No abstract

Precise and reliable localization as well as dynamic path planning are key components to enable flexibly and efficiently operating mobile robots in industrial applications. Both strongly depend on up-to-date navigation maps of the respective environment. However, in these particular applications, providing those maps can be very challenging due to the typical dynamics and size of the environment. Promising approaches tackle the issue of localization in dynamic environments by estimating an update of the map while simultaneously localizing in it. In order to have a good estimate of the dynamics of the environment and update the map accordingly, frequent observations of all areas of the environment are required. This is often not possible, especially in large environments and from a single robot's perspective. To overcome this problem, we present a cooperative approach which uses the sensor information of all mobile robots and possibly available stationary sensors to generate an up-to-date global map and precisely localize the robots within it. We use dynamic occupancy grid maps with Rao-Blackwellized particle filters in combination with a suitable server-agent architecture to allow cooperation. The advantage of our approach is shown both in simulation and on real hardware

Die starke Individualisierung und Flexibilisierung der Produktion erfordert von den eingesetzten Automatisierungslösungen einen hohen Grad an Kognition und Selbständigkeit. Im Rahmen von Industrie 4.0 werden Systeme, die Sensoren, Aktoren und Kognition integrieren, als cyber-physische Systeme bezeichnet. Fahrerlose Transportsysteme (FTS) sind ein Beispiel für solch komplexe Elemente der Produktion

This paper presents a hybrid localization approach for mobile robots combining local grid maps and natural landmarks. The approach at hand benefits from the advantages of both environment representations. While using memory-efficient geometric models describing natural landmarks as features for localization in structured regions, the proposed system clusters the remaining areas as raw local grid maps and incorporates those as pose features only for unstructured areas of the environment. To evaluate the functionality and performance of the approach at hand, extensive testing and benchmarking in an experimental setup has been conducted using an external sensor system for reference measurements

Die starke Individualisierung und Flexibilisierung der Produktion erfordert von den eingesetzten Automatisierungslösungen einen hohen Grad an Kognition und Selbständigkeit. Im Rahmen von Industrie 4.0 werden Systeme, die Sensoren, Aktoren und Kognition integrieren, als cyber-physische Systeme bezeichnet. Fahrerlose Transportsysteme (FTS) sind ein Beispiel für solch komplexe Elemente der Produktion