In this paper we address the demand for flexibility and economic efficiency in industrial autonomous guided vehicle (AGV) systems by the use of cloud computing. We propose a cloud-based architecture that moves parts of mapping, localization and path planning tasks to a cloud server. We use a cooperative longterm Simultaneous Localization and Mapping (SLAM) approach which merges environment perception of stationary sensors and mobile robots into a central Holistic Environment Model (HEM). Further, we deploy a hierarchical cooperative path planning approach using Conflict-Based Search (CBS) to find optimal sets of paths which are then provided to the mobile robots. For communication we utilize the Manufacturing Service Bus (MSB) which is a component of the manufacturing cloud platform Virtual Fort Knox (VFK). We demonstrate the feasibility of this approach in a real-life industrial scenario. Additionally, we evaluate the system's communication and the planner for various numbers of agents

In this paper we address the problem of online trajectory optimization and cooperative collision avoidance when multiple mobile service robots are operating in close proximity to each other. Using cooperative trajectory optimization to obtain smooth transitions in multi-agent path crossing scenarios applies to the demand for more flexibility and efficiency in industrial autonomous guided vehicle (AGV) systems.We introduce a general approach for online trajectory optimization in dynamic environments. It involves an elastic-band based method for time-dependent obstacle avoidance combined with a model predictive trajectory planner that takes into account the robot's kinematic and kinodynamic constraints. We augment that planning approach to be able to cope with shared trajectories of other agents and perform an potential field based cooperative trajectory optimization.Performance and practical feasibility of the proposed approach are demonstrated in simulation as well as in real world experiments carried out on a representative set of path crossing scenarios with two industrial mobile service robots.

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

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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