Robotics

Robotics researchers and engineers use MATLAB and Simulink to design, simulate, and verify every aspect of autonomous systems, from perception to motion. With MATLAB and Simulink you can:

  • Model robotic systems down to the finest details such as sensor noise and motor vibration.
  • Simulate robotic systems with accurate kinematics, dynamics, and contact properties.
  • Design and optimize both high-level autonomy and low-level control.
  • Synthesize and analyze sensor data with a maintained library of algorithms.
  • Verify robot design or algorithm gradually, from simulation to hardware-in-the-loop (HIL) test.
  • Deploy algorithms to robots via ROS or directly to microcontrollers, FPGAs, PLCs, and GPUs. 

Route Planning and Navigation for Autonomous Robots

Simplify complex tasks of route planning and navigation for robotic road vehicles by using MATLAB and Simulink. This image shows how to simulate an autonomous robot by using only three components: a road, a vehicle model and a route following an algorithm.

Route Planning and Movement Sensor for Ground Robots e-book

Processing Sensor Data

Implement sensor data processing algorithms with powerful toolboxes in MATLAB and Simulink.

Connect to sensors through ROS, Serial, and other types of protocols.

Visualize data from cameras, sonar, LiDAR, GPS, and IMUs. Automate common sensor processing tasks such as sensor fusion, filtering, geometric transformation, segmentation, and registration.

Collecting Sensor Data

You can connect to sensor via ROS. Certain sensor like cameras, LiDAR and IMU’s have ROS messages that can be converted to MATLAB data types for analysis and visualization.

  • You can automate common sensor processing workflows such as importing and batch processing big data clusters, sensor calibration, reducing noise, geometrical transformation, segmentation and recording.

Perceiving the Environment

Use built-in interactive MATLAB apps to implement algorithms for object detection and tracking, localization and mapping.

Experiment and evaluate different neural networks for image classification, regression, and feature detection.

Automatically convert algorithms into C/C++, fixed-point, HDL, or CUDA code.

Built-in MATLAB apps allow interactive object detection and monitoring, movement prediction, 3D point cloud processing and sensor fusion.

Use deep learning to classify images, regression and learn characteristics with convolution neural networks (CNN).

Modelling and Simulation of Walking Robots video.

Planning and Decision Making

Create a map of an environment by using LiDAR sensor data via Simultaneous Localization and Mapping (SLAM). Explore limited environments by designing algorithms for route and movement planning. Use route planners to calculate an unobstructed route in any map.

Design algorithms to allow your robot to decide in the face of uncertainty and work safely in cooperation based environments.

Movement Planning with MATLAB e-book