Robots assembling machines: learning from the World Robot Summit 2018 Assembly Challenge

ABSTRACT

The Industrial Assembly Challenge at the World Robot Summit was held in 2018 to showcase the state-of-the-art of autonomous manufacturing systems. The challenge included various tasks, such as bin picking, kitting, and assembly of standard industrial parts into 2D and 3D assemblies. Some of the tasks were only revealed at the competition itself, representing the challenge of ‘level 5’ automation, i.e. programming and setting up an autonomous assembly system in less than one day. We conducted a survey among the teams that participated in the challenge and investigated aspects such as team composition, development costs, system setups as well as the teams' strategies and approaches. An analysis of the survey results reveals that the competitors have been in two camps: those constructing conventional robotic work cells with off-the-shelf tools, and teams who mostly relied on custom-made end effectors and novel software approaches in combination with collaborative robots. While both camps performed reasonably well, the winning team chose a middle ground in between, combining the efficiency of established play-back programming with the autonomy gained by CAD-based object detection and force control for assembly operations.     

A new benchmark for pose estimation with ground truth from virtual reality

The development of programming paradigms for industrial assembly currently gets fresh impetus from approaches in human demonstration and programming-by-demonstration. Major low- and mid-level prerequisites for machine vision and learning in these intelligent robotic applications are pose estimation, stereo reconstruction and action recognition. As a basis for the machine vision and learning involved, pose estimation is used for deriving object positions and orientations and thus target frames for robot execution. Our contribution introduces and applies a novel benchmark for typical multi-sensor setups and algorithms in the field of demonstration-based automated assembly. The benchmark platform is equipped with a multi-sensor setup consisting of stereo cameras and depth scanning devices (see Fig. 1). The dimensions and abilities of the platform have been chosen in order to reflect typical manual assembly tasks. Following the eRobotics methodology, a simulatable 3D representation of this platform was modelled in virtual reality. Based on a detailed camera and sensor simulation, we generated a set of benchmark images and point clouds with controlled levels of noise as well as ground truth data such as object positions and time stamps. We demonstrate the application of the benchmark to evaluate our latest developments in pose estimation, stereo reconstruction and action recognition and publish the benchmark data for objective comparison of sensor setups and algorithms in industry.     

Measuring and visualizing cyber threat intelligence quality

The very raison d’être of cyber threat intelligence (CTI) is to provide meaningful knowledge about cyber security threats. The exchange and collaborative generation of CTI by the means of sharing platforms has proven to be an important aspect of practical application. It is evident to infer that inaccurate, incomplete, or outdated threat intelligence is a major problem as only high-quality CTI can be helpful to detect and defend against cyber attacks. Additionally, while the amount of available CTI is increasing it is not warranted that quality remains unaffected. In conjunction with the increasing number of available CTI, it is thus in the best interest of every stakeholder to be aware of the quality of a CTI artifact. This allows for informed decisions and permits detailed analyses. Our work makes a twofold contribution to the challenge of assessing threat intelligence quality. We first propose a series of relevant quality dimensions and configure metrics to assess the respective dimensions in the context of CTI. In a second step, we showcase the extension of an existing CTI analysis tool to make the quality assessment transparent to security analysts. Furthermore, analysts’ subjective perceptions are, where necessary, included in the quality assessment concept.

     

Towards robot cell matrices for agile production – SDU Robotics' assembly cell at the WRC 2018

ABSTRACT

To support shifting to high mix/low volume production, manufacturers in high wage countries aim for robotizing their production operations – with a special focus on the late production phases, where robotic assembly cells are then confronted with any complexities resulting from part and product varieties. The ‘World Robot Challenge 2018’ (WRC 2018) emulated such high mix/low volume production scenarios in a competition taking place in Tokyo, Japan. As part of our activities in SDU's newly founded I4.0 Lab, we integrated and advanced our experiences and developments from our various R & D projects in a novel robotic assembly cell design to compete in the WRC 2018. This article describes the system architecture as well as main aspects of its implementation regarding robot control, robot programming and computer vision and how they contributed to winning the challenge. Due to the application of collaborative robots, the cell design allows for operation without fences. Hence, multiple copies of the cell can be arranged in a highly reconfigurable, highly adaptable matrix structure in which several production flows can be handled concurrently. This concept was demonstrated by the installation of a duplicate cell that allowed for parallel developments on two cells and prolonged development also after shipping the first cell to Japan.