Abstract:
Hydraulic excavators are widely used in the construction and mining industries. While conventional hydraulic excavators have been manually operated by a human operator, automated systems are being developed as an effective alternative to manual operations for common tasks that excavators routinely perform in typical work sites. An example is an automated ground grading system that can enhance the productivity of an excavator by assisting an operator to perform ground grading in a fast and accurate manner. For this purpose, a sliding mode controller is developed for automated grading of a hydraulic excavator in this research. First, an excavator manipulator model is developed in Simulink by using SimMechanics and SimHydraulics toolboxes. Then, the sliding mode controller is used to control the manipulator to trace a predefined trajectory for a grading task. The simulation results show that the sliding mode controller can control the grading operation with less tracking errors than a PI controller. As an alternative to conventional displacement sensors in an automated excavation system, a novel approach to estimate the position of a hydraulic manipulator using a neural network-based vision system is also studied in this research. A webcam is used to capture images of a moving manipulator, and the captured images are used to train neural networks. Then, the trained neural networks can be used to estimate the position of the excavator manipulator for a feedback control system. A case study was conducted to investigate the factors that affect the performance of the neural networks. A simulation study shows a stable grading performance when a PI controller is used to control the manipulator based on the estimated manipulator position.
