Self-organized Clustering of Square Objects by Multiple Robots
Object clustering is a widely studied task in which self-organized robots form piles from dispersed objects. Although central clusters are usually desired, workspace boundaries can cause perimeter cluster formation to dominate. This research demonstrates successful clustering of square boxes —an especially challenging instance since flat edges exacerbate adhesion to boundaries— using simpler robots than previous published research. Our solution consists of two novel behaviors, Twisting and Digging, which exploit the objects’ geometry to pry boxes free from boundaries. We empirically explored the significance of different divisions of labor by measuring the spatial distribution of robots and the system performance. Data from over 40 hours of physical robot experiments show that different divisions of labor have distinct features, e.g., one is reliable while another is especially efficient.
KeywordsCentral Cluster Mixed Strategy Multiple Robot Object Cluster Digging Behavior
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- 4.Deneubourg, J., Goss, S., Franks, N., Sendova-Franks, A., Detrain, C., Chrétien, L.: The dynamics of collective sorting robot-like ants and ant-like robots. In: Proc. of Simulation of Adaptive Behavior (SAB), pp. 356–363 (1991)Google Scholar
- 5.Beckers, R., Holland, O., Deneubourg, J.: From Local Actions to Global Tasks: Stigmergy and Collective Robotics. In: Proc. of Artificial Life IV, pp. 181–189 (1994)Google Scholar
- 6.Martinoli, A.: Swarm Intelligence in Autonomous Collective Robotics from Tools to the Analysis and Synthesis of Distributed Control Strategies. PhD thesis, École Polytechnique Fédérale de Lausanne (1999)Google Scholar
- 10.Maris, M., Boeckhorst, R.: Exploiting physical constraints: Heap formation through behavioral error in a group of robots. In: Proc. of Conference on Intelligent Robots and Systems (IROS), pp. 1655–1660 (1996)Google Scholar