Eric Joe Coyle

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Terrain variations can greatly influence autonomous ground vehicle (AGV) performance. However, if the terrain is properly identified, the AGV control systems can be adjusted to better suit the terrain. Current terrain classification techniques are largely based on vision and terrain dependent vehicle reactions. But at this time both methods have(More)
The need for terrain-dependent control systems on AGVs is evident when considering the variety of outdoor terrains many AGVs encounter. Although the idea of using terrain classification algorithms to identify the terrain and then update the control modes is well-established, the problem of how to intelligently update the control modes based on(More)
| The present approach to the MAE-based design of stack lters for image restoration does not always produce results which are visually pleasing. We show that this problem can be corrected by performing the l-tering in two stages, and by altering the weight-ing of errors in the mean absolute error criterion so it more closely matches a perceptual error(More)
The observations used to classify data from real systems often vary as a result of changing operating conditions (e.g. velocity, load, temperature, etc.). Hence, to create accurate classification algorithms for these systems, observations from a large number of operating conditions must be used in algorithm training. This can be an arduous, expensive , and(More)
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