A Survey of Off-Road Mobile Robots: Slippage Estimation, Robot Control, and Sensing Technology

Wheeled field robots have promising widespread civil and military applications in off-road conditions. The complex off-road working conditions pose substantial challenges in applying field robots, especially the newly emerged fully autonomous "smart" mobile ground robots without human intervention. Two key issues arise for a successful navigation, namely geometrical and non-geometrical obstacles. So far, most of the research on off-road mobile robots has focused on approaches to avoid geometrical obstacles by ignoring the influence of wheel slippage on the robot mobility. On the other hand, wheel slippage plays a key role in the performance of off-road mobile robots and must be addressed. The purpose of this paper is to summarize the methods in the study on the wheel slippage estimation and modeling of off-road mobile robots, along with the control and sensing technology, and report the state-of-the-art in this research field. Multiple approaches: proprioceptive-sensor-based, exteroceptive-sensor-based, model-based slippage estimation and compensation methods will be discussed. In addition, this paper also investigates the new research trend of developing machine learning methods to predict and compensate for the wheel slippage under complex working conditions. Current technical challenges and room for further research improvement are also generalized. This paper will help researchers new in this field to be familiar with the wheel slippage modeling, control and sensing technology, and inspire for the next-generation robot design that can help address the wheel slippage issues.