Cover image for Evolutionary Swarm Robotics Evolving Self-Organising Behaviours in Groups of Autonomous Robots
Evolutionary Swarm Robotics Evolving Self-Organising Behaviours in Groups of Autonomous Robots
Title:
Evolutionary Swarm Robotics Evolving Self-Organising Behaviours in Groups of Autonomous Robots
Author:
Trianni, Vito. author.
ISBN:
9783540776123
Personal Author:
Physical Description:
XVI, 192 p. online resource.
Series:
Studies in Computational Intelligence, 108
Contents:
The Evolution of Self-Organization -- Embodied Cognitive Science -- Multi-Robot Systems, Swarm Robotics and Self-Organisation -- Evolutionary Robotics for Self-Organising Behaviours -- Experiments with Simulated and Real Robots -- A Self-Organising Artefact: The Swarm-bot -- Coordinated Motion -- Hole Avoidance -- Self-Organising Synchronisation -- Future Directions -- Emergent Collective Decisions through Self-Organisation -- Decision-Making Mechanisms through the Perception of Time -- From Solitary to Collective Behaviours: Decision Making and Cooperation -- Conclusions.
Abstract:
In this book the use of ER techniques for the design of self-organising group behaviours, for both simulated and real robots is introduced. This research has a twofold value. From an engineering perspective, an automatic methodology for synthesising complex behaviours in a robotic system is described. ER techniques should be used in order to obtain robust and efficient group behaviours based on self-organisation. From a more theoretical point of view, the second important contribution brought forth by the author's experiments concerns the understanding of the basic principles underlying self-organising behaviours and collective intelligence. In this experimental work, the evolved behaviours are analysed in order to uncover the mechanisms that have led to a certain organisation. In summary, this book tries to mediate between two apparently opposed perspectives: engineering and cognitive science. The experiments presented and the results obtained contribute to the assessment of ER not only as a design tool, but also as a methodology for modelling and understanding intelligent adaptive behaviours.
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