May 15–17, 2017 in Prague, Czech Republic
[Proceedings] [Sessions] [Authors] [Schedule] [Further material]

Papers by Francesco Casella:

Title: Simulation of Large Grids in OpenModelica: reflections and perspectives
Authors: Francesco Casella, Alberto Leva and Andrea Bartolini
Abstract:This paper belongs to a long-term research activity on modelling and simulation of large grids in Modelica, and specifically with the OpenModelica translator. We describe the present state of the research, its evolution over the last year, the conclusions we could reach in this period in comparison with the initial hypotheses, and some results. As a consequence, we outline the future of the presented activity.
Links: Full paper

Title: Using Modelica for advanced Multi-Body modelling in 3D graphical robotic simulators
Authors: Gianluca Bardaro, Luca Bascetta, Francesco Casella and Matteo Matteucci
Abstract:This paper describes a framework to extend the 3D robotic simulation environment Gazebo, and similar ones, with enhanced, tailor-made, multi-body dynamics specified in the Modelica language. The body-to-body interaction models are written in Modelica, but they use the sophisticated collision detection capabilities of the Gazebo engine. This contribution is a first step toward the simulation of complex robotics systems integrating detailed physics modelling and realistic sensors such as lidar and cameras. A proof-of-concept implementation is described in the paper integrating Gazebo collider and the Modelica MultiBody library, and the results obtained when simulating the interaction of an elastic sphere with a rigid plane are shown.
Links: Full paper

Title: Solving large-scale Modelica models: new approaches and experimental results using OpenModelica
Authors: Willi Braun, Francesco Casella and Bernhard Bachmann
Abstract:Modelica-based modeling and simulation is becoming increasingly important for the development of high quality engineering products. Therefore, the system size of interest in a Modelica-based simulation is continously increasing and the traditional way of generating simulation code, e.g. involving symbolic transformations like matching, sorting, and tearing, must be adapted to this situation. This paper describes recently implemented sparse solver techniques in OpenModelica in order to efficiently compile and simulate large-scale Modelica models. A proof of concept is given by evaluating the performance of selected benchmark problems.
Links: Full paper