Genetic programming (videotape): the movie
Genetic programming (videotape): the movie
COOPERATIVE COEVOLUTION OF MULTI-AGENT SYSTEMS
COOPERATIVE COEVOLUTION OF MULTI-AGENT SYSTEMS
Genetic Programming IV: Routine Human-Competitive Machine Intelligence
Genetic Programming IV: Routine Human-Competitive Machine Intelligence
Coordinating multi-rover systems: evaluation functions for dynamic and noisy environments
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Evolving cooperative strategies for UAV teams
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Performance of digital pheromones for swarming vehicle control
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Robustness analysis of genetic programming controllers for unmanned aerial vehicles
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Self-organisation and communication in groups of simulated and physical robots
Biological Cybernetics
Evolving teamwork and coordination with genetic programming
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Robot algorithms for localization of multiple emission sources
ACM Computing Surveys (CSUR)
Evolving large scale UAV communication system
Proceedings of the 14th annual conference on Genetic and evolutionary computation
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For some tasks, the use of more than one robot may improve the speed, reliability, or flexibility of completion, but many other tasks can be completed only by multiple robots. This paper investigates controller design using multi-objective genetic programming for a multi-robot system to solve a highly constrained problem, where multiple unmanned aerial vehicles (UAVs) must monitor targets spread sparsely throughout a large area. UAVs have a small communication range, sensor information is limited and noisy, monitoring a target takes an indefinite amount of time, and evolved controllers must continue to perform well even as the number of UAVs and targets changes. An evolved task selection controller dynamically chooses a target for the UAV based on sensor information and communication. Controllers evolved using several communication schemes were compared in simulation on problem scenarios of varying size, and the results suggest that this approach can evolve effective controllers if communication is limited to the nearest other UAV.