In this work, a terrain estimation framework is developed for autonomous vehicles operating on deformable terrains. Previous work in this area usually relies on steady state tire operation, linearized classical terramechanics models, or on computationally expensive algorithms that are not suitable for real-time estimation. To address these shortcomings, this work develops a reduced-order nonlinear terramechanics model as a surrogate of the Soil Contact Model (SCM) through extending a state-of-the-art Bekker model to account for additional dynamic effects. It is shown that this reduced-order surrogate model is able to accurately replicate the forces predicted by the SCM while reducing the computation cost by an order of magnitude. This surrogate model is then utilized in a unscented Kalman filter to estimate the sinkage exponent. Simulations suggest this parameter can be estimated within 4% of its true value for clay and sandy loam terrains. It is also shown that utilizing this estimated parameter can reduce the prediction errors of the future vehicle states by orders of magnitude, which could assist with achieving more robust model-predictive autonomous navigation strategies.

中文翻译：

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自动驾驶汽车在变形地形上的在线地形估计

在这项工作中，为在可变形地形上运行的自动驾驶车辆开发了地形估计框架。该领域以前的工作通常依赖于稳态轮胎操作、线性化经典地形力学模型或不适合实时估计的计算成本高的算法。为了解决这些缺点，这项工作通过扩展最先进的 Bekker 模型来解释额外的动态效应，开发了一个降阶非线性地形力学模型作为土壤接触模型 (SCM) 的替代品。结果表明，这种降阶代理模型能够准确地复制 SCM 预测的力，同时将计算成本降低一个数量级。然后在无迹卡尔曼滤波器中使用该替代模型来估计下沉指数。模拟表明，对于粘土和砂质壤土地形，该参数可以估计在其真实值的 4% 以内。还表明，利用该估计参数可以将未来车辆状态的预测误差降低几个数量级，这有助于实现更稳健的模型预测自主导航策略。