Robot

Algorithm

Sorting

Ant colony optimization algorithms

Scalability

Data mining

Computer science

Database

Ant colony

Artificial intelligence

Cluster analysis

Journal of The Royal Society Interface

For engineers the prospect of scalable collective robot systems is very appealing. Such systems typically adopt a decentralized approach in their control and coordination mechanism, which employs local sensing and action as well as limited communication. Under these constraints and informed by research on Temnothorax ants, two puck sorting algorithms were tested in a combination of simulation and with real robots. Both algorithms employed puck density as a cue. Only the overall local density, irrespective of puck type, was found to be required which offers the prospect for a more simple mechanism than had been previously considered. For one algorithm, this density cue was used both for picking up and dropping items and is, therefore, referred to as the ‘double density’ algorithm (DD). In the second algorithm, density was used as a cue only for picking up. Depositing an item was governed by the distance travelled which was specific to the type of item being carried. This was referred to as the ‘single density’ algorithm (SD). Unlike the DD it was found that, for the SD, the clustering of items is a necessary pre-condition for sorting. Results from ant experiments also showed that sorting is carried out in two phases: a primary clustering episode followed by a spacing phase. This strongly suggests that clustering may also be a precondition for spacing in ants.

Ant-inspired sorting by robots: the importance of initial clustering