Mobile robots are increasingly being used in high-risk, rough terrain situations, such as planetary exploration and military applications. Current control and localization algorithms are not well suited to rough terrain, since they generally do not consider the physical characteristics of the vehicle and of its environment. Poor attention has been devoted to the study of the dynamic ill-effects occurring at the wheel-terrain interface, such as slip and sinkage. These effects compromise odometry accuracy and traction performances leading to danger of entrapment with consequent mission failure. This paper describes methods for wheel slippage and sinkage detection aiming at improving vehicle mobility on highly challenging terrain. Novel measures for wheel slip detection are presented based on observing different sensor modalities implemented onboard and defining deterministic conditions for vehicle slippage. A vision-based algorithm for wheel sinkage estimation is also discussed based on edge detection strategy. Experimental results, obtained by a Mars rover-type robot operating in a rough-terrain environment, are presented. It is shown that these techniques are effective in detecting the dynamic effects due to wheel-terrain interaction and can lead to an efficient understanding of the vehicle physical behavior.

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