Robust Framework for intelligent Gripping Point Detection

In response to the rise in logistics costs, the degree of automation in the logistics process chain is to be significantly increased in the coming years. To create a basic understanding for the proposed work, the scope of logistics, the existing robot hardware and the first version of its perception modules are covered. The considered perception algorithm, consists of three modules: object detection, object selection and object localization. Subsequently, the performance of state of the art deep neural networks used in this system is analyzed in more detail using a specially created mobile application. The error clusters resulting from this analysis - the strong temporal variance and the false detections - are then countered by extensions to the perception algorithm. While the temporal uncertainties can be eliminated by an aggregation module, a validation module makes it possible to find missing or incorrect detections by including domain-specific context knowledge. The latter module reduces the error rate of the object detection from 10 % to 2 %. Although the former has only a minimal influence on object detection, it improves the performance in the object localization by 10 %. The combination of both modules with the existing perception algorithm allows a faultless use of the robot under the industrial conditions of the logistics environment in the automotive production without risking process stops, damaged parts or even human injuries.