Abstract
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 scenario 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 (i.e., a dominant majority of robots prevents the swarm from switching to another, possibly better option). In addition, we introduce a scenario 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.
Original language | English |
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Title of host publication | 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) |
Number of pages | 6 |
Publisher | IEEE |
Publication date | 11.2019 |
Pages | 4100-4105 |
Article number | 8967777 |
ISBN (Print) | 978-1-7281-4005-6, 978-1-7281-4003-2 |
ISBN (Electronic) | 978-1-7281-4004-9 |
DOIs | |
Publication status | Published - 11.2019 |
Event | 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems - Macau, China Duration: 03.11.2019 → 08.11.2019 Conference number: 157163 |