2,385 research outputs found
Modeling and Mathematical Analysis of Swarms of Microscopic Robots
The biologically-inspired swarm paradigm is being used to design
self-organizing systems of locally interacting artificial agents. A major
difficulty in designing swarms with desired characteristics is understanding
the causal relation between individual agent and collective behaviors.
Mathematical analysis of swarm dynamics can address this difficulty to gain
insight into system design. This paper proposes a framework for mathematical
modeling of swarms of microscopic robots that may one day be useful in medical
applications. While such devices do not yet exist, the modeling approach can be
helpful in identifying various design trade-offs for the robots and be a useful
guide for their eventual fabrication. Specifically, we examine microscopic
robots that reside in a fluid, for example, a bloodstream, and are able to
detect and respond to different chemicals. We present the general mathematical
model of a scenario in which robots locate a chemical source. We solve the
scenario in one-dimension and show how results can be used to evaluate certain
design decisions.Comment: 2005 IEEE Swarm Intelligence Symposium, Pasadena, CA June 200
Modular Self-Reconfigurable Robot Systems
The field of modular self-reconfigurable robotic systems addresses the design, fabrication, motion planning, and control of autonomous kinematic machines with variable morphology. Modular self-reconfigurable systems have the promise of making significant technological advances to the field of robotics in general. Their promise of high versatility, high value, and high robustness may lead to a radical change in automation. Currently, a number of researchers have been addressing many of the challenges. While some progress has been made, it is clear that many challenges still exist. By illustrating several of the outstanding issues as grand challenges that have been collaboratively written by a large number of researchers in this field, this article has shown several of the key directions for the future of this growing fiel
Applications of Biological Cell Models in Robotics
In this paper I present some of the most representative biological models
applied to robotics. In particular, this work represents a survey of some
models inspired, or making use of concepts, by gene regulatory networks (GRNs):
these networks describe the complex interactions that affect gene expression
and, consequently, cell behaviour
Towards Swarm Calculus: Urn Models of Collective Decisions and Universal Properties of Swarm Performance
Methods of general applicability are searched for in swarm intelligence with
the aim of gaining new insights about natural swarms and to develop design
methodologies for artificial swarms. An ideal solution could be a `swarm
calculus' that allows to calculate key features of swarms such as expected
swarm performance and robustness based on only a few parameters. To work
towards this ideal, one needs to find methods and models with high degrees of
generality. In this paper, we report two models that might be examples of
exceptional generality. First, an abstract model is presented that describes
swarm performance depending on swarm density based on the dichotomy between
cooperation and interference. Typical swarm experiments are given as examples
to show how the model fits to several different results. Second, we give an
abstract model of collective decision making that is inspired by urn models.
The effects of positive feedback probability, that is increasing over time in a
decision making system, are understood by the help of a parameter that controls
the feedback based on the swarm's current consensus. Several applicable
methods, such as the description as Markov process, calculation of splitting
probabilities, mean first passage times, and measurements of positive feedback,
are discussed and applications to artificial and natural swarms are reported
Multiobjective optimization of electromagnetic structures based on self-organizing migration
Práce se zabĂ˝vá popisem novĂ©ho stochastickĂ©ho vĂcekriteriálnĂho optimalizaÄŤnĂho algoritmu MOSOMA (Multiobjective Self-Organizing Migrating Algorithm). Je zde ukázáno, Ĺľe algoritmus je schopen Ĺ™ešit nejrĹŻznÄ›jšà typy optimalizaÄŤnĂch Ăşloh (s jakĂ˝mkoli poÄŤtem kritĂ©riĂ, s i bez omezujĂcĂch podmĂnek, se spojitĂ˝m i diskrĂ©tnĂm stavovĂ˝m prostorem). VĂ˝sledky algoritmu jsou srovnány s dalšĂmi běžnÄ› pouĹľĂvanĂ˝mi metodami pro vĂcekriteriálnĂ optimalizaci na velkĂ© sadÄ› testovacĂch Ăşloh. Uvedli jsme novou techniku pro vĂ˝poÄŤet metriky rozprostĹ™enĂ (spread) zaloĹľenĂ© na hledánĂ minimálnĂ kostry grafu (Minimum Spanning Tree) pro problĂ©my majĂcĂ vĂce neĹľ dvÄ› kritĂ©ria. DoporuÄŤenĂ© hodnoty pro parametry Ĺ™ĂdĂcĂ bÄ›h algoritmu byly urÄŤeny na základÄ› vĂ˝sledkĹŻ jejich citlivostnĂ analĂ˝zy. Algoritmus MOSOMA je dále ĂşspěšnÄ› pouĹľit pro Ĺ™ešenĂ rĹŻznĂ˝ch návrhovĂ˝ch Ăşloh z oblasti elektromagnetismu (návrh Yagi-Uda antĂ©ny a dielektrickĂ˝ch filtrĹŻ, adaptivnĂ Ĺ™ĂzenĂ vyzaĹ™ovanĂ©ho svazku v ÄŤasovĂ© oblasti…).This thesis describes a novel stochastic multi-objective optimization algorithm called MOSOMA (Multi-Objective Self-Organizing Migrating Algorithm). It is shown that MOSOMA is able to solve various types of multi-objective optimization problems (with any number of objectives, unconstrained or constrained problems, with continuous or discrete decision space). The efficiency of MOSOMA is compared with other commonly used optimization techniques on a large suite of test problems. The new procedure based on finding of minimum spanning tree for computing the spread metric for problems with more than two objectives is proposed. Recommended values of parameters controlling the run of MOSOMA are derived according to their sensitivity analysis. The ability of MOSOMA to solve real-life problems from electromagnetics is shown in a few examples (Yagi-Uda and dielectric filters design, adaptive beam forming in time domain…).
The impact of agent density on scalability in collective systems : noise-induced versus majority-based bistability
In this paper, we show that non-uniform distributions in swarms of agents have an impact on the scalability of collective decision-making. In particular, we highlight the relevance of noise-induced bistability in very sparse swarm systems and the failure of these systems to scale. Our work is based on three decision models. In the first model, each agent can change its decision after being recruited by a nearby agent. The second model captures the dynamics of dense swarms controlled by the majority rule (i.e., agents switch their opinion to comply with that of the majority of their neighbors). The third model combines the first two, with the aim of studying the role of non-uniform swarm density in the performance of collective decision-making. Based on the three models, we formulate a set of requirements for convergence and scalability in collective decision-making
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