This study assimilated data from 9 Water Vapor(WV) channels of Metop-B IASI into the CMA-GFS 4DVAR system. Two sets of experiments, METOP-B-IR and CTRL, were designed, conducting an 18-day cycle assimilation test to evaluate the impact of assimilating MetOp-B IASI water vapor data on CMA-GFS model’s analysis and forecasting fields. By using ECMWF reanalysis data ERA5 as a reference, the study demonstrated that after assimilating 9 WV data, there was certain extent of improvement in the humidity and wind fields and led to slightly positive improvements in the forecasting skills.

Localization in dynamic environments is still a challenging problem in robotics - especially if rapid and large changes occur irregularly. Inspired by SLAM algorithms, our Bayesian approach to this so-called dynamic localization problem divides it into a localization problem and a mapping problem, respectively. To tackle the localization problem we use a particle filter, coupled with a distance filter and a scan matching method, which achieves a more robust localization against dynamic obstacles. For the mapping problem we use an extended sensor model which results in an effective and precise map update effect. We compare our approach against other localization methods and evaluate the impact the map update effect has on the localization in dynamic environments.

Autonomous robot teams that simultaneously dispatch transportation tasks are playing a more and more important role in the industry. In this paper we consider the multi-robot motion planning problem in large robot teams and present a decoupled approach by combining decentralized path planning methods and swarm technologies. Instead of a central coordination, a proper behavior which is directly selected according to the context is used by the robot to keep cooperating with others and to resolve path collisions. We show experimentally that the quality of solutions and the scalability of our method are significantly better than those of conventional decoupled path planning methods. Furthermore, compared to conventional swarm approaches, our method can be widely applied in large-scale environments.

Autonomous robot teams that simultaneously dispatch transportation tasks are playing more and more an important role in present logistic centers and manufacturing plants. In this paper we consider the problem of robot motion planning for large robot teams in the industrial domain. We present adaptive road map optimization (ARMO) that is capable of adapting the road map whenever the environment has changed. Based on linear programming, ARMO computes an optimal road map configuration according to environmental constraints (including human whereabouts) and the demand for transportation tasks from loading stations in the plant. For detecting dynamic changes, the environment is described by a grid map augmented with a hidden Markov model (HMM). We show experimentally that ARMO outperforms decoupled planning in terms of computation time and time needed for task completion.

We consider the problem of Decentralized Simultaneous Localization And Mapping (DSLAM) for pedestrians in the context of Urban Search And Rescue (USAR). In this context, DSLAM is a challenging task. First, data exchange fails due to cut off communication links. Second, loop-closure is cumbersome due to the fact that firefighters will intentionally try to avoid performing loops when facing the reality of emergency response, e.g. while they are searching for victims. In this paper, we introduce a solution to this problem based on the non-selfish sharing of information between pedestrians for loop-closure. We introduce a novel DSLAM method which is based on data exchange and association via RFID technology, not requiring any radio communication. The approach has been evaluated in both semi-indoor and outdoor environments. The presented results show that sharing information between single pedestrians allows to optimize globally their individual paths, even if they are not able to communicate directly.