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Dynamics modelling of an electric rope shovel for collision avoidance

University of British Columbia

In the surface mining industry, the machines used to extract materials from the earth are steadily increasing in size, making them difficult to manage safely. Rope shovels are used to excavate material and load the haul trucks that deliver the material for further processing. During the loading process, collisions between the rope shovel and the haul truck can occur. These collisions put the machine operators at risk and can damage the machines. The research presented in this thesis was conducted as part of a larger project with the aim of developing the sensing and control systems necessary for a rope shovel collision avoidance system. A sensor suite was developed to measure the joint positions and motor currents and voltages on a full scale rope shovel. A dynamics model was developed to model the machine’s dynamics. This model captured unique mechanical characteristics of the P&H machine. Using joint positions and motor currents collected with the sensor suite, a method was developed for estimating the inertia, mass, and friction parameters of the machine. A significant benefit of this estimation method was that it did not require the use of potentially noisy velocity and acceleration data obtained through successive derivatives of the position data. Using the solved inertia, mass, and friction parameter estimates, the dynamics model was able replicate the motions of the rope shovel with a mean absolute error of 3.3◦ for the dipper handle angle and 0.9◦ for the swing angle. Finally, an algorithm was proposed that used the dynamics model to forecast the motion of the machine with the aim of predicting collisions and generating warnings for the operator. The algorithm used the amount of time before a collision and the time required for the machine to stop swinging to decide how to warn the operator. The research presented in this thesis forms a strong basis for developing future rope shovel safety and control systems.

Text

2011-04-05

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/33279

Electrical and Computer Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/