This project has focused on the design of distributed autonomous controllers for collective behavior of Micro-unmanned Aerial Vehicles (MAVs). Two alternative approaches to this topic are introduced: one based upon the Evolutionary Robotics (ER) paradigm, the other one upon flocking principles. Three computer simulators have been developed in order to carry out the required experiments, all of them having their focus on the modeling of fixed- wing aircraft flight dynamics. The employment of fixed-wing aircraft rather than the omni-directional robots typically employed in collective robotics significantly increases the complexity of the challenges that an autonomous controller has to face. This is mostly due to the strict motion constraints associated with fixed-wing platforms that require a high degree of accuracy by the controller. Concerning the ER approach, the experimental setups elaborated have resulted in controllers evolved in simulation with the following capabilities: (1) navigation across unknown environments, (2) obstacle avoidance, (3) tracking of a moving target, and (4) execution of cooperative and coordinated behaviors based on implicit communication strategies. The design methodology based upon flocking principles has involved tests on computer simulations and subsequent experimentation on real-world robotic platforms. A customized implementation of Reynolds' flocking algorithm has been developed and successfully validated through flight tests performed with the swinglet MAV. It has been notably demonstrated how the Evolutionary Robotics approach could be successfully extended to the domain of fixed-wing aerial robotics, which has never received a great deal of attention in the past.
Communication and Distributed Control in Multi-Agent Systems
2011
63 pages
Report
Keine Angabe
Englisch
Aircraft , Common Carrier & Satellite , Communication and radio systems , Distribution , Drones , Robotics , Control systems , Strategy , Employment , Moving targets , Tracking , Navigation , Platforms , Evolution(General) , Fixed wing aircraft , Avoidance , Automatic pilots , Chemical agent detectors , Omnidirectional , Flight testing , United kingdom , Computerized simulation , Simulation , Eoard , Multi agent systems , Machine learning , Cognition , Language processing , Er(Evolutionary robotics) , Mav(Micro-unmanned aerial vehicles)
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