Methods for Wheel Slip and Sinkage Estimation in Mobile Robots

Future outdoor mobile robots will have to explore larger and larger areas, performing difficult tasks, while preserving, at the same time, their safety. This will primarily require advanced sensing and perception capabilities. Video sensors supply contact-free, precise measurements and are flexible devices that can be easily integrated with multi-sensor robotic platforms. Hence, they represent a potential answer to the need of new and improved perception capabilities for autonomous vehicles. One of the main applications of vision in mobile robotics is localization. For mobile robots operating on rough terrain, conventional dead reckoning techniques are not well suited, since wheel slipping, sinkage, and sensor drift may cause localization errors that accumulate without bound during the vehicle’s travel. Conversely, video sensors are exteroceptive devices, that is, they acquire information from the robot’s environment; therefore, vision-based motion estimates are independent of the knowledge of terrain properties and wheel-terrain interaction. Indeed, like dead reckoning, vision could lead to accumulation of errors; however, it has been proved that, compared to dead reckoning, it allows more accurate results and can be considered as a promising solution to the problem of robust robot positioning in high-slip environments. As a consequence, in the last few years, several localization methods using vision have been developed. Among them, visual odometry algorithms, based on the tracking of visual features over subsequent images, have been proved particularly effective. Accurate and reliable methods to sense slippage and sinkage are also desirable, since these effects compromise the vehicle’s traction performance, energy consumption and lead to gradual deviation of the robot from the intended path, possibly resulting in large drift and poor results of localization and control systems. For example, the use of conventional dead-reckoning technique is largely compromised, since it is based on the assumption that wheel revolutions can be translated into correspondent linear displacements. Thus, if one wheel slips, then the associated encoder will register revolutions even though these revolutions do not correspond to a linear displacement of the wheel. Conversely, if one wheel skids, fewer encoder pulses will be counted. Slippage and sinkage measurements are also valuable for terrain identification according to the classical terramechanics theory. This chapter investigates vision-based onboard technology to improve mobility of robots on natural terrain. A visual odometry algorithm and two methods for online measurement of vehicle slip angle and wheel sinkage, respectively, are discussed. Tests results are presented showing the performance of the proposed approaches using an all-terrain rover moving across uneven terrain.