用深度学习预测火星车在复杂地形的打滑，提升导航安全。
SlipNet: Enhancing Slip Cost Mapping for Autonomous Navigation on Heterogeneous and Deformable Terrains
- 基于DeepLab v3+构建动态分段打滑预测模型，无需事先分类地形。
- 在五类不同地形上平均误差更低，仿真测试优于现有最佳方法。
- 适合做太空探测器自主导航、智能机器人地形适应的研究者。
自主空间探测车在变形和异质地形上导航时，因土壤特性差异导致严重车轮打滑，影响导航效率并增加被困风险。为解决此问题，我们提出SlipNet，一种无需预先地形分类即可预测分段区域车轮打滑的新方法。SlipNet采用动态地形分段与打滑分配技术，在未见过的数据上增强探测车在不确定环境中的导航能力。我们利用高保真Vortex Studio模拟器构建了合成数据生成框架，创建出涵盖多种可变形地形条件的真实数据集用于训练与评估。大量仿真结果表明，结合DeepLab v3+的SlipNet模型在五个不同地形样本上显著优于当前最先进的TerrainNet方法，实现了更低的平均绝对误差（MAE）。这些发现验证了SlipNet在复杂地形中提升探测车导航能力的有效性。
原文摘要 · Abstract (English)
Autonomous space rovers face significant challenges when navigating deformable and heterogeneous terrains due to variability in soil properties, which can lead to severe wheel slip, compromising navigation efficiency and increasing the risk of entrapment. To address this problem, we introduce SlipNet, a novel approach for predicting wheel slip in segmented regions of diverse terrain surfaces without relying on prior terrain classification. SlipNet employs dynamic terrain segmentation and slip assignment techniques on previously unseen data, enhancing rover navigation capabilities in uncertain environments. We developed a synthetic data generation framework using the high-fidelity Vortex Studio simulator to create realistic datasets that replicate a wide range of deformable terrain conditions for training and evaluation. Extensive simulation results demonstrate that our model, combining DeepLab v3+ with SlipNet, significantly outperforms the state-of-the-art TerrainNet method, achieving lower mean absolute error (MAE) across five distinct terrain samples. These findings highlight the effectiveness of SlipNet in improving rover navigation in challenging terrains.
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