International Journal of Computer Applications (0975 – 8887) Volume 126 – No.2, September 2015 31 An Overview of Swarm Robotics: Swarm Intelligence Applied to Multi-robotics Belkacem Khaldi, Foudil Cherif Department of Computer Science. LESIA Laboratory, University of Biskra, Algeria. ABSTRACT As an emergent research area by which swarm intelligence is applied to multi-robot systems; swarm robotics (a very particular and peculiar sub-area of collective robotics) studies how to coordinate large groups of relatively simple robots through the use of local rules. It focuses on studying the design of large amount of relatively simple robots, their physical bodies and their controlling behaviors. Since its introduction in 2000, several successful experimentations had been realized, and till now more projects are under investigations. This paper seeks to give an overview of this domain research; for the aim to orientate the readers, especially those who are newly coming to this research field. General Terms Swarm robotics, swarm intelligence, multi-robot systems. Keywords Swarm robotics applications, swarm robotics simulators, swarm robotics problems classification. 1. INTRODUCTION Inspired from the complex behaviors observed in natural swarm systems (e.g., social insects and order living animals), swarm intelligence (SI) is a new field that aims to build fully distributed de-centralized systems in which overall system functionality emerges from the interaction of individual agents with each other and with their environment. As a result to try applying the insight gained from this domain research into multi-robotics, an emerging research area called swarm robotics (SR) has been issued. SR is the study of how to coordinate large groups of relatively simple robots through the use of local rules. It focuses on studying the design of large amount of relatively simple robots, their physical bodies and their controlling behaviors [1]. SR is closely related to the idea of SI and it shares its interest in self-organized decentralized systems. Hence, it offers several advantages for robotic applications such as scalability, and robustness due to redundancy [2]. This paper seeks to give an overview of SR for the aim to orientate the readers, especially those who are newly coming to this research field, the paper highlights the grand lines of the different main focuses areas in this domain research. In the upcoming sections, we introduce in section 2 the SI as an emergent research domain inspired from nature swarms, followed by overviewing Multi-robot systems (MRS) in section 3. In section 4 we introduce SR as an application of SI technics to MRS. The remaining sections (section 5 to section 8) are for more details about SR, these sections involve: definition of SR and its features, its potential applications in real world, the classification of the problems being focused on, and finally exploring some real successful projects and simulations being realized in real experimentation. 2. SWARM INTELLIGENCE Who among us haven’t been amazed by the individually simple but collectively complex behavior exhibited by natural grouping systems including social insects such as: ant’ colonies, termites, bees, wasps …etc., and high order living animals such as: flocks of birds, fish schooling, and packs of wolves …etc.? Inspired by the robustness, scalability, and distributed self-organization principles observed in these amazing natural collective complex behaviors emerged from individual simple local interactions rules, an attempt to apply the insight gained through this research to artificial systems (e.g., massively distributed computer systems and robotics) has given rise to a new research topic called (SI) [3]. This increasing domain research that is firstly introduced in the context of cellular robotic systems by Beni and Wang [4] is considered as a sub-field of artificial intelligence based around on the study of collective behaviors in de-centralized, self- organized systems [5]. Although there is no a specific definition for swarm intelligence, we adopt heir the one denoted by Dorigo & Birattari [6]: ‘The discipline that deals with natural and artificial systems composed of many individuals that coordinate using de- centralized control and self-organization. In particular, the discipline focuses on the collective behaviors that result from the local interactions of the individuals with each other and with their environment’. So, a swarm intelligence system consists typically of a population of relatively simple agents (relatively homogenous or there are a few types of them [6]) interacting only locally with themselves and with their environment, without having a global knowledge about their own state and of the state of the world. Moreover, the overall observed behavior is emerged in response to the local environment and to local interactions between the agents that follow often very simple rules [7]. Natural swarm based theories have been applied to solve analogous engineering problems in several domains engineering from combinatorial optimization to rooting communication network as well as robotics applications, etc. (for a recent comprehensive review, readers can refer to [8]). The most well-known swarm based algorithms are: Ant Colony Optimization Algorithms (ACO), Particle Swarm Optimization Algorithms (PSO), Artificial Fish Swarm Algorithm (AFSA) and Bee based Algorithms. The ACO algorithm is inspired from the foraging behavior of ant colonies in finding shortest paths from their nests to food sources. The source of inspiration of PSO based algorithms comes especially from the behavior observed in bird flocking or fish schooling when they are moving together for long