Plasticity in Collective Decision-Making for Robots: Creating Global Reference Frames, Detecting Dynamic Environments, and Preventing Lock-ins
| Main Authors: | Mohammad Divband Soorati, Maximilian Krome, Marco Mendoza, Javad Ghofrani, Heiko Hamann |
|---|---|
| Format: | info Video eJournal |
| Terbitan: |
, 2019
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| Online Access: |
https://zenodo.org/record/3345808 |
Daftar Isi:
- Swarm robots operate as autonomous agents and a swarm as a whole gets autonomous by its capability of collective decision-making. Despite intensive research on models of collective decision-making, the implementation in multi-robot systems is still challenging. Here, we advance the state of the art by introducing more plasticity to the decision-making process and by increasing the task difficulty. Most studies on large-scale multi-robot decision-making are limited to one instance of an iterated exploration-dissemination phase followed by successful and permanent convergence. We investigate a dynamic environment that requires constant collective monitoring of option qualities. Once a significant change in qualities is detected by the swarm, it has to collectively reconsider its previous decision accordingly. This is only possible by preventing lock-ins, a global consensus state of no return. In addition, we introduce a task of increased difficulty as the robots must locate themselves to assess the quality of an option. Using local communication, swarm robots propagate hop-count information throughout the swarm to form a global reference frame. We successfully validate our implementation in many swarm robot experiments concerning robustness to disruptions of the reference frame, scalability, and adaptivity to a dynamic environment.