In nature, it is possible to observe a cooperative behaviour in all animals, since, according to Charles Darwin’s theory, every being, from ants to human beings, form groups in which most individuals work for the common good. However, although study of dozens of social species has been done for a century, details of how and why cooperation evolved remain to be worked out. Actually, cooperative behaviour has been studied from different points of view. For instance evolutionary biologists and animal behaviour researchers look for the genetic basis and molecular drivers of this kind of behaviours, as well as the physiological, environmental, and behavioural impetus for sociality; while neuroscientists discover key correlations between brain chemicals and social strategies. From a more mathematical point of view, economics have developed a modelling approach, based on game theory, to quantify cooperation and predict behavioural outcomes under different circumstances. Although game theory has helped to reveal an apparently innate desire for fairness, developed models are still imperfect. Furthermore, social insect behaviour, from a biological point of view, might be emulated by a micro-robot colony and, in that way, analysis of a tremendous amount of insect trajectories and manual event counting is replaced by tracking several miniature robots on a desktop table.
Swarm robotics is a new approach that emerged on the field of artificial swarm intelligence, as well as the biological studies of insects (i.e. ants and other fields in nature) which coordinate their actions to accomplish tasks that are beyond the capabilities of a single individual. In particular, swarm robotics is focused on the coordination of decentralised, self-organised multi-robot systems in order to describe such a collective behaviour as a consequence of local interactions with one another and with their environment.
Research in swarm robotics involves from robot design to their controlling behaviours, by including tracking techniques for systematically studying swarm-behaviour. Moreover, swarm robotic-based techniques can be used in a number of applications. This is, for instance, the case of the Particle Swarm Optimization (PSO) which is a direct search method, based on swarm concepts, that models and predicts social behaviour in the presence of objectives.
In this case, the swarm under study is typically modelled by particles in multidimensional space that have two essential reasoning capabilities: their memory of their own best position and the knowledge of the global or their neighbourhood’s best, such that swarm members communicate good positions to each other and adjust their own position and velocity based on those good positions in order to obtain the best problem solution.
Different challenges have to be solved in the field of swarm robotics. This book is focused on real practical applications by analyzing how individual robotic agents should behave in a robotic swarm in order to achieve a specific goal such as target localization or path planning.
In this context, the first paper, by Hereford and Siebold, concentrates on looking for a target in a room. They describe, on the one hand, the way a PSO algorithm, based on bird flocking, may be embedded into a robot swarm; and, on the other, the implementation of a four-step trophallactic behaviour of social insects in a robotic platform by making sensor measurements instead of exchanging information when two or more particles are in contact. Different software and hardware tests were developed to evaluate both search strategy performances. Another issue which may be solved by PSO methods is the robotic cell problem, where each integrating machine could be identified as a member of a swarm. In this context, Kamalabadi et al. present a hybrid PSO algorithm to find a schedule robot movement to minimize cycle time when multiple-type parts three-machine robotic cells are considered. Its performance has been compared with three well-known metaheuristic algorithms: Genetic Algorithm (GA), Basic Algorithm (PSOI) and Constriction Algorithm (PSO-II), by succeeding in the most problems, especially for large-sized ones.
The next two papers have focused on the problem of path planning for mobile robots. Firstly, Curkovic et al. introduce a way to solve the navigation problem for a robot in a workspace containing differently shaped and distributed by means of a simplification of Honey Bees Mating Algorithm. Moreover, a plan optimization technique that results in a minimization of the required time or the travelled distance is proposed. Again, method performance is successfully evaluated with respect to the Genetic Algorithm. Secondly, Xue et al. apply PSO-type control for real-time path planning on a typical swarm of wheeled mobile robots in an unstructured environment. Furthermore, an overview of a system modelling at both individual and swarm levels as well as a fusion-framework is presented. Their study was tested through virtual signal generators and simulations about swarm-component measuring and fusing.
Another application of the PSO techniques is the design of an infinite impulse response (IIR) digital filter of robot force/position sensors. Zhang proposes an IIR filter design from the knowledge of the structure of a filter and master of an intelligent optimization algorithm. The PSO algorithm is then used to optimize parameter values. Newly, simulation is used to validate the developed technique.
Finally, it is essential to systematically study and test swarm-behaviour by analyzing what each swarm member is doing as well as where and when it acts. For that purpose, Martínez and del Pobil has developed a visual application that robustly identifies and tracks all robotic swarm members. Different situations and visual systems were studied to achieve that goal. Experimental results on a real system are also presented.
This book has only provided a partial picture of the field of swarm robotics by focusing on practical applications. The global assessment of the contributions contained in this book is reasonably positive since they highlighted that it is necessary to adapt and remodel biological strategies to cope with the added complexity and problems that arise when robot individuals are considered.