AI Bibliography
AI Bibliography |
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Hunt, E. R., Jones, S., & Hauert, S. (2019). Testing the limits of pheromone stigmergy in high-density robot swarms. Royal Society open science, 6(11), 190225. |
| Resource type: Journal Article BibTeX citation key: Hunt2019 View all bibliographic details  |
Categories: Artificial Intelligence, Biological Science, Cognitive Science, Complexity Science, Computer Science, Data Sciences, Decision Theory, Engineering, General, Mathematics Subcategories: Autonomous systems, Big data, Chaos theory, Decision making, Drones, Edge AI, Internet of things, Machine learning, Neural nets, Robotics, Situational cognition, Systems theory Creators: Hauert, Hunt, Jones Publisher: Collection: Royal Society open science |
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Area coverage and collective exploration are key challenges for swarm robotics. Previous research in this field has drawn inspiration from ant colonies, with real, or more commonly virtual, pheromones deposited into a shared environment to coordinate behaviour through stigmergy. Repellent pheromones can facilitate rapid dispersal of robotic agents, yet this has been demonstrated only for relatively small swarm sizes (N < 30). Here, we report findings from swarms of real robots (Kilobots) an order of magnitude larger (N > 300) and from realistic simulation experiments up to N = 400. We identify limitations to stigmergy in a spatially constrained, high-density environment—a free but bounded two-dimensional workspace—using repellent binary pheromone. At larger N and higher densities, a simple stigmergic avoidance algorithm becomes first no better, then inferior to, the area coverage of non-interacting random walkers. Thus, the assumption of robustness and scalability for such approaches may need to be re-examined when they are working at a high density caused by ever-increasing swarm sizes. Instead, subcellular biology, and diffusive processes, may prove a better source of inspiration at large N in high agent density environments.
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