Future lunar/planetary exploration missions will demand mobile robots with the capability of reaching more challenging science targets and driving farther per day than the current Mars rovers. Among other improvements, reliable slippage estimation and compensation strategies will play a key role in enabling a safer and more efficient navigation. This paper reviews and discusses this body of research in the context of planetary exploration rovers. Previously published state‐of‐the‐art methods that have been validated through field testing are included as exemplary results. Limitations of the current techniques and recommendations for future developments and planetary missions close the survey.

SUMMARYIn this paper, we present the work related to the application of a visual odometry approach to estimate the location of mobile robots operating in off-road conditions. The visual odometry approach is based on template matching, which deals with estimating the robot displacement through a matching process between two consecutive images. Standard visual odometry has been improved using visual compass method for orientation estimation. For this purpose, two consumer-grade monocular cameras have been employed. One camera is pointing at the ground under the robot, and the other is looking at the surrounding environment. Comparisons with popular localization approaches, through physical experiments in off-road conditions, have shown the satisfactory behavior of the proposed strategy.

This paper presents a new methodology where machine learning is used for detecting various levels of slip in the context of planetary exploration robotic missions. This methodology aims at employing proprioceptive rover sensor signals. Consequently, no operational complexity is added to the rover's commanding and it is independent of lighting conditions. Two supervised learning methods (Support Vector Machines and Artificial Neural Networks) are compared to two unsupervised learning approaches (K‐means and Self‐Organizing Maps (SOM)). Physical experiments using a single‐wheel testbed equipped with an MSL spare wheel and a real planetary exploration rover validate the implemented methodology. Performance is evaluated in terms of well‐known metrics both considering single data points and subsets of consecutive data points (moving median filter). Computation time and storage requirements are also examined. One of the SOM‐based algorithms, semantic SOM method, demonstrates a proper balance between the benefits of supervised learning algorithms (high success rate, >96%) and the advantages of unsupervised learning methods (low storage requirements, 5 kb, and no need of manually‐labeled training data). This paper also addresses the most convenient placement of IMU sensors on the rover chassis such that slippage detection is maximized.

No abstract

No abstract