Real-time manipulator position sensing for automation of hydraulic excavators

Automation of hydraulic excavators is valuable due to their potential applications in hazardous environments or remote locations, such as radioactive-contaminated areas. Manipulator position sensing is a key issue in the study of hydraulic excavator automation.

A neural network-based computer vision system was designed using MathWorks' MATLAB neural network toolbox and used to estimate the boom, arm, and bucket cylinder displacements of an excavator manipulator during a grading operation simulation. A computer ran the excavator simulation and a webcam connected to the computer took snapshots of the excavator manipulator animation displayed on a secondary screen. The webcam took screenshots of the manipulator at different positions during a grading operation. Those images were then down-sampled and used to train the neural network. The researchers from Volvo Construction Equipment and The University of Alabama then compared the manipulator positions estimated by the neural network-based computer vision system with the actual values.