- Mixed Biological-Robotic Systems
- Multi-Robot Coordination and Swarming
- Smart Materials
- Journal Club
Aggregation processes are ubiquitous in a multitude of domains ranging from physics and biology, to swarm robotic systems. The processes responsible for aggregation are likely to share similarities on different scales, from proteins, to social insects, and mammals, suggesting a common methodological framework for modeling their dynamics. Formal modeling of the underlying processes might lead not only to a better understanding of natural processes, e.g., the aggregation dynamics of gregarious insects, but is also beneficial in an engineering context, for instance for designing nano-structures by self-assembly, self-organized building processes, self-organized bacteria or mixed animal-robot societies as well as determining their parameters. Also, aggregation can be understood as an important collective behavior in swarm robotics, as it might be the prerequisite for more complex collective tasks that rely on local interactions.
We model the dynamics of self-organized robot aggregation inspired by a study on the aggregation of gregarious arthropods. In swarms of German cockroaches, aggregation into clusters emerges solely from local interactions between the individuals, whereas the probabilities to join or leave a cluster are a function of the cluster size. We propose a non-spatial population dynamics model that keeps track of the number of robots in clusters of specific size using control parameters of the individual robots and the probability of detecting another robot in the environment. The model is able to quantitatively and qualitatively predict the dynamics observed in extensive realistic multi-robot simulation, and provides qualitative agreement with data obtained from aggregation of Blattela germanica larvae. In particular, we show by analysis, numerical and realistic simulation that the emergence of a single aggregate requires a minimal communication range between individuals.
Kernbach, Serge (Ed.): Handbook of Collective Robotics, pp. 231-260, Pan Stanford, 2013.
The International Journal of Robotics Research, 30 (5), pp. 615-626, 2011, (Special Issue on Stochasticity in Robotics and Biological Systems).
Modeling Self-Organized Aggregation in a Swarm of Miniature Robots (Inproceeding)
Int. Conf. on Robotics and Automation, Workshop on Collective Behaviors inspired by Biological and Biochemical Systems, Rome, Italy, 2007.