Quadcopters: From System Modeling to Real-Time Simulator

Presenter: John Straetmans, Computer Engineering Student, University of Michigan

This project attempts to build an accurate real-time (RT) drone simulator through the full integration of a 1D functional model of a drone created in Altair Activate®, along with its corresponding geometry, into Unreal Engine via the Functional Mock-up Interface (FMI) standard. Then, VR, peripheral controllers, and other functionalities were added to the representation. This task was accomplished by modifying the Altair RT Vehicle Package, making it able to handle not just vehicles, but any system model located in an FMU for co-simulation, in this case a quadcopter model. Once the FMU containing the Altair Activate® drone model was successfully loaded into Unreal Engine, the tools provided by the application allow additional features to be added, such as VR support. By implementing an FMU, together with its geometry, into Unreal Engine, we can visually analyze the dynamics of the system to further verify the drone model and its performance. In the future, this integration process should be facilitated to automatically load any FMU following just a few steps.

All Inspire,Activate,Compose,Embed Videos

Altair Model-Based Development Customer Stories from 2019 Global ATC

These success stories illustrate how customers are leveraging Altair's Math & Systems technology for Model-Based Development to develop better products, faster. Simulations involve 3D, 1D, and/or 0D modeling approaches based on the integrated use of Altair MotionSolve™, Altair Activate™, and/or Altair Compose™.

Presentations, Videos

Altair Activate Key Capabilities

Playlist highlighting the key capabilities of Altair Activate

Videos

Altair Embed Key Capabilities

Playlist featuring the key capabilities of Altair Embed

Videos

Solid Modeling of a Mechanical Part

A short workflow illustrating the power of solid modeling and editing in Inspire Studio, applied to a junction pipe with flanges.

Videos

Create and Control NURBS Curves & Surfaces

Utilize Non-uniform Rational B-Splines (NURBS) curves and surfaces to accurately represent even the most complex shapes with flexibility and precision.

Videos

Multi-Disciplinary Evaluation Of Vehicle Platooning Scenarios

Presenter: Christian Kehrer, Business Development Manager, Altair

This presentation discusses the multi-disciplinary evaluation of truck platooning, with the lead truck sending out acceleration, braking and steering signals for the following trucks to react accordingly. The benefits address safety requirements, fuel savings, traffic capacity and convenience. The presentation demonstrates why platooning requires a holistic approach in the sense of connecting different modeling and simulation methods for a virtual evaluation of this system of systems.

Presentations, Videos

Exoskeleton Modeling Using MotionSolve & Activate

Presenter: Nino Michniok, Mechanical Engineering Student, University of Kaiserslautern

The first part of the presentation shows the detailed process of building the multibody system of an actuated exoskeleton in MotionView/MotionSolve (MV/MS). The required movements are transferred to the corresponding joints by “Motions”. By this the exoskeleton can Stand Up, Walk diagonally across the floor and Sit Down. In the second part the “Motions” in MV/MS are replaced by controllers (position control) whichdeliver a certain torque to actuate the exoskeleton. The main topic here is the implementation of the co-simulation between Activate and MV/MS. In the end the presentation gives a quick outlook of similar works at the University of Applied Sciences Kaiserslautern in Germany.

Presentations, Videos

Deep Reinforcement Learning for Robotic Controls

Presenter: Dario Mangoni on behalf of Alessandro Tasora, Engineering Professor and Digital Dynamics Lab Leader, University of Parma

This presentation address the use of the Proximal Policy Optimization (PPO) deep reinforcement learning algorithm to train a Neural Network to control a robotic walker and a robotic arm in simulation. The Neural Network is trained to control the torque setpoints of motors in order to achieve an optimal goal.

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