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Cooperative objects’ transportation simulation was performed by a swarm of units acquiring the learning ability on the network of units. It is well known that several kinds of insects may achieve complex tasks in a group, where each insect may play its role by the information of its surroundings. This collective behavior, as emerged in a group of social insects, has been called “swarm intelligence.” Although the swarm intelligence has been applied to control a robotic swarm, these artificial systems still depend on controllers placed outside of a swarm to get rules for each robot. In order to improve the system and implement the natural behavior into the artificial control system, a swarm should have the learning ability that may correct and acquire its behavior by itself. In this study, a cooperative transportation task of different-sized objects to the proper destinations by lots of units was simulated. Transportation goals were varied to the size of objects. In addition, the units could not sense the size of the objects solely. Considering individual units in the artificial neural network, the learning algorithm of artificial neural networks has been applied to decide the transportation destinations. Each unit may decide its action based on nearby objects and the state of nearby units, and the swarm transports objects under the control of the swarm network. As a result, it is shown that the swarm acquired the learning ability on how to bring various-sized objects to the appropriate goals. |
