Fraunhofer-Institut für Produktionsanlagen und Konstruktionstechnik IPK

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  • Publication
    Visualizing trajectories for industrial robots from sampling-based path planning on mobile devices
    ( 2018)
    Guhl, Jan
    ;
    Vick, Axel
    ;
    Vonasek, Vojtech
    ;
    Krüger, Jörg
    Production lines are nowadays transforming into flexible modular and interconnected cells to react to rapidly changing product demands. The arrangement of the workspace inside the modular cells will vary according to the actual product being developed. Tasks like motion planning will not be possible to precompute. Instead, it has to be solved on demand. Planning the trajectories for the industrial robots with respect to changing obstacles and other varying environment parameters is hard to solve with classical path planning approaches. A possible solution is to employ sampling-based planning techniques. In this paper we present a distributed sampling-based path planner and an augmented reality visualization approach for verification of trajectories. Combining the technologies ensures a confirmed continuation of the production process under new conditions. Using parallel and distributed path planning speeds up the planning phase significantly and comparing different mobile devices for augmented reality representation of planned trajectories reveals a clear advantage for hands-free HoloLens. The results are demonstrated in several experiments in laboratory scale.
  • Publication
    Distributed Motion Planning for Industrial Random Bin Picking
    ( 2018)
    Vonasek, Vojtech
    ;
    Vick, Axel
    ;
    Krüger, Jörg
    The task of bin picking is to automatically unload objects from a container using a robotic manipulator. A widely used solution is to organize the objects into a predictable pattern, e.g., a workpiece carrier, in order to simplify all integral subtasks like object recognition, motion planning and grasping. In such a case, motion planning can even be solved offline as it is ensured that the objects are always at the same positions. However, there is a growing demand for non-structured bin picking, where the objects can be placed randomly in the bins. This arises from recent trends of transforming classical factories into smart production facilities allowing small lot sizes at the efficiency of mass production. Due to unknown positions of the objects in the non-structured bin picking scenario, trajectories for the manipulator cannot be precomputed, but they have to be computed online. Sampling-based motion planning methods like Rapidly Exploring Random Tree (RRT) can be used to plan the trajectories. In this paper, we propose a modification of RRT for distributed motion planning aiming to reduce the runtime. The planning task is first simplified by computing several guiding waypoints. The waypoints are distributed to a set of planners running in parallel and each planner computes a short trajectory between two given waypoints. Connecting the waypoints is easier than solving the original task, therefore each planner runs fast. In comparison to other parallel motion planning techniques, the proposed approach does not require any communication among the computational nodes, which is more suitable for cloud-based computing. The proposed work has been verified both in simulation and on a prototype of a bin picking system.