Hydraulic machines used in mining and excavation applications are nonlinear systems. Apart from the nonlinearity due to the dynamic coupling between the different links there are significant actuator nonlinearities due to the inherent properties of the hydraulic system. Optimal motion planning for these machines, i.e., planning motions that optimize a user-selectable combination of criteria such as time. energy, etc., would help the designers of such machines, besides aiding the development of more productive robotic machines. Optimal motion planning in turn requires fast (computationally efficient) machine models in order to be practically usable. This work proposes a method for constructing hydraulic machine models using memory-based learning. We demonstrate the approach by constructing a machine model of a 25-ton hydraulic excavator with a 10 m maximum reach. The learning method is used to construct the hydraulic actuator model and is used in conjunction with a linkage dynamic model to construct a complete excavator model that is much faster than an analytical model. Our test results show an average bucket tip position prediction error of 1 m over 50 sec of machine operation. This is better than any comparable speed model reported in the literature. The results also show that the approach effectively captures the interactions between the different hydraulic actuators. The excavator model is used in a time-optimal motion planning scheme. We demonstrate the optimization results on a real excavator testbed to underscore the effectiveness of the model for optimal motion computation.
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