In this paper, we study self-organized flocking in a swarm of mobile robots. We present Kobot, a mobile robot platform developed specifically for swarm robotic studies. We describe its infrared-based short range sensing system, capable of measuring the distance from obstacles and detecting kin robots, and a novel sensing system called the virtual heading system (VHS) which uses a digital compass and a wireless communication module for sensing the relative headings of neighboring robots.We propose a behavior based on heading alignment and proximal control that is capable of generating self-organized flocking in a swarm of Kobots. By self-organized flocking we mean that a swarm of mobile robots, initially connected via proximal sensing, is able to wander in an environment by moving as a coherent group in open space and to avoid obstacles as if it were a "super-organism". We propose a number of metrics to evaluate the quality of flocking. We use a default set of behavioral parameter values that can generate acceptable flocking in robots, and analyze the sensitivity of the flocking behavior against changes in each of the parameters using the metrics that were proposed. We show that the proposed behavior can generate flocking in a small group of physical robots in a closed arena as well as in a swarm of 1000 simulated robots in open space. We vary the three main characteristics of the VHS, namely: (1) the amount and nature of noise in the measurement This work is funded by TÜBİTAK (Turkish Scientific and Technical Council) through the "KARİYER: Kontrol Edilebilir Robot Ogulları" project with number 104E066. Additionally, Hande Çelikkanat acknowledges the partial support of the TÜBİTAK graduate student fellowship. Fatih Gökçe is currently enrolled in the Faculty Development Program (ÖYP) at Middle East Technical University on behalf of Süleyman Demirel University. 98 Swarm Intell (2008) 2: 97-120 of heading, (2) the number of VHS neighbors, and (3) the range of wireless communication.Our experiments show that the range of communication is the main factor that determines the maximum number of robots that can flock together and that the behavior is highly robust against the other two VHS characteristics. We conclude by discussing this result in the light of related theoretical studies in statistical physics.

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Detection and distance estimation of micro unmanned aerial vehicles (mUAVs) is crucial for (i) the detection of intruder mUAVs in protected environments; (ii) sense and avoid purposes on mUAVs or on other aerial vehicles and (iii) multi-mUAV control scenarios, such as environmental monitoring, surveillance and exploration. In this article, we evaluate vision algorithms as alternatives for detection and distance estimation of mUAVs, since other sensing modalities entail certain limitations on the environment or on the distance. For this purpose, we test Haar-like features, histogram of gradients (HOG) and local binary patterns (LBP) using cascades of boosted classifiers. Cascaded boosted classifiers allow fast processing by performing detection tests at multiple stages, where only candidates passing earlier simple stages are processed at the preceding more complex stages. We also integrate a distance estimation method with our system utilizing geometric cues with support vector regressors. We evaluated each method on indoor and outdoor videos that are collected in a systematic way and also on videos having motion blur. Our experiments show that, using boosted cascaded classifiers with LBP, near real-time detection and distance estimation of mUAVs are possible in about 60 ms indoors (1032×778 resolution) and 150 ms outdoors (1280×720 resolution) per frame, with a detection rate of 0.96 F-score. However, the cascaded classifiers using Haar-like features lead to better distance estimation since they can position the bounding boxes on mUAVs more accurately. On the other hand, our time analysis yields that the cascaded classifiers using HOG train and run faster than the other algorithms.

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a b s t r a c tIn this paper, we study how flocking affects the accuracy and speed of individuals in longrange ''migration''. Specifically, we extend a behavior that can generate self-organized flocking in a swarm of robots to follow a homing direction sensed through the magnetic field of the Earth and evaluate how the final points reached by the flock are scattered in space and how the speed of the flock is affected. We propose that four factors influence the performance of migration, in the proposed behavior, namely: (1) averaging through heading alignment behavior, (2) disturbances caused by proximal control behavior, (3) noise in sensing the homing direction, and (4) differences in the characteristics of the individuals. Systematic experiments are conducted to evaluate the effects of these factors using both physical and simulated robots. The results show that although flocking reduces the speed of an individual, it increases the accuracy of ''migration'' for flocks that are larger than a certain size.

ÖzetBu çalışmada iki boyutlu düzensiz şekillere sahip deri ayakkabı kalıplarının, yine düzensiz şekle sahip doğal deri materyali üzerine en az fire verecek şekilde dizilmesini gerçekleştiren bir yazılım geliştirilmiştir. Geliştirilen yazılım hem otomatik dizilim işlemini hem de el ile dizilim işlemini yapabilecek düzeydedir. Otomatik dizilim işlemi, literatürde son yıllarda popüler olan Gri Kurt Optimizasyonu algoritması kullanılarak gerçekleştirilmiştir. El ile dizilim işlemi ise, kullanıcıların bilgisayarın faresini kolaylıkla kullanabilecekleri şekilde tasarlanmıştır. Yazılım, Microsoft Visual Studio 2010 ortamında C# programlama dili ile geliştirilmiştir. Sonuçta hem el ile hem de otomatik olarak dizilim işlemlerinin başarılı bir şekilde yapılabildiği görülmüştür.Anahtar kelimeler: Gri kurt optimizasyonu, optimizasyon, iki boyutlu dizilim.

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