Tractable robot simulation for terrain leveling
2017
This thesis describes the problem of terrain leveling, in which one or more robots or
vehicles are used to
atten a terrain. The leveling operation is carried out either in
preparation for construction, or for terrain reparation. In order to develop and prototype
such a system, the use of simulation is advantageous. Such a simulation requires
high fidelity to accurately model earth moving robots, which navigate uneven terrain
and potentially manipulate the terrain itself. It has been found that existing tools
for robot simulation typically do not adequately model deformable and/or uneven
terrain. Software which does exist for this purpose, based on a traditional physics
engine, is difficult if not impossible to run in real-time while achieving the desired
accuracy. A number of possible approaches are proposed for a terrain leveling system
using autonomous mobile robots. In order to test these approaches in simulation, a
2D simulator called Alexi has been developed, which uses the predictions of a neural
network rather than physics simulation, to predict the motion of a vehicle and changes
to a terrain. The neural network is trained using data captured from a high-fidelity
non-real-time 3D simulator called Sandbox. Using a trained neural network to drive
the 2D simulation provides considerable speed-up over the high-fidelity 3D simulation,
allowing behaviour to be simulated in real-time while still capturing the physics of
the agents and the environment. Two methods of simulating terrain in Sandbox are
explored with results related to performance given for each. Two variants of Alexi
are also explored, with results related to neural network training and generalization
provided.
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