Swarm

This paper proposes a strategy for the coordination of a swarm of robots in an unknown environment. The basic idea is to achieve the autonomous movement of the group from an initial region to a target region avoiding obstacles. We use a behavior model similar to bacterial Quorum Sensing (QS) as a technique for the coordination of robots. This behavior has been described as a key element in the interaction between bacteria, and we use it as a basic tool for local interaction, both between the robot and between the robot and the environment. The movement of the swarm of robots, or multi-agent robotic system, is shown as an emerging behavior resulting from the interaction of agents (in the context of artificial intelligence) from basic rules of behavior. The proposed strategy was successfully evaluated by simulation on a set of robots.

Location Sharing Motivations within the Framework of Uses and Gratifications Approach: A Qualitative Study on Foursquare and Swarm Today, the average number of daily check-ins in Foursquare and Swarm exceeds 9 million, even though all social networks have the ability of location awareness. This study aims to reveal check-in motivations of location-based social network users. In this study, a qualitative research is conducted within the framework of Uses and Gratification Approach and in-depth interviews are used for data collection. The participants consist of 10 users using both Foursquare and Swarm. The users motivations identified in the research are as follow: the functionality of LBSNs in the choice of place; the chance to save their place histories; the presentation of self via shared places ; socializing with new people and existing friends; establishing a sense of belonging and ownership ties with places through mayor-ship; and the game elements in LBSNs.

SUMMARYWe present a review of recent activities in swarm robotic research, and analyse existing literature in the field to determine how to get closer to a practical swarm robotic system for real world applications. We begin with a discussion of the importance of swarm robotics by illustrating the wide applicability of robot swarms in various tasks. Then a brief overview of various robotic devices that can be incorporated into swarm robotic systems is presented. We identify and describe the challenges that should be resolved when designing swarm robotic systems for real world applications. Finally, we provide a summary of a series of issues that should be addressed to overcome these challenges, and propose directions for future swarm robotic research based on our extensive analysis of the reviewed literature.

The efficacy of sweep nets and a CDC white light-suction trap for the sampling of
Culicoides
species  (Diptera:  Ceratopogonidae)  were  compared  on  a  livestock  farm  in  Northern  Spain 
during  the  Summer  of  2013.  A  total  of  6,082  specimens  representing  26  species  were 
collected  with  sweep  nets  in  4  areas  at  different  heights  (ground  level,  1.5  m,  and  3  m), 
and  8,463  specimens  representing  28  species  with  a  single  white  light  trap.  Eight  species  - 
Culicoides brunnicans, Culicoides punctatus, Culicoides obsoletus/Culicoides scoticus, Culicoides
lupicaris, Culcoides picturatus, Culicoides achrayi
, and
Culicoides simulator
- were dominant and
accounted for 97.4% and 97.2% of the total specimens collected with both methods, sweep
nets, and light traps, respectively. The sex ratios with sweep netting and light trapping were
strongly female biased (78.4% and 97.1%, respectively). Nulliparous and parous females were
predominantly captured with both methods. A high percentage (17%) of gravid females was,
however,  captured  on  manure  at  ground  level  while  sweeping.  Searches  for  male  swarms 
revealed  the  presence  of  several 
C.  punctatus
  swarms  consisting  of  26  to  196  males  and  3 
swarms of
C. obsoletus
that ranged from 1 to 12 males in size. This study suggested that both
methods are suitable and complementary tools for
Culicoides
sampling.

This paper proposes a new obstacle and collision avoidance control laws for a three-dimensional swarm of boids. The swarm exhibit collective emergent behaviors whilst avoiding the obstacles in the workspace. While flocking, animals group up in order to do various tasks and even a greater chance of evading predators. A generalized algorithms for attraction to the centroid, inter-individual swarm avoidance and obstacle avoidance is designed in this paper. We present a set of new continuous time-invariant velocity control laws is presented which is formulated via the Lyapunov-based control scheme. The control laws proposed in this paper also ensures practical stability of the system. The effectiveness of the proposed control laws is demonstrated via computer simulations.

An agent-based Keynesian laboratory is built with a twofold purpose; to further investigate the ideas of Keynes and check them for internal consistency, and secondly to investigate the stability of a Keynesian world and explore the scope for economic policy. The laboratory is developed in Swarm and has an exensive user interface to enable use by other researchers or students

Image edge detection mainly involves in identifying points in an image at which the image brightness changes sharply or more formally has discontinuities. The points at which image brightness changes sharply are typically organized into a set of curved line segments termed edges. Edges give important structural information about the images which would play a vital role in various fields of image processing and computer vision. In order to optimize the process of image edge detection and produce better edges for images, we have implemented edge detection using two swarm intelligence techniques. In this chapter we will study in detail how ant colony optimisation and particle swarm optimization techniques were utilised to identify edges of an image. I. INTRODUCTION The concept of edge detection mainly involves in identifying local variation in brightness levels in images. Image edge detection is mainly employed for image processing and computer vision. Computer vision helps in extracting useful information from the detected objects such as images, videos. In last two decades numerous techniques and variations have been developed. Despite them being carried out, main skeleton of these techniques remains similar. With varying application domains, most of these techniques have been modified to fit into various applications. The changes undergo mostly in perspective of encoding scheme, parameter tuning and search strategy. The edge detection task is to find the boundaries of image regions based on properties such as intensity and texture [3]. It is a critical low-level procedure of image processing because edges carry a lot of information. The edge detection process generally includes five steps: 1) Filtering: Filtering out noise from the image and improving the performance of the edge detector. 2) Enhancement: Emphasising pixels which have important change in local intensity. 3) Detection: Identifying the edges and thresholding. 4) Link: Linking the broken edges (such as hysteresis thresholding techniques). 5) Localisation: Locating the edge accurately and estimating the edge orientation (edge and orientation map). In the modern age computer vision concepts are useful for detecting checks through images and in automated driving cars. Edge detection helps in identifying the important structural object of the image, which thereby is used for identifying important information from image such as number, size, and relative location of objects in an image. If we compare black and white grayscale images the variation in colour for our eyes is very sudden but at pixel level, it is not very rapid. The edge detected is a constant change of pixel values when we travel from one bright level to another bright level in the space of pixels of an image. Usually, the technique for identifying variations in the pixel values to identify edge variation is calculated for each pixel value in image space which is not an optimistic solution for identifying an image. So, to optimize the solution for this scenario in this paper we will further discuss about implementing swarm intelligence for detecting edges. Swarm intelligence mainly involves the study of collective and individual behaviour of biological systems which help in improving efficiency for solving complex problems such as identifying shortest path for food source or identifying their enemies in nature. Even though all the insects are unsophisticated and unorganised they make wonders by adapting to their problems in environment and solving their daily life scenarios.

The efficacy of sweep nets and a CDC white light-suction trap for the sampling of Culicoides
species (Diptera: Ceratopogonidae) were compared on a livestock farm in Northern Spain
during the Summer of 2013. A total of 6,082 specimens representing 26 species were
collected with sweep nets in 4 areas at different heights (ground level, 1.5 m, and 3 m),
and 8,463 specimens representing 28 species with a single white light trap. Eight species -
Culicoides brunnicans, Culicoides punctatus, Culicoides obsoletus/Culicoides scoticus, Culicoides
lupicaris, Culcoides picturatus, Culicoides achrayi, and Culicoides simulator - were dominant and
accounted for 97.4% and 97.2% of the total specimens collected with both methods, sweep
nets, and light traps, respectively. The sex ratios with sweep netting and light trapping were
strongly female biased (78.4% and 97.1%, respectively). Nulliparous and parous females were
predominantly captured with both methods. A high percentage (17%) of gravid females was,
however, captured on manure at ground level while sweeping. Searches for male swarms
revealed the presence of several C. punctatus swarms consisting of 26 to 196 males and 3
swarms of C. obsoletus that ranged from 1 to 12 males in size. This study suggested that both
methods are suitable and complementary tools for Culicoides sampling.

The most common versions of particle swarm optimization (PSO) algorithms are rotationally variant. It has also been pointed out that PSO algorithms can concentrate particles along paths parallel to the coordinate axes. In this paper, the authors explicitly connect these two observations by showing that the rotational variance is related to the concentration along lines parallel to the coordinate axes. Based on this explicit connection, the authors create fitness functions that are easy or hard for PSO to solve, depending on the rotation of the function.

The term shared dataspace refers to the general class of programming languages in
which the principal means of communication among the concurrent components of programs is a common, content-addressable data structure called a dataspace. In the programming language and artificial intelligence communities, there is considerable interest in such languages, e.g., logic-based languages, production rule systems, and the Linda language. However, these languages have not been the subject of extensive program verification research. This paper specifies a proof system for a shared dataspace programming notation called Swarm—a programming logic similar in style to that of UNITY. The paper then uses the proof system to reason about a Swarm program to label the equal-intensity regions of a digital image.