MATLAB and Simulink for Offroad Vehicles

Learn how to create virtual testbenches for developing and validating control and autonomy of heavy machinery.

Engineers who develop advanced control, automation, and autonomous capabilities for heavy equipment in industries such as construction, mining, agriculture, and forestry can follow this tutorial and examples to build a virtual testbench for offroad vehicles.

With this virtual testbench, you can use the combination of high-fidelity physical modeling, kinematic design, and photorealistic virtual simulation to refine and validate advanced control, automation, and autonomous algorithms for offroad vehicles, ensuring their reliable performance before deployment.

Download Offroad Autonomy Support Package

Advanced Control, Automation, and Autonomy Design for Offroad Vehicles

Top 10 Use Cases

Generate synthetic sensor data from scenario simulations to develop control and autonomous algorithms.
Enhance controller design with high-fidelity IMU and GPS models, capturing real-world effects.
Fuse sensor data for offroad vehicle pose estimation and navigation using INS and GNSS.
Generate paths for complex terrain maneuvers, considering vehicle kinematics and obstacles.
Design trajectory-tracking controllers that respect velocity, acceleration, and actuator constraints.
Simulate automated earthmoving with excavators using inverse kinematics and Lidar for motion control.
Visualize offroad vehicle motion in photorealistic 3D environments using Unreal Engine^®.
Test control designs in hardware-in-the loop (HIL) simulations with Speedgoat.
Simulate dynamic objects to model realistic interactions with offroad vehicles in scenario simulations.
Deploy and validate control and autonomy algorithms on embedded hardware.