Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
Swarm intelligence
Autonomous Robots
Self-Organization in Biological Systems
Self-Organization in Biological Systems
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Simulating swarm intelligence in honey bees: foraging in differently fluctuating environments
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
A Space- and Time-Continuous Model of Self-Organizing Robot Swarms for Design Support
SASO '07 Proceedings of the First International Conference on Self-Adaptive and Self-Organizing Systems
Collective AI: context awareness via communication
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
A navigation algorithm for swarm robotics inspired by slime mold aggregation
SAB'06 Proceedings of the 2nd international conference on Swarm robotics
A macroscopic model for self-organized aggregation in swarm robotic systems
SAB'06 Proceedings of the 2nd international conference on Swarm robotics
Collective perception in a robot swarm
SAB'06 Proceedings of the 2nd international conference on Swarm robotics
Pheromone robotics and the logic of virtual pheromones
SAB'04 Proceedings of the 2004 international conference on Swarm Robotics
The I-SWARM project: intelligent small world autonomous robots for micro-manipulation
SAB'04 Proceedings of the 2004 international conference on Swarm Robotics
Opinion dynamics for decentralized decision-making in a robot swarm
ANTS'10 Proceedings of the 7th international conference on Swarm intelligence
Bonding as a swarm: applying bee nest-site selection behaviour to protein docking
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Evolving a novel bio-inspired controller in reconfigurable robots
ECAL'09 Proceedings of the 10th European conference on Advances in artificial life: Darwin meets von Neumann - Volume Part I
Robotics and Autonomous Systems
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This article presents a bio-inspired communication strategy for large-scale robotic swarms. The strategy is based purely on robot-to-robot interactions without any central unit of communication. Thus, the emerging swarm regulates itself in a purely self-organized way. The strategy is biologically inspired by the trophallactic behavior (mouth-to-mouth feedings) performed by social insects. We show how this strategy can be used in a collective foraging scenario and how the efficiency of this strategy can be shaped by evolutionary computation. Although the algorithm works stable enough that it can be easily parameterized by hand, we found that artificial evolution could further increase the efficiency of the swarm's behavior. We investigated the suggested communication strategy by simulation of robotic swarms in several arena scenarios and studied the properties of some of the emergent collective decisions made by the robots. We found that our control algorithm led to a nonlinear, but graduated path selection of the emerging trail of loaded robots. They favored the shortest path, but not all robots converged to this trail, except in arena setups with extreme differences in the length of the two possible paths. Finally, we demonstrate how the flexibility of collective decisions that arise through this new strategy can be used in changing environments. We furthermore show the importance of a negative feedback in an environment with changing foraging targets. Such feedback loops allow outdated information to decay over time. We found that task efficiency is constrained by a lower and an upper boundary concerning the strength of this negative feedback.