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Construction equipment machines today benefits from hydraulic system due to high power density. And, the development of an excavator using “open-center system with spool valves”, in general, requires iterative hardware design tuning activities for optimized performance and fuel economy while matching operator’s commands. Instead of traditional hydraulic and multi-body dynamic simulation with an operator simulation model, this paper focuses on the methodology development of real-time simulation model for an excavator, including the hydraulic system of an excavator’s boom, arm, bucket, and travel as well as multi-body dynamic system. The real-time capability is realized by reducing unnecessary compressible units and achieving numerical stability at sudden pressure changes when valves open and close. The real-time simulation model has been verified later with an actual Volvo CE excavator machine, and the correlation was quite satisfactory.

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.