2009 Special Issue: Extending the Evolutionary Robotics approach to flying machines: An application to MAV teams

Authors:
Fabio Ruini;Angelo Cangelosi
Affiliations:
Adaptive Behaviour and Cognition Research Group, Centre for Robotics and Neural Systems, School of Computing & Mathematics, University of Plymouth, Drake Circus, Plymouth PL4 8AA, UK;Adaptive Behaviour and Cognition Research Group, Centre for Robotics and Neural Systems, School of Computing & Mathematics, University of Plymouth, Drake Circus, Plymouth PL4 8AA, UK
Venue:
Neural Networks
Year:
2009

Citing 13
Cited 0

Introduction to Multiagent Systems

Introduction to Multiagent Systems
Simulation of Unmanned Air Vehicle Flocking

TPCG '03 Proceedings of the Theory and Practice of Computer Graphics 2003
Evolving cooperative strategies for UAV teams

GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Coevolving Predator and Prey Robots: Do "Arms Races" Arise in Artificial Evolution?

Artificial Life
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
Distributed Coordination of Simulated Robots Based on Self-Organization

Artificial Life
Flying Fast and Low Among Obstacles: Methodology and Experiments

International Journal of Robotics Research
Evolved swarming without positioning information: an application in aerial communication relay

Autonomous Robots
Reverse-engineering of artificially evolved controllers for swarms of robots

CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Individual and cooperative tasks performed by autonomous MAV Teams driven by embodied neural network controllers

IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
A Multiagent Swarming System for Distributed Automatic Target Recognition Using Unmanned Aerial Vehicles

IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans

Quantified Score

Hi-index	0.00

Visualization

Abstract

The work presented in this article focuses on the use of embodied neural networks-developed through Evolutionary Robotics and Multi-Agent Systems methodologies-as autonomous distributed controllers for Micro-unmanned Aerial Vehicle (MAV) teams. The main aim of the research is to extend the range of domains that could be successfully tackled by the Evolutionary Robotics approach. The flying robots realm is an area that has not been yet thoroughly investigated by this discipline. This is due to the lack of an affordable and reliable robotic platform to use for carrying out experiments, and to the difficulty and the high computational load involved in experiments based upon a realistic software simulator for aircraft. We believe that the most recent improvements to the state of the art now permit the investigation of this domain. For demonstrating this point, two different evolutionary computer simulation models are presented in this article. The first model, which uses a simplified 2D test environment, has resulted in controllers evolved with the following capabilities: (1) navigation through unknown environments, (2) obstacle-avoidance, (3) tracking of a movable target, and (4) execution of cooperative and coordinated behaviors based on implicit communication strategies. In order to improve the robustness of these results and their potential use in real MAV teams, a more sophisticated 3D model is presented herein. The results obtained so far using the two models demonstrate the feasibility of the chosen approach for further research on the design of autonomous controllers for MAVs.