How can an Arduino interact with Matlab

Robotics and Autonomous Systems


MATLAB and Simulink in the field of robotics and autonomous systems

Develop autonomous applications: from perception to movement and optimization of behavior at the system level

Robotics scientists and engineers use MATLAB® and Simulink®to design, simulate and verify every aspect of autonomous systems - from perception to movement.

  • Modeling robotic systems down to the smallest details such as sensor noise and motor vibration.
  • Simulation of robotic systems with precise kinematics, dynamics and contact properties.
  • Design and optimization of both high-level autonomy and low-level control.
  • Synthesis and analysis of sensor data with a well-maintained library of algorithms.
  • Step-by-step verification of the robot design or algorithm, from simulation to hardware-in-the-loop (HIL) test.
  • Provision of algorithms on robots via ROS or directly on microcontrollers, FPGAs, PLCs and graphics cards.

“With model-based design and automatic code generation, we are able to cope with the complexity of Agile Justin's 53 degrees of freedom. Without model-based design, it would have been impossible to build the controls for such a complex robot system while taking the hard real-time performance into account. "

Berthold Bäuml, German Aerospace Center (DLR)

Design of the hardware platform

Generate a 3D physical or electromechanical model of autonomous vehicles, drones, and manipulators for simulation, optimization, and reinforcement learning of control algorithms.

  • Import existing 3D models from URDF files or CAD software.
  • Make the model physically accurate by implementing the dynamics, contacts, hydraulics and pneumatics.
  • Adding an electrical diagram layer completes the digital twins.


Processing of the sensor data

Implementation of algorithms for sensor data processing with powerful toolboxes in MATLAB and Simulink.

  • Connection to sensors via ROS, serial and other protocol types.
  • Visualization of data from cameras, sonar, LiDAR, GPS and IMUs. Automation of common sensor processing tasks such as sensor fusion, filtering, geometric transformation, segmentation and registration.


Perception of the environment

With the integrated interactive MATLAB apps, algorithms for object recognition and tracking as well as for localization and mapping can be implemented.

  • Experiment and evaluate different neural networks for image classification, regression and feature recognition.
  • The algorithms are automatically in C / C ++, Fixed Point, HDL or CUDA®Code converted for use in hardware.


Planning and decision making

Use a constantly updated algorithm library to implement 2D or 3D path planning for a robot that is defined either as a point mass or as a system with kinematic and dynamic requirements. Do the task planning with Stateflow® defining the conditions and actions required for decision making in real time.

Design of control systems

Use integrated interactive MATLAB apps to analyze the behavior of complex systems in the time and frequency domain. Design feedback controllers using the deterministic approach, the optimization approach, or the reinforcement learning approach.

Communication with platforms and goals

Set autonomous algorithms on ROS based systems and microcontrollers like Arduino® and Raspberry Pi ™ ready. Communicate with embedded targets using protocols such as CAN, EtherCAT®, 802.11 ™, TCP / IP, UDP, I2C, SPI, MODBUS® and bluetooth®.

Hardware interface support

“With MATLAB and Simulink, we can use a single environment to develop control algorithms, troubleshoot, analyze data, and more, instead of having to switch between multiple tools. Such an integration reduces the overall development time of a project as well as its susceptibility to errors. "

Dr. John Wen, Rensselaer Polytechnic Institute

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