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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.

Due to the high demand of fuel efficient construction equipment, significant research effort has been dedicated to improving excavator efficiency. Among various possibilities, methods to recuperate energy during cab swing motion have been widely examined. Electric and hydraulic hybrids designs have shown to greatly improve fuel efficiency but require drastic design changes. The redesigned systems thus require many hours of operation to offset the manufacturing costs with fuel savings. In this research, a relatively simple swing energy recuperation system is presented using an additional accumulator, fixed displacement hydraulic motor, and control valves. With the system, hydraulic fluid is stored in an accumulator, and a simple controller opens a valve to allow the stored energy to assist the engine in running the main pumps.

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.