Robotics researchers and engineers use MATLAB and Simulink to design, simulate, and verify every aspect of autonomous systems, from perception to motion.
- 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.
“Model-Based Design and automatic code generation enable us 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 controllers for such a complex robotic system with hard real-time performance.”
Design the Hardware Platform
Create a 3D physical model or an 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 dynamics, contacts, hydraulics, and pneumatics.
- Complete the digital twins by adding an electrical diagram layer.
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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.
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 for deployment to hardware.
Planning and Decision Making
Use an actively maintained algorithm library to implement 2D or 3D path planning for a robot that is either defined as a point mass or a system with kinematic and dynamic constraints. Perform task planning with Stateflow®, defining the conditions and actions needed for decision making in real time.
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Communicating with Platforms and Targets
Deploy autonomous algorithms to ROS-based systems and microcontrollers such as Arduino® and Raspberry Pi™. Communicate with embedded targets via protocols, including CAN, EtherCAT®, 802.11™, TCP/IP, UDP, I2C, SPI, MODBUS®, and Bluetooth®.
Hardware Interface Supports
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“With MATLAB and Simulink we can use a single environment for control algorithm development, debugging, data analysis, and more—instead of switching between multiple tools. That integration reduces overall project development time and the chances of introducing errors.”
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