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Visual navigation is an essential part of planetary rover autonomy. Rock segmentation emerged as an important interdisciplinary topic among image processing, robotics, and mathematical modeling. Rock segmentation is a challenging topic for rover autonomy because of the high computational consumption, real-time requirement, and annotation difficulty. This research proposes a rock segmentation framework and a rock segmentation network (NI-U-Net++) to aid with the visual navigation of rovers. The framework consists of two stages: the pre-training process and the transfer-training process. The pre-training process applies the synthetic algorithm to generate the synthetic images; then, it uses the generated images to pre-train NI-U-Net++. The synthetic algorithm increases the size of the image dataset and provides pixel-level masks—both of which are challenges with machine learning tasks. The pre-training process accomplishes the state-of-the-art compared with the related studies, which achieved an accuracy, intersection over union (IoU), Dice score, and root mean squared error (RMSE) of 99.41%, 0.8991, 0.9459, and 0.0775, respectively. The transfer-training process fine-tunes the pre-trained NI-U-Net++ using the real-life images, which achieved an accuracy, IoU, Dice score, and RMSE of 99.58%, 0.7476, 0.8556, and 0.0557, respectively. Finally, the transfer-trained NI-U-Net++ is integrated into a planetary rover navigation vision and achieves a real-time performance of 32.57 frames per second (or the inference time is 0.0307 s per frame). The framework only manually annotates about 8% (183 images) of the 2250 images in the navigation vision, which is a labor-saving solution for rock segmentation tasks. The proposed rock segmentation framework and NI-U-Net++ improve the performance of the state-of-the-art models. The synthetic algorithm improves the process of creating valid data for the challenge of rock segmentation. All source codes, datasets, and trained models of this research are openly available in Cranfield Online Research Data (CORD).

Performance in autonomous driven vehicles is susceptible of degradation when traversing different terrains, thus needing motion controllers to be tuned for different terrain profiles. Such tuning stage is a time consuming process for the programmer or operator, and it is often based on intuition or heuristic approaches, and once tuned, the performance of the vehicle varies according to the terrain nature. In this context, we provide a visual based approach to identify terrain variability and its transitions, while observing and learning the performance of the vehicle using machine learning techniques. Based on the identified terrain and the knowledge regarding the performance of the vehicle, our system self-tunes the motion controller, in real time, to enhance its performance. In particular, the trajectory tracking errors are reduced, the control input effort is decreased, and the effects of the wheel-terrain interaction are mitigated preserving the system robustness. The tests were carried out by simulation and experimentation using a robotized commercial platform. Finally, implementation details and results are included in this paper, showing an enhancement in the motion performance up to 92.4% when the highest accuracy of the terrain classifier was 84.3%.