From the series: MATLAB and Simulink Robotics Arena
Sebastian Castro, MathWorks
Pulkit Kapur, MathWorks
Sebastian Castro and Pulkit Kapur discuss how to deploy algorithms from MATLAB® and Simulink® to be used standalone in the Robot Operating System (ROS).
First, Sebastian and Pulkit introduce two workflows for deploying algorithms as standalone ROS nodes. The first approach involves generating C/C++ code from files and manually writing C++ code that is compatible with ROS. The second approach combines code generation tools with Robotics System Toolbox™ to automatically generate and run executable ROS nodes.
This video will focus on the second approach listed above. Find out how you can use MATLAB and Simulink to automate the deployment process, as well as how to interact with generated code from MATLAB and Simulink to test algorithms, tune parameters, view data, and more.
Finally, you will see a demonstration of the concepts above through an autonomous object tracking example. This example covers several steps of the deployment workflow, as well as highlights the modularity of MATLAB and ROS by showing the deployed algorithm using real and simulated TurtleBot® robotic platforms.
Introduction to Robotic Systems Meet MATLAB and Simulink Robotics Arena team members Sebastian Castro and Connell D’Souza as they discuss designing a robotic system and the support provided to robotics student competition teams.
Introduction to Contact Modeling, Part 1 Sebastian Castro and Ed Marquez Brunal introduce the fundamentals of mechanical contact modeling and simulation with Simulink, as well as show examples for automotive and robotics applications.
Introduction to Contact Modeling, Part 2 Sebastian Castro and Ed Marquez Brunal discuss various approaches and online resources for modeling mechanical contact and friction forces using Simulink, Simscape, and Simscape Multibody.
Direction of Arrival with MATLAB Stephen Cronin from the Robotics Association at Embry-Riddle Aeronautical University demonstrates how to detect the direction of arrival of an underwater acoustic signal using MATLAB.
Walking Robots, Part 1: Modeling and Simulation Join Sebastian Castro as he shows you how to model a two-legged walking robot, including joint motion actuation and contact forces, using Simscape Multibody.
Walking Robots, Part 2: Actuation and Control Join Sebastian Castro as he shows you how you can use Simulink and the Simscape product family to connect a walking robot model to detailed actuator models with motion planning and control algorithms.
Walking Robots, Part 3: Trajectory Optimization Join Sebastian Castro as he shows you how you can use MATLAB and the Global Optimization Toolbox to find optimal motion trajectories for a Simulink model of a walking robot.
Real-Time Beat Tracking Challenge Jeremy Bell, Angus Keatinge, and James Wagner of The University of New South Wales (UNSW Sydney) discuss their team’s winning entry to the IEEE Signal Processing Cup 2017.
Getting Started with MATLAB and ROS Join Sebastian Castro and Pulkit Kapur as they show how Robotics System Toolbox can help you connect MATLAB and the Robot Operating System (ROS).
Getting Started with Simulink and ROS Join Sebastian Castro and Pulkit Kapur as they show how Robotics System Toolbox can help you connect Simulink and the Robot Operating System (ROS).
Deploying Algorithms to ROS Join Sebastian Castro and Pulkit Kapur as they show how automatic code generation tools can help you deploy algorithms developed in MATLAB and Simulink to run in the Robot Operating System (ROS).
Building Interactive Design Tools Build interactive design tools to reduce development time. Zachary Leitzau from Embry-Riddle Aeronautical University demonstrates the use of a self-built app to help design a model airplane.
Simulating Quadcopter Missions Simulation is a great way to test and tune control algorithms for quadcopters. Julien Cassette talks about using Simulink, Robotics Operating System (ROS), and Gazebo to simulate quadcopter missions from student competitions.
Optimizing Airframe Sizing Follow Joshua Williams from Cornell University Unmanned Air Systems (CUAir) as he demonstrates the use of a genetic algorithm to optimize airframe sizing for model airplanes.
Building Apps with MATLAB and App Designer Build apps with MATLAB to automate repetitive interactive code. Sebastian Castro and Connell D'Souza from the Robotics Arena demonstrate building interactive apps using App Designer.
Designing Distributed Systems with ROS Join Sebastian Castro and Connell D’Souza as they discuss techniques in Simulink to design and deploy multirate and multiplatform robotics algorithms with the Robot Operating System (ROS).
Designing Robot Manipulator Algorithms Accelerate the design of robot manipulator algorithms by using the Robotics Systems Toolbox functionality and integrating robot models with simulation tools to program and test manipulation tasks.
Introduction to Filter Design Join Mark Schwab and Connell D'Souza as they demonstrate the use of the Filter Designer app and interactively design filters for digital signal processing that can be implemented in MATLAB or Simulink.
From Data to Model Create a model for a piece of hardware from input and output data using the System Identification app. Connell D'Souza and Kris Fedorenko explain the workflow from data gathering to model evaluation.
Controlling Robot Manipulator Joints Learn how MATLAB, Simulink, and Robotics System Toolbox can help you design joint torque controllers for robotic manipulation and grasping tasks.
Getting Started with the Mobile Robotics Simulation Toolbox Learn how to work with the Mobile Robotics Simulation Toolbox on the MATLAB Central File Exchange.
MATLAB Apps with ROS Learn how to design interactive MATLAB apps to communicate with ROS enabled robots and simulators.
Robotics Education with MATLAB Professor Peter Corke and Sebastian Castro discuss how MATLAB and Simulink can be used in robotics education.
Programming Robot Swarms Explore how to use MATLAB and Simulink for prototyping and implementation of robot swarm behavior.
Using Ground Truth for Object Detection, Part 1 Use the Ground Truth Labeler app to generate quality ground truth data that can be used to train and evaluate object detectors.
Using Ground Truth for Object Detection, Part 2 Use labeled ground truth data to train and evaluate object detectors.
Deep Learning with NVIDIA Jetson and ROS Learn how GPU Coder can be used to deploy deep learning algorithms from MATLAB to embedded NVIDIA GPUs, and how the deployed code can be used with the Robot Operating System (ROS).
Sensor Fusion for Orientation Estimation Join Roberto Valenti and Connell D’Souza as they discuss using Sensor Fusion and Tracking Toolbox to perform sensor fusion for orientation estimation.
Modeling Pneumatic Robot Actuators, Part 1 Veer and Maitreyee show how you can model a pneumatic system by using physical blocks available in Simscape.
Modeling Pneumatic Robot Actuators, Part 2 Veer and Maitreyee show you how to build a throwing mechanism to throw a ball at a certain target using Simscape Multibody.
Modeling Pneumatic Robot Actuators, Part 3 Veer and Maitreyee first show how you can extend Simscape Multibody throwing mechanism models with physical effects modeled in Simscape. Later, controller is implemented in the system to track the reference piston position.
Autopilot Development Using Model-Based Design Claudio Conti of Sapienza Flight Team at Sapienza University of Rome joins Connell D’Souza to talk about using Model-Based Design and Real-Time Simulation to design a custom autopilot.
LQR Control of an Autonomous Underwater Vehicle Learn the basics of implementing a Linear-Quadratic Regulator (LQR) controller for an autonomous underwater vehicle with Juan Rojas and Nathan Liebrecht of the Autonomous Robotic Vehicle Project.
Buoy Detection Using Simulink In this video, we will demonstrate how to perform Buoy Detection using Simulink. This video has been designed for use in the AUVSI RoboBoat and RoboSub competitions.
Ball Tracking with a Desktop Computer In this session you’ll learn how to deploy MATLAB® and Simulink® onto a desktop computer for the purpose of controlling an Unmanned Vehicle System in student competitions.
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