Adaptive Path Formation in Self-Assembling Robot Swarms by Tree-like Vascular Morphogenesis

For self-assembly, robot swarms can be programmed to form predefined shapes. However, if the swarm is required to adapt the assembled shapes to dynamic features of the environment at runtime, then the shapes' structures need to be dynamic, too. A prerequisite for adaptation is the exploration and detection of changes followed by appropriate rearrangements of the assembled structure. We study a robot swarm that forms trees to explore its environment and searches for bright areas. The tree-formation process is inspired by the vascular morphogenesis of natural plants. The detection of light produces a virtual resource shared within the tree, helping to drop useless branches while reinforcing efficient paths between the bright areas and the tree root. We successfully verify our approach with several swarm robot experiments in a dynamic environment, showing that the robot swarm can collectively discriminate between light sources at different distances and of different qualities.