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Due to the trend away from mass production to highly customized goods, there is a great demand for versatile robots in the manufacturing industry. Classic fixedprogrammed industrial robots and rail-bound transport vehicles, which are restricted to transporting standardized boxes, do not offer enough flexibility for modern factories. Machine learning methods and 3D vision can give manipulators the ability to perceive and understand the environment and therefore enable them to perform object manipulation tasks. State of the art grasp-detection methods rely on data with cumbersome annotated grasp-poses, while labelled data for object recognition only is easier to gather. This work describes the development of an automatic transport robot using a sensitive manipulator and 3D vision for autonomous transport of objects. This mobile manipulator is able to drive flexible paths, localize predefined objects and grasp them using an out-of-the-box neural network for object detection and hand-crafted methods for extracting grasp-points from depth images to avoid cumbersome grasp-point-annotated training data. Furthermore, this paper discusses problems occurring when a neural network trained o |
