CNLS '89 Proceedings of the ninth annual international conference of the Center for Nonlinear Studies on Self-organizing, Collective, and Cooperative Phenomena in Natural and Artificial Computing Networks on Emergent computation
Neural networks and intellect: using model-based concepts
Neural networks and intellect: using model-based concepts
An Introduction to Genetic Algorithms
An Introduction to Genetic Algorithms
Evolving cooperative strategies for UAV teams
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Emergence of Cooperation: State of the Art
Artificial Life
Neuro-control of unmanned underwater vehicles
International Journal of Systems Science
Parallel simulation of UAV swarm scenarios
WSC '04 Proceedings of the 36th conference on Winter simulation
Flying Fast and Low Among Obstacles: Methodology and Experiments
International Journal of Robotics Research
Training feedforward neural networks using genetic algorithms
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
Integrating Language and Cognition: A Cognitive Robotics Approach
IEEE Computational Intelligence Magazine
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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The work presented here focuses on the use of embodied neural network controllers for MAV (Micro-unmanned Aerial Vehicles) teams. The computer model we have built aims to demonstrate how autonomous controllers for groups of flying robots can be successfully developed through simulations based on multi-agent systems and evolutionary robotics methodologies. We first introduce the field of autonomous flying robots, reviewing the most relevant contributes on thi s research field and highlighting the elements of novelty contained in our approach. We then describe the simulation model we have elaborated and the results obtained in different experimental scenarios. In all experiments, MAV teams made by four agents have to navigate autonomously through an unknown environment, reach a certain target and finally neutralize it through a self-detonation. The different setups comprise an environment with various obstacles (skyscrapers) and a fixed target, one with a moving target, and one where the target (fixed or moving) needs to be attacked cooperatively in order to be neutralized. The results obtained show how the evolved controllers are able to perform the various tasks with an accuracy level between 72% and 94% when the target has to be approached individually. The performance slightly decreases only when the target is both able to move and can only be neutralized through a coordinated operation. The paper ends with a discussion on the possible applications of autonomous MAV teams to real life scenarios.