Stochastic modeling for mobility estimation has vastly improved through recent research and the advancements in technology, but those advancements haven’t fully been applied to full vehicle mobility control and on-line (real time) analysis of each driving wheel’s contribution and influence.

This paper presents an analysis of fundamental mobility dynamics implemented into advanced stochastic estimation methodologies. Based on this analysis, the paper formulates: (i) developed on-line mobility criteria in stochastic conditions from one wheel to full vehicle with six driving wheels in which the contribution of every wheel can be estimated, and (ii) terrain characterization and agile vehicle dynamics information to estimate UGV mobility in real time.

This analysis enables on-line mobility estimation for UGVs to make control changes as the event of poor terrain conditions occur (or before it) rather than after the event, causing the vehicle to then optimize its reaction to regain control. These fundamental applications for mobility control in stochastic conditions enable today’s one wheel modeling solutions to be applied to the full vehicle.

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