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Aerial communications12/28/2022 ![]() Wind estimation using an optical flow sensor on a miniature air vehicle. Rodriguez, A., Andersen, E., Bradley, J., & Taylor, C. In Proceedings of the genetic and evolutionary computation conference (Vol. 2, pp. Evolving cooperative strategies for UAV teams. In SIGGRAPH computer graphics (Vol. 21, pp. Flocks, herds and schools: a distributed behavioral model. ![]() Environments for multi-agent systems (pp. In Lecture notes in computer science : Vol. Digital pheromones for coordination of unmanned vehicles. In Proceedings of the IEEE international conference on robotics and automation (pp. Developing a control architecture for multiple unmanned aerial vehicles to search and localize RF time-varying mobile targets: part I. In Proceedings of the IEEE 9th Asia-Pacific conference on communications (Vol. 2, pp. ![]() Information and communication technology in the service of disaster mitigation and humanitarian relief. Evolutionary robotics: the biology, intelligence, and technology of self-organizing machines. In From animals to animats 7, proceedings of the 7th international conference on simulation of adaptive behavior (pp. Minimalist coherent swarming of wireless networked autonomous mobile robots. Nembrini, J., Winfield, A., & Melhuish, C. A cooperative perception system for multiple UAVs: application to automatic detection of forest fires. Merino, L., Caballero, F., Martínez-de Dios, J. Proceedings of the World automation congress (pp. Control of swarming UAVs in collaborative missions. In Flying insects and robots symposium (p. A simple and robust fixed-wing platform for outdoor flying robot experiments. In Proceedings of the AIAA 3rd “Unmanned unlimited” technical conference, AIAA paper 2004-6530. Information energy for sensor-reactive UAV flock control. Lawrence, D., Donahue, R., Mohseni, K., & Han, R. In Proceedings of the IEEE international conference on wireless and mobile communications. Mobility models for UAV group reconnaissance applications. In Proceedings of the 44th IEEE Midwest symposium on circuits and systems (Vol. 1, pp. Design and analysis of swarm-based sensor systems. In Proceedings of the 10th annual international conference on mobile computing and networking (pp. In Proceedings of the IEEE swarm intelligence symposium (pp. Beyond swarm intelligence: the UltraSwarm. Holland, O., Woods, J., De Nardi, R., & Clark, A. Evolving behaviors for a swarm of unmanned air vehicles. Gaudiano, P., Bonabeau, E., & Shargel, B. In Proceedings of the 41st IEEE conference on decision and control (Vol. 3, pp. Cooperative control for multiple autonomous UAVs searching for targets. Piscataway: IEEE Press.įlint, M., Polycarpou, M., & Fernández-Gaucherand, E. ![]() Hierarchical distributed control for search and tracking by heterogeneous aerial robot networks. UltraSwarm: a further step towards a flock of miniature helicopters. Princeton: Princeton University Press.ĭe Nardi, R., & Holland, O. R., Sneyd, J., Theraulaz, G., & Bonabeau, E. Piscataway: IEEE Press.Ĭamazine, S., Deneubourg, J. In Proceedings of the IEEE military communications conference (Vol. 3, pp. Coordinated flocking of UAVs for improved connectivity of mobile ground nodes. A detailed behavioral analysis is then performed on the fittest swarm to gain insight as to the behavior of the individual agents.īasu, P., Redi, J., & Shurbanov, V. This approach has the advantage of yielding original and efficient swarming strategies. For this reason, artificial evolution is used to automatically develop neuronal controllers for the swarm of homogenous agents. Rather than relative or absolute positioning, agents must rely only on their own heading measurements and local communication with neighbors.ĭesigning local interactions responsible for the emergence of the SMAVNET deployment and maintenance is a challenging task. This endeavor is motivated by an application whereby a large number of Swarming Micro Air Vehicles (SMAVs), of fixed-wing configuration, must organize autonomously to establish a wireless communication network (SMAVNET) between users located on ground. In this paper we study in 2D simulation the design of a swarm system which does not make use of positioning information or stigmergy. However, such resources are not always obtainable in real-world applications because of hardware and environmental constraints. In most swarm systems, agents are either aware of the position of their direct neighbors or they possess a substrate on which they can deposit information (stigmergy). ![]()
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