It is important for Mars exploration rovers to achieve autonomous and safe mobility over rough terrain. Terrain classification can help rovers to select a safe terrain to traverse and avoid sinking and/or damaging the vehicle. Mars terrains are often classified using visual methods. However, the accuracy of terrain classification has been less than 90% in read operations. A high-accuracy vision-based method for Mars terrain classification is presented in this paper. By analyzing Mars terrain characteristics, novel image features, including multiscale gray gradient-grade features, multiscale edges strength-grade features, multiscale frequency-domain mean amplitude features, multiscale spectrum symmetry features, and multiscale spectrum amplitude-moment features, are proposed that are specifically targeted for terrain classification. Three classifiers, K-nearest neighbor (KNN), support vector machine (SVM), and random forests (RF), are adopted to classify the terrain using the proposed features. The Mars image dataset MSLNet that was collected by the Mars Science Laboratory (MSL, Curiosity) rover is used to conduct terrain classification experiments. The resolution of Mars images in the dataset is 256 × 256. Experimental results indicate that the RF classifies Mars terrain at the highest level of accuracy of 94.66%.

The underwater docking of autonomous underwater vehicles (AUVs) is conducive to energy supply and data exchange. A vision-based high-precision estimation of “the positions and poses of an AUV relative to a docking station” (PPARD) is a necessary condition for successful docking. Classical binarization methods have a low success rate in extracting guidance features from fuzzy underwater images, resulting in an insufficient stability of the PPARD estimation. Based on the fact that guidance lamps are blue strong point light sources, this study proposes an adaptive calculation method of binary threshold for the guidance image. To decrease the failure of guidance feature extraction, a guidance image enhancement method is proposed to strengthen the characteristic that the guidance lamps are strong point light sources with a certain area. The PPARD is estimated through solving the minimum value of the imaging error function for the vision-based extracted guidance features. The experimental results showed that the absolute estimation error for each degree of freedom in the PPARD was at most 10%, which was lower than that of the orthogonal iteration (OI) method. In addition, the proposed guidance feature extraction method proved to be better than the classical methods, with the extraction success rate reaching 87.99%.

Identifying terrain parameters is important for high-fidelity simulation and high-performance control of planetary rovers. The wheel–terrain interaction classes (WTICs) are usually different for rovers traversing various types of terrain. Every terramechanics model corresponds to its wheel–terrain interaction class (WTIC). Therefore, for terrain parameter identification of the terramechanics model when rovers traverse various terrains, terramechanics model switching corresponding to the WTIC needs to be solved. This paper proposes a speed-independent vibration-based method for WTIC recognition to switch the terramechanics model and then identify its terrain parameters. In order to switch terramechanics models, wheel–terrain interactions are divided into three classes. Three vibration models of wheels under three WTICs have been built and analyzed. Vibration features in the models are extracted and non-dimensionalized to be independent of wheel speed. A vibration-feature-based recognition method of the WTIC is proposed. Then, the terrain parameters of the terramechanics model corresponding to the recognized WTIC are identified. Experiment results obtained using a Planetary Rover Prototype show that the identification method of terrain parameters is effective for rovers traversing various terrains. The relative errors of estimated wheel–terrain interaction force with identified terrain parameters are less than 16%, 12%, and 9% for rovers traversing hard, gravel, and sandy terrain, respectively.