Mapping is regarded as one of the most fundamental tasks for mobile robots. In this work, we present an approach that enables multiple resource-limited mobile robots to cooperatively build an image-based map of the environment and to afterwards localize in it. To achieve this, we deploy a hierarchical team of mobile robots. A parent robot possesses state-of-the-art sensors, computation power and acts as a leader. It teleoperates small child robots within its line-of-sight. In contrast to other approaches and due to the cooperation among the robots, we can relax the requirement that every robot must be able to self-localize to take part in multi-robot mapping. Additionally, our algorithm ensures the mapping of the entire area in an efficient way, i.e., it fulfills the requirements of area coverage. To test our approach, extensive experiments have been performed both in simulation and real-world. In the latter case, a team of four heterogeneous mobile robots was deployed. Besides the successful cooperation in the robot team, localization results are presented to validate the applicability of the proposed mapping procedure.

The scaling properties of multimodal optimization methods have seldom been studied, and existing studies often concentrated on the idea that all local optima of a multimodal function can be found and their number can be estimated a priori. We argue that this approach is impractical for complex, high-dimensional target functions, and we formulate alternative criteria for scalable multimodal optimization methods. We suggest that a scalable niching method should return the more local optima the longer it is run, without relying on a fixed number of expected optima. This can be fulfilled by sequential and semi-sequential niching methods, several of which are presented and analyzed in that respect. Results show that, while sequential local search is very successful on simpler functions, a clustering-based particle swarm approach is most successful on multi-funnel functions, offering scalability even under deceptive multimodality, and denoting it a starting point towards effective scalable niching.

An important problem in systems biology is parameter estimation for biochemical system models. Our work concentrates on the metabolic subnetwork of the valine and leucine biosynthesis in Corynebacterium glutamicum, an anaerobic actinobacterium of high biotechnological importance. Using data of an in vivo experiment measuring 13 metabolites during a glucose stimulus-response experiment we investigate the performance of various evolutionary algorithms on the parameter inference problem in biochemical modeling. Due to the inconclusive information on the reversibility of the reactions in the pathway, we develop both a reversible and an irreversible differential equation model based on the recent convenience kinetics approach. As the reversible model allows better approximation on the whole, we use it to analyze the impact of different settings on four especially promising EAs. We show that Particle Swarm Optimization as well as Differential Evolution are useful methods for parameter estimation on convenience kinetics models outperforming Genetic Algorithm and Evolution Strategy approaches and nearly reaching the quality of independent spline approximations on the raw data.