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A neural network-based computer vision system is developed to estimate position of an excavator manipulator in real time. A camera is used to capture images of a manipulator, and the images are down-sampled and used to train a neural network. Then, the trained neural network can estimate the position of the excavator manipulator in real time. To study the feasibility of the proposed system, a webcam is used to capture images of an excavator simulation model and the captured images are used to train a neural network. The simulation results show that the developed neural network-based computer vision system can estimate the position of the excavator manipulator with an acceptable accuracy.

Hydraulic excavators perform numerous tasks in the construction and mining industry. Although ground grading is a common task, proper grading cannot easily be achieved. Grading requires an experienced operator to control the boom, arm, and bucket cylinders in a rapid and coordinated manner. Due to this reason, automated grade control is being considered as an effective alternative to conventional human-operated ground grading. In this paper, a path-planning method based on a 2D kinematic model and inverse kinematics is used to determine the desired trajectory of an excavator's boom, arm, and bucket cylinders. Then, the developed path planning method and PI control algorithms for the three cylinders are verified by a simple excavator model developed in Simulink®. The simulation results show that the automated grade control algorithm can grade level or with reduced operation time and error.