Daniel Ewert

RoboCup Logistics League Sponsored by Festo: A Competitive Factory Automation Testbed

A new trend in automation is to deploy so-called cyber-physical systems (CPS) which combine computation with physical processes. The novel RoboCup Logistics League Sponsored by Festo (LLSF) aims at a such CPS logistic scenarios in an automation setting. A team of robots has to produce products from a number of semi-finished products which they have to machine during the game. Different production plans are possible and the robots need to recycle scrap byproducts. This way, the LLSF is a very interesting league offering a number of challenging research questions for planning, coordination, or communication in an application-driven scenario. In this paper, we outline the objectives of the LLSF and present steps for developing the league further towards a benchmark for logistics scenarios for CPS. As a major milestone we present the new automated referee system which helps in governing the game play as well as keeping track of the scored points in a very complex factory scenario.

Self-optimising Production Systems

One of the central success factors for production in high-wage countries is the solution of the conflict that can be described with the term “planning efficiency”. Planning efficiency describes the relationship between the expenditure of planning and the profit generated by these expenditures. From the viewpoint of a successful business management, the challenge is to dynamically find the optimum between detailed planning and the immediate arrangement of the value stream. Planning-oriented approaches try to model the production system with as many of its characteristics and parameters as possible in order to avoid uncertainties and to allow rational decisions based on these models. The success of a planning-oriented approach depends on the transparency of business and production processes and on the quality of the applied models. Even though planning-oriented approaches are supported by a multitude of systems in industrial practice, an effective realisation is very intricate, so these models with their inherent structures tend to be matched to a current stationary condition of an enterprise. Every change within this enterprise, whether inherently structural or driven by altered input parameters, thus requires continuous updating and adjustment. This process is very cost-intensive and time-consuming; a direct transfer onto other enterprises or even other processes within the same enterprise is often impossible. This is also a result of the fact that planning usually occurs a priori and not in real-time. Therefore it is hard for completely planning-oriented systems to react to spontaneous deviations because the knowledge about those naturally only comes a posteriori.

Automation of robotic assembly processes on the basis of an architecture of human cognition

A novel concept to cognitive automation of robotic assembly processes is introduced. An experimental assembly cell with two robots was designed to verify and validate the concept. The cell’s numerical control—termed a cognitive control unit (CCU)—is able to simulate human information processing at a rule-based level of cognitive control. To enable the CCU to work on a large range of assembly tasks expected of a human operator, the cognitive architecture SOAR is used. On the basis of a self-developed set of production rules within the knowledge base, the CCU can plan assembly processes autonomously and react to ad-hoc changes in assembly sequences effectively. Extensive simulation studies have shown that cognitive automation based on SOAR is especially suitable for random parts supply, which reduces planning effort in logistics. Conversely, a disproportional increase in processing time was observed for deterministic parts supply, especially for assemblies containing large numbers of identical parts.

Towards Benchmarking Cyber-Physical Systems in Factory Automation Scenarios

A new trend in automation is to deploy so-called cyber-physical systems (CPS) which combine computation with physical processes. In future factory automation scenarios, mobile robots will play an important role to help customizing the production process, for instance, by transporting semi-products and raw materials to the machines. Therefore it will be important to compare the performance of mobile robots in such future logistics tasks. In this paper we sketch how the novel RoboCup Logistics League with its automated referee and overhead tracking system can be developed into a standard benchmark for logistics application in factory automation scenarios.

A graph based hybrid approach of offline pre-planning and online re-planning for efficient assembly under realtime constraints

Assembly tasks, e.g. the assembly of an automobile headlight, are a big challenge for nowadays planning systems. Depending on the problem domain, a planner has to deal with a huge number of objects which can be combined in several ways. Uncertainty about the outcome of actions and the availability of parts to be assembled even worsens the problem. As a result, classic approaches have shown to be of little use for reactive (online) planning during assembly, due to the huge computational complexity. The approach proposed in this paper bypasses this problem by calculating the complex planning problems, e.g. which parts must be mounted in which sequence, prior to the actual assembly. During assembly the precalculated solutions are then used to provide fast decisions allowing an efficient execution of the assembly. Within this paper this online planning combined with offline planning and the assessment of realtime constraints during assembly could be executed in the future will be described.

Decisive Factors for the Success of the Carologistics RoboCup Team in the RoboCup Logistics League 2014

The RoboCup Logistics League is one of the youngest application- and industry-oriented leagues. Even so, the complexity and level of difficulty has increased over the years. We describe decisive technical and organizational aspects of our hardware and software systems and (human) team structure that made winning the RoboCup and German Open competitions possible in 2014.

PROBLEM BASED LEARNING OF OBJECT-ORIENTED PROGRAMMING WITH LEGO MINDSTORMS NXT AND LEJOS

Problem based learning has proved to be a powerful educational approach to successful and effective teaching. Experiences worldwide have shown the attraction of robotics and the improved motivation of students dealing with robots. Problem based learning (PBL) as well as robotics are usually applied when dealing with smaller groups of students. However, in the first year of study student numbers of more than 1000 are a common phenomenon for lectures in mechanical engineering. This paper introduces a setup for teaching object-oriented programming based on programming LEGO Mindstorms NXT robots for large scaled groups. The huge number of students – up to 1500 students per year – to be dealt with presents a special challenge, due to resource limitations as well as technical aspects. We therefore present a problem based learning approach based on a fixed robot setup with prebuild robot models.

Edge extraction by merging 3D point cloud and 2D image data

Edges provide important visual information by corresponding to discontinuities in the physical, photometrical and geometrical properties of scene objects, such as significant variations in the reflectance, illumination, orientation and depth of scene surfaces. The significance has drawn many people to work on the detection and extraction of edge features. The characteristics of 3D point clouds and 2D digital images are thought to be complementary, so the combined interpretation of objects with point clouds and image data is a promising approach to describe an object in computer vision area. However, the prerequisite for different levels of integrated data interpretation is the geometric referencing between the 3D point cloud and 2D image data, and a precondition for geometric referencing lies in the extraction of the corresponding features. Addressing the wide-ranged applications of edge detection in object recognition, image segmentation and pose identification, this paper presents a novel approach to extract 3D edges. The main idea is combining the edge data from a point cloud of an object and its corresponding digital images. Our approach is aimed to make use of the advantages of both edge processing and analysis of point clouds and image processing to represent the edge characteristics in 3D with increased accuracy. On the 2D image processing part, an edge extraction is applied on the image by using the Canny edge detection algorithm after the raw image data pre-processing. An easily-operating pixel data mapping mechanism is proposed in our work for corresponding 2D image pixels with 3D point cloud pixels. By referring to the correspondence map, 2D edge data are merged into 3D point cloud. On the point cloud part, the border extracting operator is performed on the range image. As a preparation work, the raw point cloud data are used to generate a range image. Edge points in the range image, points with range, are converted to 3D point type with the application of the Point Cloud Library (PCL) to define the edges in the 3D point cloud.

Selfoptimized assembly planning for a ROS based robot cell

In this paper, we present a hybrid approach to automatic assembly planning, where all computational intensive tasks are executed once prior to the actual assembly by an Offline Planner component. The result serves as basis of decision-making for the Online Planner component, which adapts planning to the actual situation and unforeseen events. Due to the separation into offline and online planner, this approach allows for detailed planning as well as fast computation during the assembly, therefore enabling appropriate assembly duration even in nondeterministic environments. We present simulation results of the planner and detail the resulting planners behavior.

The Carologistics Approach to Cope with the Increased Complexity and New Challenges of the RoboCup Logistics League 2015

The RoboCup Logistics League RCLL has seen major rule changes increasing the complexity, e.g. by raising the number of product variants from 3 to almost 250, and introducing new challenges like the handling of physical processing machines. We describe various aspects of our system that allowed to improve the performance in 2015 and our efforts to advance the league as a whole.