Offroad Autonomy for Heavy Machinery
Develop autonomous systems for offroad vehicles and heavy machinery in construction, agriculture, and mining applications with the Robotics System Toolbox™ Offroad Autonomy Library. This support package provides specialized tools for designing, simulating, and testing autonomy algorithms. Through integration with Unreal Engine®, this support package enables photorealistic scenario simulations to test and refine the performance of offroad vehicles, such as dump trucks and backhoes, under diverse conditions.
To download the Robotics System Toolbox Offroad Autonomy Library support package, see Install Robotics System Toolbox Offroad Autonomy Library Support Package.
For more information about using Unreal Engine with Robotics System Toolbox, see High-Fidelity Simulation.
Functions
offroadControllerMPPI | Local path planner for offroad vehicles and heavy machinery using MPPI algorithm (Since R2024b) |
articulatedSteeringKinematics | Articulated steering vehicle model (Since R2025a) |
traversabilityMap | Create traversability map using digital elevation data of terrain (Since R2025a) |
Blocks
Scenes
Offroad Pit Mining Scene | Offroad pit mining scene in Unreal Engine environment |
Construction Site Scene | Construction site scene in Unreal Engine environment |
Topics
Reference Applications
- Offroad Navigation for Autonomous Haul Trucks in Open Pit Mine
Series shows how to create a set of planners to enable autonomous haul trucks to navigate uneven terrain and avoid obstacles. (Since R2024a)
- STEP 1: Create Route Planner for Offroad Navigation Using Digital Elevation Data
- STEP 2: Create Onramp and Terrain-Aware Global Planners for Offroad Navigation
- STEP 3: Navigate Global Path Through Offroad Terrain Using Local Planner
- STEP 4: Create Path Following Model Predictive Controller
- STEP 5: Model and Control Autonomous Vehicle in Offroad Scenario
Support Package Set Up
- Install Robotics System Toolbox Offroad Autonomy Library Support Package
Use Add-On for designing, simulating, and validating algorithms for offroad heavy machinery.