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Abstract-Self-organization in natural systems demonstrates very reliable and scalable collective behavior without using any central elements. When providing collective robotic systems with self-organizing principles, we are facing new problems of making self-organization purposeful, self-adapting to changing environments and faster, in order to meet requirements from a technical perspective. This paper describes on-going work of creating such an artificial self-organization within artificial robot organisms, performed in the framework of several European projects.

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Abstract-The field of reconfigurable swarms of modular robots has achieved a current status of performance that allows applications in diverse fields that are characterized by human support (e.g. exploratory and rescue tasks) or even in human-less environments. The main goal of the EC project REPLICATOR [1] is the development and deployment of a heterogeneous swarm of modular robots that are able to switch autonomously from a swarm of robots, into different organism forms, to reconfigure these forms, and finally to revert to the original swarm mode [2]. To achieve these goals three different types of robot modules have been developed and an extensive suite of embodied distributed cognition methods implemented [3]. Hereby the methodological key aspects address principles of self-organization. In order to tackle our ambitious approach a Grand Challenge has been proposed of autonomous operation of 100 robots for 100 days (100 days, 100 robots). Moreover, a framework coined the SOScycle (SOS: Swarm-Organism-Swarm) is developed. It controls the transitions between internal phases that enable the whole system to alternate between different modes mentioned above. This paper describes the vision of the Grand Challenge and the implementation and the results of the different phases of the SOS-cycle.

In computer vision there are many sophisticated methods to perform inference over multiple lines, however they are quite ad-hoc. In this paper a fully Bayesian approach is used to fit multiple lines to a point cloud simultaneously. Our model extends a linear Bayesian regression model to an infinite mixture model and uses a Dirichlet process as a prior for the partition. We perform Gibbs sampling over non-unique parameters as well as over clusters to fit lines of a fixed length, a variety of orientations, and a variable number of data points. The performance is measured using the Rand Index, the Adjusted Rand Index, and two other clustering performance indicators. This paper is mainly meant to demonstrate that general Bayesian methods can be used for line estimation. Bayesian methods, namely, given a model and noise, perform optimal inference over the data. Moreover, rather than only demonstrating the concept as such, the first results are promising with respect to the described clustering performance indicators. Further research is required to extend the method to inference over multiple line segments and multiple volumetric objects that will need to be built on the mathematical foundation that has been laid down in this paper.