Automated 3D-motion planning for ramps and stairs in intra-logistics material flow simulations

In this paper we present the integration of computational steering techniques into the interactive material flow simulation d3FACT insight. This kind of simulation differs from traditional, long running High Performance Computing (HPC) simulations such as Computational Fluid Dynamics (CFD) or Molecular Dynamics in many aspects. One very important aspect is that these simulations run in (soft) real-time, thus the corresponding visualization needs to be updated after every step of the simulation. In turn, this allows to let changes, made through the visualization, impact the actual simulation and again, to see the effects in visualization. To allow this kind of control over the simulation and to further provide a flexible basis to integrate several instances of simulation, visualization and steering components, we used and enhanced a self-developed computational steering platform, which fits best for the needs of highly interactive and distributed simulations. Thereby we are able to realize multi-user and comparative scenarios which were not possible in this field of simulations before.

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Fast converging, automated experiment runs for material flow simulations using distributed computing and combined metaheuristics

Proceedings Title: Proceedings of the 2012 Winter Simulation Conference (WSC) ◽

10.1109/wsc.2012.6465058 ◽

2012 ◽

Cited By ~ 10

Author(s):

Christoph Laroque ◽

Alexander Klaas ◽

Jan-Hendrik Fischer ◽

Mathis Kuntze

Keyword(s):

Distributed Computing ◽

Material Flow ◽

Flow Simulations

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Automated Model Development and Parametrization of Material Flow Simulations

2019 Winter Simulation Conference (WSC) ◽

10.1109/wsc40007.2019.9004702 ◽

2019 ◽

Author(s):

Michael Milde ◽

Gunther Reinhart

Keyword(s):

Material Flow ◽

Model Development ◽

Flow Simulations

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Hardness of Motion Planning with Obstacle Uncertainty in Two Dimensions

The International Journal of Robotics Research ◽

10.1177/0278364921992787 ◽

2021 ◽

pp. 027836492199278

Author(s):

Luke Shimanuki ◽

Brian Axelrod

Keyword(s):

Motion Planning ◽

Free Path ◽

Configuration Space ◽

Exact Algorithm ◽

Two Dimensions ◽

Planning Under Uncertainty ◽

Constant Number ◽

3D Motion ◽

Practical Algorithms ◽

Case Planning

We consider the problem of motion planning in the presence of uncertain obstacles, modeled as polytopes with Gaussian-distributed faces (PGDFs). A number of practical algorithms exist for motion planning in the presence of known obstacles by constructing a graph in configuration space, then efficiently searching the graph to find a collision-free path. We show that such an exact algorithm is unlikely to be practical in the domain with uncertain obstacles. In particular, we show that safe 2D motion planning among PGDF obstacles is [Formula: see text]-hard with respect to the number of obstacles, and remains [Formula: see text]-hard after being restricted to a graph. Our reduction is based on a path encoding of MAXQHORNSAT and uses the risk of collision with an obstacle to encode variable assignments and literal satisfactions. This implies that, unlike in the known case, planning under uncertainty is hard, even when given a graph containing the solution. We further show by reduction from [Formula: see text]-SAT that both safe 3D motion planning among PGDF obstacles and the related minimum constraint removal problem remain [Formula: see text]-hard even when restricted to cases where each obstacle overlaps with at most a constant number of other obstacles.

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