Swarm robotics: a review from the swarm engineering perspective <s> Coordinated motion <s> Grouping of animals is a natural phenomenon in which a number of animal individuals are involved in movement as forming a group. Examples are insect swarms and fish schools. In this article an attempt is made to describe the motion of grouping individuals kinematically as distinct from simple diffusion or random walk, to model the grouping on the basis of dynamics of animal motion, and to interpret the grouping from the standpoint of advection-diffusion processes. Also presented is dynamical modeling for the group size distribution as a result of amalgamation and splitting processes of groups. Examples of animal grouping are described in detail. They are insect swarms, zooplankton swarms, fish schools, bird flocks, and mammal herds. The presented mathematical models are compared with data of these animal groupings. <s> BIB001 </s> Swarm robotics: a review from the swarm engineering perspective <s> Coordinated motion <s> The aggregate motion of a flock of birds, a herd of land animals, or a school of fish is a beautiful and familiar part of the natural world. But this type of complex motion is rarely seen in computer animation. This paper explores an approach based on simulation as an alternative to scripting the paths of each bird individually. The simulated flock is an elaboration of a particle systems, with the simulated birds being the particles. The aggregate motion of the simulated flock is created by a distributed behavioral model much like that at work in a natural flock; the birds choose their own course. Each simulated bird is implemented as an independent actor that navigates according to its local perception of the dynamic environment, the laws of simulated physics that rule its motion, and a set of behaviors programmed into it by the "animator." The aggregate motion of the simulated flock is the result of the dense interaction of the relatively simple behaviors of the individual simulated birds. <s> BIB002 </s> Swarm robotics: a review from the swarm engineering perspective <s> Coordinated motion <s> Heterogeneous, "aggregated" patterns in the spatial distributions of individuals are almost universal across living organisms, from bacteria to higher vertebrates. Whereas specific features of aggregations are often visually striking to human eyes, a heuristic analysis based on human vision is usually not sufficient to answer fundamental questions about how and why organisms aggregate. What are the individual-level behavioral traits that give rise to these features? When qualitatively similar spatial patterns arise from purely physical mechanisms, are these patterns in organisms biologically significant, or are they simply epiphenomena that are likely characteristics of any set of interacting autonomous individuals? If specific features of spatial aggregations do confer advantages or disadvantages in the fitness of group members, how has evolution operated to shape individual behavior in balancing costs and benefits at the individual and group levels? Mathematical models of social behaviors such as schooling in fishes provide a promising avenue to address some of these questions. However, the literature on schooling models has lacked a common framework to objectively and quantitatively characterize relationships between individual-level behaviors and group-level patterns. In this paper, we briefly survey similarities and differences in behavioral algorithms and aggregation statistics among existing schooling models. We present preliminary results of our efforts to develop a modeling framework that synthesizes much of this previous work, and to identify relationships between behavioral parameters and group-level statistics. <s> BIB003
Description In coordinated motion, also known as flocking, robots move in formation similarly to schools of fish or flocks of birds. For a group of autonomous robots, coordinated motion can be very useful as a way to navigate in an environment with limited or no collisions between robots and as a way to improve the sensing abilities of the swarm ). Source of inspiration Coordinated motion behaviors are frequent in almost all social animals. In particular, flocking in group of birds or schooling in group of fish are impressive examples of self-organized coordinated motion BIB001 ). Through coordinated motion, animals gain several advantages, such as a higher survival rate, more precise navigation and reduced energy consumption BIB003 . Approaches In swarm robotics, coordinated motion behaviors are usually based on virtual physics-based design. Robots are supposed to keep a constant distance from one another and an uniform alignment while moving BIB002 . Coordinated motion behaviors have also been obtained via artificial evolution.