Abstract
In this paper a honeybee inspired collective-decision-making algorithm called BEECLUST is studied in a swarm of autonomous robots and the performance of the swarm is investigated in different conditions. The algorithm has low requirements thus it is promising for implementation in robots with low resources. Here the algorithm is applied in swarms of improved e-puck robots in three different conditions in order to study the strengths and limitations of the algorithm. The collective system demonstrated a high performance in adapting to a dynamic environment as well as a very low sensitivity to additional robots with malfunctioning sensors. On the other hand the system shows an strong response to robots that act as social seeds influencing the decision-making of the swarm.
Highlights
In this paper a honeybee inspired collective-decision-making swarm robotics, autonomous agents, self-organisation, bioalgorithm called BEECLUST is studied in a swarm of auinspired algorithm, swarm intelligence, collective decision tonomous robots and the performance of the swarm is inmaking, search vestigated in different conditions
In [17] it was shown in a light gradient, that agents controlled by the BEECLUST algorithm are able to find the global optimum out of several local optima, but they are able to react on environmental changes like changing the global and local optima
The dynamic environment experiment was created to demonstrate that agents controlled by the BEECLUST algorithm are flexible in their decision-making in a dynamic environment even where the environment is very inert and unstable
Summary
In this paper a honeybee inspired collective-decision-making swarm robotics, autonomous agents, self-organisation, bioalgorithm called BEECLUST is studied in a swarm of auinspired algorithm, swarm intelligence, collective decision tonomous robots and the performance of the swarm is inmaking, search vestigated in different conditions. The algorithm has low requirements it is promising for implementation in robots with low resources. Social insects are promising sources of inspiration for colder to study the strengths and limitations of the algorithm. The algorithm is investigated for its behaviour in differto robots that act as social seeds influencing the decisionent conditions in order to make a deeper understanding of making of the swarm. The capabilities and limitations of the algorithm for swarm robotics and getting insights into mechanisms used in natural honeybees
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