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Steps to Simulate Autonomous Earthmoving Algorithms

Using the example of an excavator, this overview shows how to develop autonomous motion planning algorithms and control systems and how to validate these algorithms in 3D scenario simulations using MATLAB® and Simulink®.

Introduction:

Explore the world of simulating earthmoving algorithms seamlessly with MATLAB. Dive into the intricacies of motion planning and collision prevention.

URDF Exporter Simplifies Motion Simulation

The URDF Exporter from SOLIDWORKS streamlines the representation for motion simulation tasks. This open-source tool efficiently exports kinematic motion data, enhancing the simulation process.

Mapping Out Autonomous Earthmoving

Understand the importance of having a detailed map of the environment for effective collision prevention strategies. Dive into utilizing lidar data for detecting obstacles and ensuring a safe working environment.

Enhancing Point Cloud Measurements

Leverage CAD mesh for precise point cloud measurements, enabling accurate identification of objects like houses and dumpsters. Learn the significance of incorporating lidar technology for excavator detection.

Simulation Strategies for Earthmoving

Discover the process of converting point cloud data into dynamic 3D occupancy maps for realistic excavator simulations. Explore the intricate motion planning involving inverse kinematic solvers and collision-free motion planners.

Customized Planners for Efficiency

Learn how to customize planners tailored to specific earthmoving applications. Dive into the optimization-based trajectory planning, where you can insert custom state spaces and validate functions for complex excavator systems.

Conclusion:

Master the art of simulating autonomous earthmoving algorithms with MATLAB, unlocking a realm of possibilities in the world of robotics and automation.

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