Objekterkennung

A new look into the depths of technical drawings: A deep learning framework reads CAD drawings more accurately than ever before, recognizing geometrical dimensioning and tolerancing, dimensions, and every other detail. What used to be tedious manual labor is now carried out by an AI that understands the special features of every line and label. This promising technology not only increases accuracy but also speeds up the processing of drawings considerably. The system thus opens up new avenues for precision in production.

The complex dynamics of block warehouses pose major challenges to the manual stocktaking process. Frequent relocation of pallets, crates or pallet cages without fixed storage locations leads to a time-consuming and error-prone inventory process, wherein goods often have to be searched for and damages due to improper storage can occur. The use of (semi-)autonomous drones offers a promising solution to enable automated stocktaking, especially if these are appropriately equipped for optical goods detection.

In factory operations, measuring equipment is often used without automatic storage or further processing possibilities of the measured value. In this case, employees must capture and process the measured values manually. In this article, an approach for the optical detection and digitization of measured values with the help of machine learning methods is presented. This aims to reduce the workload of the employees, avoid reading errors and enable automated documentation.

In contrast to the use of robots in standardized production processes, robots must be flexible and adaptable within dynamic logistics processes in order to cope with variable environmental conditions and non-standardized goods. Due to the recent advances in the field of artificial intelligence and networking through industry 4.0, robots will perform complex tasks in logistics in a reliable way in future. A crucial component of a robot system represents the interpretation of the work environment with the help of multi-modal sensor systems, especially image processing systems. This paper describes applications for robotic systems in logistics as well as a concrete example of focusing on the interpretation of multi-modal sensor data for the automation of a logistics task.

Progress in the areas of 3D sensor systems and artificial intelligence provides new opportunities for the development of flexible robotic systems that are applicable in scenarios without predefined and constant environmental conditions or standardized processes. An example from the field of logistics is the automatic unloading of containers. The development of a suitable robotic system on the one hand requires applicable gripping technologies, on the other hand it requires a reliable object recognition method in order to recognize and localize differently shaped logistic goods within a packaging scenario. This paper presents an object recognition method for logistic goods from three different shape classes using point clouds acquired by a laser scanner. The method is evaluated with real packaging scenarios.

There are numerous and relevant application domains like inspection, maintenance, surveillance, and handling of non-uniform goods where the use of robots would be beneficial. This article addresses the reasons that make it difficult for a present-day robot to master these domains. The dual arm system AILA will be presented, which takes further steps in the direction of such a use case.

Due to the increasing globalization of commodity flow a growth of mass good transportation is perceptible. Therefore automatic unloading of goods and their automatic transfer to logistic systems is one of the technical challenges. Opportunities arising from automatic goods unloading are explored in [1]. A system for unloading cubic goods automatically was developed and its introduction to market started last year. Changing environments and various operational areas are the motivation for research on a cognitive system for unloading goods. Hence, a concept for a cognitive system for unloading containers is introduced in this article. The components for such a system are image processing, robot control handling system and kinematics. For each of these components cognitive methods and technologies are proposed to be integrated in an overall system.