intelligence of Humor (FS-12-02), Discovery Informatics: The Role of AI Research in Innovating Scientific Processes (FS-12-03), Human Control of Bio-Inspired Swarms (FS-12-04), Information Retrieval and Knowledge Discovery in Biomedical Text (FS-12-05), Machine Aggregation of Human Judgment (FS-12-06), Robots Learning Interactively from Human Teachers (FS-12-07), and Social Networks and Social Contagion (FS-12-08). The highlights of each symposium are presented in this report.
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Meza Álvarez, Joaquín Javier, Juan Manuel Cueva Lovelle, and Helbert Eduardo Espitia. "REVISIÓN SOBRE ALGORITMOS DE OPTIMIZACIÓN MULTI-OBJETIVO GENÉTICOS Y BASADOS EN ENJAMBRES DE PARTÍCULAS." Redes de Ingeniería 6, no. 2 (March 9, 2016): 54. http://dx.doi.org/10.14483/udistrital.jour.redes.2015.2.a06.

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El enfoque evolutivo como también el comportamiento social han mostrado ser una muy buena alternativa en los problemas de optimización donde se presentan varios objetivos a optimizar. De la misma forma, existen todavía diferentes vias para el desarrollo de este tipo de algoritmos. Con el fin de tener un buen panorama sobre las posibles mejoras que se pueden lograr en los algoritmos de optimización bio-inspirados multi-objetivo es necesario establecer un buen referente de los diferentes enfoques y desarrollos que se han realizado hasta el momento.En este documento se revisan los algoritmos de optimización multi-objetivo más recientes tanto genéticos como basados en enjambres de partículas. Se realiza una revisión critica con el fin de establecer las características más relevantes de cada enfoque y de esta forma identificar las diferentes alternativas que se tienen para el desarrollo de un algoritmo de optimización multi-objetivo bio-inspirado.Review about genetic multi-objective optimization algorithms and based in particle swarmABSTRACTThe evolutionary approach as social behavior have proven to be a very good alternative in optimization problems where several targets have to be optimized. Likewise, there are still different ways to develop such algorithms. In order to have a good view on possible improvements that can be achieved in the optimization algorithms bio-inspired multi-objective it is necessary to establish a good reference of different approaches and developments that have taken place so far. In this paper the algorithms of multi-objective optimization newest based on both genetic and swarms of particles are reviewed. Critical review in order to establish the most relevant characteristics of each approach and thus identify the different alternatives have to develop an optimization algorithm multi-purpose bio-inspired design is performed.Keywords: evolutionary computation, evolutionary multi-objective optimization.
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Dong, Xiaoguang, and Metin Sitti. "Controlling two-dimensional collective formation and cooperative behavior of magnetic microrobot swarms." International Journal of Robotics Research 39, no. 5 (January 28, 2020): 617–38. http://dx.doi.org/10.1177/0278364920903107.

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Magnetically actuated mobile microrobots can access distant, enclosed, and small spaces, such as inside microfluidic channels and the human body, making them appealing for minimally invasive tasks. Despite their simplicity when scaling down, creating collective microrobots that can work closely and cooperatively, as well as reconfigure their formations for different tasks, would significantly enhance their capabilities such as manipulation of objects. However, a challenge of realizing such cooperative magnetic microrobots is to program and reconfigure their formations and collective motions with under-actuated control signals. This article presents a method of controlling 2D static and time-varying formations among collective self-repelling ferromagnetic microrobots (100 [Formula: see text]m to 350 [Formula: see text]m in diameter, up to 260 in number) by spatially and temporally programming an external magnetic potential energy distribution at the air–water interface or on solid surfaces. A general design method is introduced to program external magnetic potential energy using ferromagnets. A predictive model of the collective system is also presented to predict the formation and guide the design procedure. With the proposed method, versatile complex static formations are experimentally demonstrated and the programmability and scaling effects of formations are analyzed. We also demonstrate the collective mobility of these magnetic microrobots by controlling them to exhibit bio-inspired collective behaviors such as aggregation, directional motion with arbitrary swarm headings, and rotational swarming motion. Finally, the functions of the produced microrobotic swarm are demonstrated by controlling them to navigate through cluttered environments and complete reconfigurable cooperative manipulation tasks.
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Albani, Dario, Wolfgang Hönig, Daniele Nardi, Nora Ayanian, and Vito Trianni. "Hierarchical Task Assignment and Path Finding with Limited Communication for Robot Swarms." Applied Sciences 11, no. 7 (March 31, 2021): 3115. http://dx.doi.org/10.3390/app11073115.

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Complex service robotics scenarios entail unpredictable task appearance both in space and time. This requires robots to continuously relocate and imposes a trade-off between motion costs and efficiency in task execution. In such scenarios, multi-robot systems and even swarms of robots can be exploited to service different areas in parallel. An efficient deployment needs to continuously determine the best allocation according to the actual service needs, while also taking relocation costs into account when such allocation must be modified. For large scale problems, centrally predicting optimal allocations and movement paths for each robot quickly becomes infeasible. Instead, decentralized solutions are needed that allow the robotic system to self-organize and adaptively respond to the task demands. In this paper, we propose a distributed and asynchronous approach to simultaneous task assignment and path planning for robot swarms, which combines a bio-inspired collective decision-making process for the allocation of robots to areas to be serviced, and a search-based path planning approach for the actual routing of robots towards tasks to be executed. Task allocation exploits a hierarchical representation of the workspace, supporting the robot deployment to the areas that mostly require service. We investigate four realistic environments of increasing complexity, where each task requires a robot to reach a location and work for a specific amount of time. The proposed approach improves over two different baseline algorithms in specific settings with statistical significance, while showing consistently good results overall. Moreover, the proposed solution is robust to limited communication and robot failures.
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Aihara, Ikkyu, Daichi Kominami, Yasuharu Hirano, and Masayuki Murata. "Mathematical modelling and application of frog choruses as an autonomous distributed communication system." Royal Society Open Science 6, no. 1 (January 2019): 181117. http://dx.doi.org/10.1098/rsos.181117.

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Interactions using various sensory cues produce sophisticated behaviour in animal swarms, e.g. the foraging behaviour of ants and the flocking of birds and fish. Here, we investigate the behavioural mechanisms of frog choruses from the viewpoints of mathematical modelling and its application. Empirical data on male Japanese tree frogs demonstrate that (1) neighbouring male frogs avoid call overlaps with each other over a short time scale and (2) they collectively switch between the calling state and the silent state over a long time scale. To reproduce these features, we propose a mathematical model in which separate dynamical models spontaneously switch due to a stochastic process depending on the internal dynamics of respective frogs and also the interactions among the frogs. Next, the mathematical model is applied to the control of a wireless sensor network in which multiple sensor nodes send a data packet towards their neighbours so as to deliver the packet to a gateway node by multi-hop communication. Numerical simulation demonstrates that (1) neighbouring nodes can avoid a packet collision over a short time scale by alternating the timing of data transmission and (2) all the nodes collectively switch their states over a long time scale, establishing high network connectivity while reducing network power consumption. Consequently, this study highlights the unique dynamics of frog choruses over multiple time scales and also provides a novel bio-inspired technology that is applicable to the control of a wireless sensor network.
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Sadiku, Matthew N. O., Mahamadou Tembely, and Sarhan M. Musa. "Swarm Intelligence: A Primer." International Journal of Advanced Research in Computer Science and Software Engineering 8, no. 5 (June 2, 2018): 100. http://dx.doi.org/10.23956/ijarcsse.v8i5.681.

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Swarm intelligence is the emergent collective intelligence of groups of simple agents. It belongs to the emerging field of bio-inspired soft computing. It is inspired from the biological entities such as birds, fish, ants, wasps, termites, and bees. Bio-inspired computation is a field of study that is closely related to artificial intelligence. This paper provides a brief introduction to swarm intelligence.
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Dissertations / Theses on the topic "Bio-inspired swarms"

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Kerman, Sean C. "Methods and Metrics for Human Interaction with Bio-Inspired Robot Swarms." BYU ScholarsArchive, 2013. https://scholarsarchive.byu.edu/etd/3870.

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In this thesis we propose methods and metrics for human interaction with bio-inspired robot teams. We refine the concept of a stakeholder and demonstrate how a human can use stakeholders to lead a swarm as well as switch the swarm between different collective behaviors. We extend the human interaction metrics of interaction time and interaction effort presented in [1] to swarm systems and introduce the concept of interaction effort. These metrics allow us to understand how well the system performs under human influence. We employ systems theory to estimate these metrics, which is useful because this can be done without performing user studies.
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Brown, Daniel Sundquist. "Toward Scalable Human Interaction with Bio-Inspired Robot Teams." BYU ScholarsArchive, 2013. https://scholarsarchive.byu.edu/etd/3776.

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Bio-inspired swarming behaviors provide an effective decentralized way of coordinating robot teams. However, as robot swarms increase in size, bandwidth and time constraints limit the number of agents a human can communicate with and control. To facilitate scalable human interaction with large robot swarms it is desirable to monitor and influence the collective behavior of the entire swarm through limited interactions with a small subset of agents. However, it is also desirable to avoid situations where a small number of agent failures can adversely affect the collective behavior of the swarm. We present a bio-inspired model of swarming that exhibits distinct collective behaviors and affords limited human interaction to estimate and influence these collective behaviors. Using a simple naive Bayes classifier, we show that the global behavior of a swarm can be detected with high accuracy by sampling local information from a small number of agents. We also show that adding a bio-inspired form of quorum sensing to a swarm increases the scalability of human-swarm interactions and also provides an adjustable threshold on the swarm's vulnerability to agent failures.
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Berg, Jannik, and Camilla Haukenes Karud. "Swarm intelligence in bio-inspired robotics." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for datateknikk og informasjonsvitenskap, 2011. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-13684.

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In this report, we have explored swarm intelligence through a box-pushing taskwith physical robots called e-pucks. Research on social insects has been presentedtogether with dierent ways of controlling autonomous robots, where combiningthis knowledge has been essential in our quest to make a biological plausible antretrieving system.Inspired by ants and behavior-based robotics, we have created the system CRABS.It is based on Brooks' subsumption architecture to control six dierent behaviors,from a xed input-output scheme. The system is designed to easily handle addingor removal of behavior layers. Behavior modules can also be used separately andported to other software or hardware platforms.During this project we came across several hardware and software challenges in-vestigating cooperative behavior. With the use of the simulation tool Webots, wewere able to determine e-pucks' capabilities, and through this knowledge able todesign and construct an articial food source. This operated as the box-item in thebox-pushing task.Based on two types of sensors and two actuators (wheels), we had a strategy toaccomplish the box-pushing task following the biological principles of social insects.The guidelines of the ant retrieving model made CRABS a self-organized systemthat given three or more e-pucks, will always succeed in retrieving the box back tothe wall. The most remarkable view on this accomplishment is that is done throughthe use of only stigmergy and positive/negative feedback.One of the things we've experienced throughout this thesis is that hardware is a morework demanding and inconsistent platform than your usual software simulation.Everything is not given, and although Webots provided helpful shortcuts, a lot oftime and hard work was put down in order to get the system up and running. Withthat being said, we are pleased that we took the hardware rout and were able totest and validate our system on physical robots.
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Ramezan, Shirazi Ataollah. "Bio-inspired self-organizing swarm robotics." Thesis, University of Surrey, 2017. http://epubs.surrey.ac.uk/844948/.

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Swarm robotics is the collaboration of a large number of robots to accomplish a set of specified tasks. It has great potential for the generation of self-organizing adaptive systems, where simple behaviours at agent level result in complex behaviours at swarm level. These systems promise to be robust, flexible, and scalable, and have many innovative applications in the future. Elimination of a central controller and instead relying on local awareness and distributed decision making are the main distinguishing characteristics of such, systems which make them different from classical engineering and necessitate a different design methodology. The challenges to design a control mechanism for self-organizing swarm robotic systems mainly come from the difficulty of mapping between the macroscopic behaviours of a swarm and the microscopic behaviours of its individual agents, and also decision making based on local awareness. Nature presents the best examples of self-organising collective systems. They can be divided into two categories, animal collective behaviours, and cellular organs. Although studying animal collective behaviours paves the way for understanding the principles of self-organizing collective systems, cellular organs show more complex behaviours and structures. The goal of this research is to adapt cellular morphogenesis mechanisms for collective behaviours in a swarm of minimalist robots. The trade of between the size of a swarm and the complexity of involved robots necessitates using simpler and cheaper robots. In addition, miniaturization of robots for future micro-robotic applications require to minimize the number of on-board devices in robots. In this thesis, we focus on developing minimalist algorithms inspired by biological morphogenesis for collective swarm behaviours, including collective flocking, target following, and target enclosure. The proposed algorithms are applicable to highly restricted robots without global positioning, directional sensing, motion feedback, and long-range communication devices. At first, I show how morphogens can retain the integrity and original shape of a swarm of robots without directional sensing, while the swarm moves and interacts with the environment. Then, a coordinated motion strategy is presented in order to preserve connectivity of a real swarm of minimalist robots following a target in their environment. Finally, a new approach is presented for target enclosure with a control over the shape of aggregation around the target. In this approach, a morphogen gradient produced by a target reacts with a second one diffusing through the edge of aggregation in order to spot weak points of the aggregation. The last two algorithms implemented in a real swarm of Kilobots.
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Zuniga, Virgilio. "Bio-inspired optimization algorithms for smart antennas." Thesis, University of Edinburgh, 2011. http://hdl.handle.net/1842/5766.

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This thesis studies the effectiveness of bio-inspired optimization algorithms in controlling adaptive antenna arrays. Smart antennas are able to automatically extract the desired signal from interferer signals and external noise. The angular pattern depends on the number of antenna elements, their geometrical arrangement, and their relative amplitude and phases. In the present work different antenna geometries are tested and compared when their array weights are optimized by different techniques. First, the Genetic Algorithm and Particle Swarm Optimization algorithms are used to find the best set of phases between antenna elements to obtain a desired antenna pattern. This pattern must meet several restraints, for example: Maximizing the power of the main lobe at a desired direction while keeping nulls towards interferers. A series of experiments show that the PSO achieves better and more consistent radiation patterns than the GA in terms of the total area of the antenna pattern. A second set of experiments use the Signal-to-Interference-plus-Noise-Ratio as the fitness function of optimization algorithms to find the array weights that configure a rectangular array. The results suggest an advantage in performance by reducing the number of iterations taken by the PSO, thus lowering the computational cost. During the development of this thesis, it was found that the initial states and particular parameters of the optimization algorithms affected their overall outcome. The third part of this work deals with the meta-optimization of these parameters to achieve the best results independently from particular initial parameters. Four algorithms were studied: Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing and Hill Climb. It was found that the meta-optimization algorithms Local Unimodal Sampling and Pattern Search performed better to set the initial parameters and obtain the best performance of the bio-inspired methods studied.
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Bhandare, Ashray Sadashiv. "Bio-inspired Algorithms for Evolving the Architecture of Convolutional Neural Networks." University of Toledo / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1513273210921513.

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Cambier, Nicolas. "Bio-inspired collective exploration and cultural organisation." Thesis, Compiègne, 2019. http://www.theses.fr/2019COMP2511.

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Les systèmes autonomes sont récemment devenus une solution efficace pour des applications telles que l'exploration et la surveillance d'environnements. Dans ces situations, l'utilisation de plusieurs robots pourrait améliorer l'efficacité des solutions proposées, bien que cela nécessite des stratégies d'organisation qui soient à la fois robuste, flexible et adaptables à la taille de la flotte de robots. En robotique en essaim, ces qualités sont assurées par la décentralisation, la redondance (plusieurs/tous les robots effectuent la même tâche), des interactions locales et des règles simples. Les interactions et communications locales sont une composante clef de la robotique en essaim. Jusqu'ici, la communication n'a été utilisée que pour des tâches relativement simples, tels que signaler les préférences ou l'état d'un robot. Cependant, la communication peut être bien plus riche et similaire aux langages humains. Dans ces conditions, elle permettrait aux essaims de robots de gérer de nouvelles situations qui ne seraient pas prévues par leurs concepteurs. De riches communications sont donc nécessaires pour obtenir des essaims entièrement autonomes, en particulier dans des environnements inconnus. Dans cette thèse, nous proposons une approche pour faire émerger des communications riches dans des essaims de robots en utilisant les jeux de langages comme protocole de communication et l'agrégation probabiliste comme cas d'étude. L'agrégation probabiliste est un prérequis pour de nombreuses tâches en robotique en essaim mais elle est aussi extrêmement sensible aux conditions expérimentales. Elle requiert donc un réglage spécifique de ses paramètres pour chaque nouvelle condition, y compris les changements d'échelle ou de densité. Avec notre approche, nous avons observé que l'exécution simultanée du jeu de nommage et de l'agrégation mène, dans certaines conditions, à un nouveau comportement d'agglomération en plusieurs groupes, chacun avec son propre nom, qui est contrôlable via les paramètres de l'agrégation. En poussant ces interactions plus loin, nous démontrons que les dynamiques sociales du jeu de nommage peuvent sélectionner des paramètres d'agrégation efficaces. Cette sélection culturelle crée donc des contrôleurs résilients, qui évoluent en-ligne en fonction du contexte courant
Automatically-controlled artificial systems have recently been used in numerous settings including environmental monitoring and explorations, with great success. In such cases, the use of multiple robots could increase efficiency, although we should ensure that their communication and organisation strategies are robust, exible, and scalable. These qualities can be ensured through decentralisation, redundancy (many/all robots perform the same task), local interaction, and simplistic rules, as is the case in swarm robotics. One of the key components of swarm robotics is local interaction or communication. The later has, so far, only been used for relatively simple tasks such as signalling a robot's preference or state. However, communication has more potential because the emergence of meaning, as it exists in human language, could allow robots swarms to tackle novel situations in ways that may not be a priori obvious to the experimenter. This is a necessary feature for having swarms that are fully autonomous, especially in unknown environments. In this thesis, we propose a framework for the emergence of meaningful communications in swarm robotics using language games as a communication protocol and probabilistic aggregation as a case study. Probabilistic aggregation can be a prerequisite to many other swarm behaviours but, unfortunately, it is extremely sensitive to experimental conditions, and thus requires specific parameter tuning for any setting such as population size or density.With our framework, we show that the concurrent execution of the naming game and of probabilistic aggregation leads, in certain conditions, to a new clustering and labelling behaviour that is controllable via the parameters of the aggregation controller. Pushing this interplay forward, we demonstrate that the social dynamics of the naming game can select efficient aggregation parameters through environmental pressure. This creates resilient controllers as the aggregation behaviour is dynamically evolved online according to the current environmental setting
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Mendonça, Ricardo André Martins. "A learning approach to swarm-based path detection and tracking." Master's thesis, Faculdade de Ciências e Tecnologia, 2012. http://hdl.handle.net/10362/8226.

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Dissertação para obtenção do Grau de Mestre em Engenharia Electrotécnica e de Computadores
This dissertation presents a set of top-down modulation mechanisms for the modulation of the swarm-based visual saliency computation process proposed by Santana et al. (2010) in context of path detection and tracking. In the original visual saliency computation process, two swarms of agents sensitive to bottom-up conspicuity information interact via pheromone-like signals so as to converge on the most likely location of the path being sought. The behaviours ruling the agents’motion are composed of a set of perception-action rules that embed top-down knowledge about the path’s overall layout. This reduces ambiguity in the face of distractors. However, distractors with a shape similar to the one of the path being sought can still misguide the system. To mitigate this issue, this dissertation proposes the use of a contrast model to modulate the conspicuity computation and the use of an appearance model to modulate the pheromone deployment. Given the heterogeneity of the paths, these models are learnt online. Using in a modulation context and not in a direct image processing, the complexity of these models can be reduced without hampering robustness. The result is a system computationally parsimonious with a work frequency of 20 Hz. Experimental results obtained from a data set encompassing 39 diverse videos show the ability of the proposed model to localise the path in 98.67 % of the 29789 evaluated frames.
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Medetov, Seytkamal. "Bio-inspired Approaches for Informatio Dissemination in Ad hon Networks." Thesis, Belfort-Montbéliard, 2014. http://www.theses.fr/2014BELF0253/document.

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La dissémination d’information dans les réseaux VANET est une opération fondamentale pour la sécurité routière. Il est dès lors nécessaire de concevoir et mettre en oeuvre des algorithmes efficaces et adaptatifs pour la dissémination d’informations sélectives et pertinentes.Dans ce travail, des approches Bio-inspirées sont proposées, à partir des comportements auto-organisés des essaims comme les colonies de fourmis et d’abeilles. Ces approches visent à fournir à chaque véhicule des informations en provenance de son environnement et alerter les conducteurs. Dans la première approche, le système de communication direct et indirect des fourmis est utilisé. Les fourmis partagent les informations sur les sources de nourriture avec des membres de la colonie en sécrétant la phéromone sur leurs chemins. La deuxième approche est inspirée par le système de communication des abeilles. Les abeilles partagent les informations à propos des sources de nourriture avec les autres membres de la ruche par des messages spécifiques, selon l’importance de ces sources.Une nouvelle mesure de "pertinence" associée aux messages est définie, par analogie à la sécrétion des phéromones des fourmis et au niveau de l’intensité des messages pour les abeilles, pour disséminer des informations de sécurité dans une zone géographique. Les simulations sont effectuées en utilisant le simulateur NS2 pour mesurer l’efficacité des approches proposées sous différentes conditions, en particulier en termes de densités et vitesses des véhicules
Information dissemination in Vehicular Ad hoc Networks (VANETs) is a fundamental operation to increase the safety awareness among vehicles on roads. Thus, the design and implementation of efficient and scalable algorithms for relevant information dissemination constitutes a major issue that should be tackled.In this work, bio-inspired information dissemination approaches are proposed, that use self-organization principles of swarms such as Ant and Honey Bee colonies. These approaches are targeted to provide each vehicle with the required information about its surrounding and assist drivers to be aware of undesirable road conditions. In the first approach, Ant’s direct and indirect communication systems are used. Ants share information about food findings with colony members by throwing pheromone on the returning to the nest. The second, an RSU-based approach is inspired by the Bee communication system. Bees share profitable food sources with hive-mates in their hive by specific messages.A “relevance” value associated to the emergency messages is defined as an analogue to pheromone throwing in Ant colony, and as an analogue to profitability level in Bee colony, to disseminate safety information within a geographical area. Simulations are conducted using NS2 network simulator and relevant metrics are evaluated under different node speeds and network densities to show the effectiveness of the proposed approaches
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Enyedy, Albert J. "Robotic Construction Using Intelligent Scaffolding." Digital WPI, 2020. https://digitalcommons.wpi.edu/etd-theses/1356.

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Construction is a complex activity that requires the cooperation of multiple workers. Every year, construction activities cause injuries and casualties. To make construction safer, new solutions could be provided by robotics. Robots could be employed not only to replace human workers, but also to make construction in harsh environments safe and cost-effective, paving the way for enhanced underwater infrastructure, deeper underground mining, and planetary colonization. In this thesis, we focus on the topic of collective construction, which involves the cooperation of multiple robots, by presenting a collective robot construction method of our own. Collective construction can be a more viable option than employing individual, complex robots, by potentially allowing the effective realization of large structures, while offering resilience through redundancy, analogous to insect colonies. Our approach offers a novel solution in the design trade-off between choosing the number of robots involved vs. the complexity of the robots involved. On the one hand, capable and complex robots are expensive, limiting the cost effectiveness of realizing large swarms which provide redundancy and increase the system’s resilience to faults. On the other hand, simple and inexpensive robots can be manufactured in large numbers and offer high redundancy, at the cost of limited individual capa bilities and lower performance. We use two types of robots: intelligent scaffolding and worker robots. The intelligent scaffolding acts as regular scaffolding, allowing the worker robots to navigate the structure they assemble, while also guiding and monitoring the construction of the structure. The worker robots move and connect scaffolding and building material while only knowing the local commands necessary to complete their task. This approach is loosely inspired by termite mounds, in which termites use the process of stigmergy in which they mark construction pellets with pheromones to affect the progress of construction, while navigating the struc ture that they build. Thanks to intelligent scaffolding, construction robots have a simple design that allows minimalist onboard computation and communication equipment. In this thesis, we produced a minimum viable prototype demonstrating this concept. Intelligent scaffolding is realized through smart blocks that can be laid and connected to each other. The smart blocks are capable of simple computation and communication once laid. The construction robot uses local navigation methods by line-following across the scaffolding and building blocks of the system. The blocks and construction robot both have a modular design, simplifying the process of manufacturing and repairs while maintaining a low cost. The robot and blocks use magnets to increase the margin of error during block manipulation and allow for the assembly and removal of scaffolding as well as its reuse between build sites. To communicate with the robot, the intelligent scaffolding blocks send local IR signals, similar to TV remote signals, when the robot is on top of them, minimizing the risk of global interference and keeping the system portable. To monitor the connectivity of the system throughout the life cycle of the structure, electrical connections run through each of the blocks, which indicate the status of the structure and can be used to diagnose the location of breaks in the structure for maintenance.
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Books on the topic "Bio-inspired swarms"

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Swarm Intelligence and Bio-Inspired Computation. Elsevier, 2013. http://dx.doi.org/10.1016/c2012-0-02754-8.

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Yang, Xin-She, Mehmet Karamanoglu, Zhihua Cui, Amir Hossein Gandomi, and Renbin Xiao. Swarm Intelligence and Bio-Inspired Computation: Theory and Applications. Elsevier, 2013.

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Book chapters on the topic "Bio-inspired swarms"

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Schmickl, Thomas. "How to Engineer Robotic Organisms and Swarms?" In Bio-Inspired Self-Organizing Robotic Systems, 25–52. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20760-0_2.

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Nordmann, Brian. "Bio-Inspired Computing, Information Swarms, and the Problem of Data Fusion." In NATO Science for Peace and Security Series A: Chemistry and Biology, 35–44. Dordrecht: Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-94-007-2488-4_3.

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Priyadarshi, Neeraj, Farooque Azam, Sandeep Singh Solanki, Amarjeet Kumar Sharma, Akash Kumar Bhoi, and Dhafer Almakhles. "A Bio-Inspired Chicken Swarm Optimization-Based Fuel Cell System for Electric Vehicle Applications." In Bio-inspired Neurocomputing, 297–308. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5495-7_16.

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Xu, Xiaohua, Zhoujin Pan, Ping He, and Ling Chen. "Constrained Clustering via Swarm Intelligence." In Bio-Inspired Computing and Applications, 404–9. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-24553-4_53.

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Tekchandani, Prakash, and Aditya Trivedi. "Clock Drift Management Using Particle Swarm Optimization." In Bio-Inspired Computing and Applications, 386–93. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-24553-4_51.

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Huang, Kai, and Yong quan Zhou. "A Novel Chaos Glowworm Swarm Optimization Algorithm for Optimization Functions." In Bio-Inspired Computing and Applications, 426–34. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-24553-4_56.

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Han, Fei, Hai-Fen Yao, and Qing-Hua Ling. "An Improved Extreme Learning Machine Based on Particle Swarm Optimization." In Bio-Inspired Computing and Applications, 699–704. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-24553-4_92.

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Li, Yangyang, Zhenghan Chen, Yang Wang, and Licheng Jiao. "Quantum-Behaved Particle Swarm Optimization Using MapReduce." In Bio-inspired Computing – Theories and Applications, 173–78. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-3614-9_22.

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Misra, Rajesh, and Kumar Sankar Ray. "Particle Swarm Optimization Based on Random Walk." In Computational Vision and Bio-Inspired Computing, 147–63. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6862-0_13.

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Liang, J. J., Bo Yang Qu, Song Tao Ma, and Ponnuthurai Nagaratnam Suganthan. "Memetic Fitness Euclidean-Distance Particle Swarm Optimization for Multi-modal Optimization." In Bio-Inspired Computing and Applications, 378–85. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-24553-4_50.

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Conference papers on the topic "Bio-inspired swarms"

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Ali, Atif, Yasir Khan Jadoon, Malik Usman Dilawar, Muhammad Qasim, Shujah Ur Rehman, and Muhammad Usama Nazir. "Robotics: Biological Hypercomputation and Bio-Inspired Swarms Intelligence." In 2021 1st International Conference on Artificial Intelligence and Data Analytics (CAIDA). IEEE, 2021. http://dx.doi.org/10.1109/caida51941.2021.9425245.

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Sutantyo, Donny, and Paul Levi. "A bio-inspired TDMA scheduling algorithm for underwater robotic swarms." In 2013 IEEE International Conference on Robotics and Biomimetics (ROBIO). IEEE, 2013. http://dx.doi.org/10.1109/robio.2013.6739612.

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Pinciroli, Carlo, Adam Lee-Brown, and Giovanni Beltrame. "A Tuple Space for Data Sharing in Robot Swarms." In 9th EAI International Conference on Bio-inspired Information and Communications Technologies (formerly BIONETICS). ACM, 2016. http://dx.doi.org/10.4108/eai.3-12-2015.2262503.

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Dongsik Chang, Wencen Wu, Donald R. Webster, Marc J. Weissburg, and Fumin Zhang. "A bio-inspired plume tracking algorithm for mobile sensing swarms in turbulent flow." In 2013 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2013. http://dx.doi.org/10.1109/icra.2013.6630683.

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Goodrich, Michael, Sean Kerman, Brian Pendleton, and P. B. Sujit. "What Types of Interactions do Bio-Inspired Robot Swarms and Flocks Afford a Human?" In Robotics: Science and Systems 2012. Robotics: Science and Systems Foundation, 2012. http://dx.doi.org/10.15607/rss.2012.viii.014.

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Prasetyo, Judhi, Giulia De Masi, Raina Zakir, Muhanad Alkilabi, Elio Tuci, and Eliseo Ferrante. "A bio-inspired spatial defence strategy for collective decision making in self-organized swarms." In GECCO '21: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3449639.3459356.

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Kiwon Yeom and Ji-Hyung Park. "Artificial morphogenesis for arbitrary shape generation of swarms of multi agents." In 2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA). IEEE, 2010. http://dx.doi.org/10.1109/bicta.2010.5645177.

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Wareham, Todd. "Exploruing Algorithmic Options for the Efficient Design and Reconfiguration of Reactive Robot Swarms." In 9th EAI International Conference on Bio-inspired Information and Communications Technologies (formerly BIONETICS). ACM, 2016. http://dx.doi.org/10.4108/eai.3-12-2015.2262395.

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Yeom, Kiwon. "Notice of Violation of IEEE Publication Principles: Bio-inspired automatic shape formation for swarms of self-reconfigurable modular robots." In 2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA). IEEE, 2010. http://dx.doi.org/10.1109/bicta.2010.5645171.

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Lu, Qi, Antonio D. Griego, G. Matthew Fricke, and Melanie E. Moses. "Comparing Physical and Simulated Performance of a Deterministic and a Bio-inspired Stochastic Foraging Strategy for Robot Swarms." In 2019 International Conference on Robotics and Automation (ICRA). IEEE, 2019. http://dx.doi.org/10.1109/icra.2019.8794240.

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