Evolving Self-Organizing Behaviors for a Swarm-Bot
Autonomous Robots
Robotics and Autonomous Systems
Efficient evaluation functions for evolving coordination
Evolutionary Computation
A Multi-robot Surveillance System Simulated in Gazebo
IWINAC '07 Proceedings of the 2nd international work-conference on Nature Inspired Problem-Solving Methods in Knowledge Engineering: Interplay Between Natural and Artificial Computation, Part II
Genetic team composition and level of selection in the evolution of cooperation
IEEE Transactions on Evolutionary Computation
Is situated evolution an alternative for classical evolution?
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Distributed coordination algorithms for mobile robot swarms: new directions and challenges
IWDC'05 Proceedings of the 7th international conference on Distributed Computing
Coordinated multi-robot exploration
IEEE Transactions on Robotics
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Self-organizing without a central controller in order to achieve collaboration towards an objective is one the main challenges in the design and operation of multi-robot systems. It is of great interest in the field to explore different approaches in order to achieve this end. Here we consider a distributed open-ended evolutionary approach called Asynchronous Situated Coevolution (ASiCO) and introduce a series of biologically inspired concepts in order to address the solution of complex multi-robot problems with several objectives and which require the coordination of robots within distinct groups carrying out heterogeneous tasks. Different elements are explored in this paper, including how to efficiently implement a co-evolutionary approach that can operate in real time using only local information perceived by the real robots as they act on the environment and how these experiments can be tweaked in order to produce the desired behaviors from the teams and individual robots.