-Remote laser beam welding (R laser beam cutting (RLC) are technologies that a automated applications in industrial faciliti contactless mode of action. The main adv processes are the high operation speed, good pr a tailored contour of the welding seams and cut optimal use of these processes the laser beam accurately over the work piece. A common tec of such processes is the combination of a laser unit, a laser scanner and an industrial ro represents a programming challenge, as the freedom of the robot plus the two degrees of fre scanner lead to a kinematic redundant syste advantages of this combination, the large robo the fast and precise motion of the laser scanne attractive technology for improvement of wel processes. An automated programming system setup is presented in this paper.
Automotive manufacturers and customers wish to have fully automated driving functionality available in a huge set of locations, scenarios, and markets. This raises the need for universally applicable scene understanding and motion planning algorithms that do not rely on highly accurate maps or excessive infrastructure communication. In this paper we introduce two novel approaches for extracting a topological roadgraph with possible intersection options from sensor data along with a geometric representation of the available maneuvering space. Also, a search and optimization-based path planning method for guiding the vehicle along a selected track in the roadgraph and within the free-space is presented. We compare the methods presented in simulation and show results of a test drive with a research vehicle. Our evaluations show the applicability in low speed maneuvering scenarios and the stability of the algorithms even for low quality input data.
Ensuring safe operation of autonomous vehicles requires testing them including critical combinations of obstacle configurations plus sensor and actuator inaccuracies. A method for testing inaccuracy combinations has already been published by the authors. This paper enhances the capabilities of the method by automatically collecting scenarios from physical vehicle drives that are relevant for further analysis. For such situations, a state trace including all variables of the whole planning and control system is stored together with environment information. The stored data is the input for further analysis. An implementation of this approach is demonstrated using a simulation and a full size vehicle.
Abstract:As the rise of single-core processing power is exhausted due to technical limitations, the automotive branch is forced to migrate its control unit software to architectures that feature multiple Independent Execution Units (IEUs). This policy shift brings along new problems resulting from the tremendously increased complexity of such systems. Facing these challenges, software engineers have to cope with possible data inconsistencies caused by, e.g., race conditions or cycles. Being an important and standardized software architecture for electronic control units, the Automotive Open System Architecture (AUTOSAR) provides the basis for tools that support the complexity handling when migrating to architectures with multiple IEUs. Our concept is realized by a tool that executes data dependency analyses directly on AUTOSAR models, determines critical dependencies, automatically solves trivial problems and provides semi-automatic resolution of advanced conflicts.To support the actual parallelization of the system, the tool additionally determines groups of executable units that are suitable to run on a common IEU. This appreciably facilitates the validation of AUTOSAR models and the search for a good mapping of the processing tasks to IEUs.
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