Collectives and Design Complex Systems
Collectives and Design Complex Systems
Evolving cooperative strategies for UAV teams
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Evolutionary path planner for UAVs in realistic environments
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Analyzing and visualizing multiagent rewards in dynamic and stochastic domains
Autonomous Agents and Multi-Agent Systems
Proceedings of the 40th Conference on Winter Simulation
Towards a deeper understanding of cooperative equilibrium: characterization and complexity
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
CLEAN rewards for improving multiagent coordination in the presence of exploration
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
Mobile ad hoc networks in the sky: state of the art, opportunities, and challenges
Proceedings of the second ACM MobiHoc workshop on Airborne networks and communications
| Hi-index | 0.00 |
Unmanned Aerial Vehicles (UAVs) have traditionally been used for short duration missions involving surveillance or military operations. Advances in batteries, photovoltaics and electric motors though, will soon allow large numbers of small, cheap, solar powered unmanned aerial vehicles (UAVs) to fly long term missions at high altitudes. This will revolutionize the way UAVs are used, allowing them to form vast communication networks. However, to make effective use of thousands (and perhaps millions) of UAVs owned by numerous disparate institutions, intelligent and robust coordination algorithms are needed, as this domain introduces unique congestion and signal-to-noise issues. In this paper, we present a solution based on evolutionary algorithms to a specific ad-hoc communication problem, where UAVs communicate to ground-based customers over a single wide-spectrum communication channel. To maximize their bandwidth, UAVs need to optimally control their output power levels and orientation. Experimental results show that UAVs using evolutionary algorithms in combination with appropriately shaped evaluation functions can form a robust communication network and perform 180% better than a fixed baseline algorithm as well as 90% better than a basic evolutionary algorithm.