Издательство InTech, 2010, -110 pp.
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 faiess, 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.
Bio-inspired search strategies for robot swarms.
A New Hybrid Particle Swarm Optimization Algorithm to the Cyclic Multiple-Part Type Three-Machine Robotic Cell Problem.
Comparison of Swarm Optimization and Genetic Algorithm for Mobile Robot Navigation.
Key Aspects of PSO-Type Swarm Robotic Search: Signals Fusion and Path Planning.
Optimization Design Method of IIR Digital Filters for Robot Force Position Sensors.
Visual Analysis of Robot and Animal Colonies.
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 faiess, 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.
Bio-inspired search strategies for robot swarms.
A New Hybrid Particle Swarm Optimization Algorithm to the Cyclic Multiple-Part Type Three-Machine Robotic Cell Problem.
Comparison of Swarm Optimization and Genetic Algorithm for Mobile Robot Navigation.
Key Aspects of PSO-Type Swarm Robotic Search: Signals Fusion and Path Planning.
Optimization Design Method of IIR Digital Filters for Robot Force Position Sensors.
Visual Analysis of Robot and Animal Colonies.