A geometry-based slip prediction model for planetary rovers. (September 2020)

• Record Type: Journal Article
• Authors: Ma, Hao; Yang, Huan; Li, Qunzhi; Liu, Shaochuang
• Is Part Of: Computers & electrical engineering. Volume 86(2020)
• Publication Date: 2020-09
• Subjects: Slip prediction -- Visual information -- Terrain geometry -- Slip continuity -- Planetary rovers; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2020.106749
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: This method of predicting slip mainly relies on non-contact measurement of on-board cameras. Parameters describing driving continuity are introduced to improve the model. Experiments use data with the best measuring accuracy and sampling density so far. Abstract: Wheeled robots commonly undergo slip in various types of natural terrains, particularly in planetary exploration missions. Therefore, it is important to predict slip from a distance for planetary rovers. However, the classical geometry-based slip model has barely been studied in depth in recent years and still has limitations. This paper studies the possibility of model improvement and analyzes the prediction potential of this method. For this purpose, three new input variables, namely, the step differences of pitch and roll angles and the slip of the previous step, are incorporated into the classical model. Based on more accurately measured and densely sampled dataset, these new variables are all proven effective through a series of experiments using different algorithms. The experimental analysis also shows that the slip prediction accuracy can reach a comparable level to the measurement error of the slip. This means that the improved model can achieve the best prediction accuracy, and this method is good enough. Graphical abstract: Image, graphical abstract