Artificial Life
First Results in the Coordination of Heterogeneous Robots for Large-Scale Assembly
ISER '00 Experimental Robotics VII
Traderbots: a new paradigm for robust and efficient multirobot coordination in dynamic environments
Traderbots: a new paradigm for robust and efficient multirobot coordination in dynamic environments
An Architecture for Behavior-Based Reinforcement Learning
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Artificial life approach for continuous optimisation of non-stationary dynamical systems
Integrated Computer-Aided Engineering
Auction-based multi-robot task allocation in COMSTAR
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Efficient evaluation functions for evolving coordination
Evolutionary Computation
Bio-Inspired Artificial Intelligence: Theories, Methods, and Technologies
Bio-Inspired Artificial Intelligence: Theories, Methods, and Technologies
KES '09 Proceedings of the 13th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems: Part I
Multirobot systems: a classification focused on coordination
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Task-driven species in evolutionary robotic teams
IWINAC'11 Proceedings of the 4th international conference on Interplay between natural and artificial computation - Volume Part I
Quantitative and qualitative coordination for multi-robot systems
AICI'12 Proceedings of the 4th international conference on Artificial Intelligence and Computational Intelligence
Information Sciences: an International Journal
Distributed embodied evolution for collective tasks: parametric analysis of a canonical algorithm
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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This work deals with the application of multi-robot systems to real tasks and, in particular, their coordination through interaction based control systems. Within this field, the practical solutions that have been implemented in real robots mainly use strongly coordinated architectures and assignment strategies because of reliability and fault tolerance issues when addressing problems in reality. Emergent approaches have also been proposed with limited success, basically due to the unpredictability of the behaviors obtained. Here, an emergent approach, called r-ASiCo, is presented containing a procedure to produce predictable solutions and thus avoiding the typical problems associated with these techniques. The r-ASico algorithm is the real time version of the Asynchronous Situated Co-evolution algorithm (ASiCo), which exploits natural open-ended evolution to generate emergent complex collective behaviors and deals with systems made up of a huge number of elements and nonlinear interactions. The goal of r-ASiCo is to design the global behavior desired for the robot team as a collective entity and allow the emergence of behaviors through the interaction of the team members using social rules they learn to implement. To this end, r-ASiCo manages a series of features that are inherent to natural evolution based methods such as energy exchange and mating selection procedures, together with a technique to guide the evolution towards a design objective, the principled evaluation function selection procedure. Hence, this paper presents the components and operation of r-ASiCo and illustrates its application through a collective cleaning task example. It was implemented using 8 e-puck robots in two different real scenarios and its results complemented with those of a 30 e-puck case. The results show the capabilities of r-ASiCo to create a self-organized and adaptive multi-robot system configuration that is tolerant to environmental changes and to failures within the robot team.