Knowledge of a manipulator's workspace is fundamental for a variety of tasks including robot design, grasp planning and robot base placement. Consequently, workspace representations are well studied in robotics. Two important representations are reachability maps and inverse reachability maps. The former predicts whether a given end-effector pose is reachable from where the robot currently is, and the latter suggests suitable base positions for a desired end-effector pose. Typically, the reachability map is built by discretizing the 6D space containing the robot's workspace and determining, for each cell, whether it is reachable or not. The reachability map is subsequently inverted to build the inverse map. This is a cumbersome process which restricts the applications of such maps. In this work, we exploit commonalities of existing six and seven axis robot arms to reduce the dimension of the discretization from 6D to 4D. We propose Reachability Map 4D (RM4D), a map that only requires a single 4D data structure for both forward and inverse queries. This gives a much more compact map that can be constructed by an order of magnitude faster than existing maps, with no inversion overheads and no loss in accuracy. Finally, we showcase the efficiency gains by applying RM4D for finding suitable base positions in a scenario with 800 target grasps.

Deep Learning-based approaches for 3d object detection and 6d pose estimation typically require large amounts of labeled training data. Labeling image data is expensive and particularly the 6d pose information is difficult to obtain, as it requires a complex setup during image acquisition. Training with synthetic data is therefore very attractive. Large amounts of synthetic, labeled data can be generated, but it is not yet fully understood how certain aspects of data generation affect the detection and pose estimation performance. Our work therefore focuses on creating synthetic training data and investigating the effects on detection performance. We present two methods for data generation: rendering object views and pasting them on random background images, and simulating realistic scenes. The former is computationally simpler and achieved better results, but the detection performance is still very sensitive to small changes, e.g. the type of background images.

In this paper we contribute to the research on facilitating industrial robot programming by presenting a concept for intuitive drag and drop like programming of pick and place tasks with Augmented Reality (AR). We propose a service-oriented architecture to achieve easy exchangeability of components and scalability with respect to AR devices and robot workplaces. Our implementation uses a HoloLens and a UR5 robot, which are integrated into a framework of RESTful web services. The user can drag recognized objects and drop them at a desired position to initiate a pick and place task. Although the positioning accuracy is unsatisfactory yet, our implemented prototype achieves most of the desired advantages to proof the concept.

This paper describes our concept for control of robots and machine tools through a private factory cloud which is dynamically extended by additional public resources. After discussing the opportunities and drawbacks of the virtualization of robot controllers (RC) and software programmable logic controllers (soft-PLC), we present first experiences with the setup of private and public infrastructures. Finally, we introduce our experimental design for investigating the requirements of machine control algorithms regarding availability, dependability and realtime capability in local and public configurations.