NeSAM：用于越野移动的带土壤自适应的神经符号运动动力学模型
NeSAM: Neuro-Symbolic Kinodynamics with Soil Adaptation for Off-Road Mobility
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- George Mason University（乔治梅森大学）
- University of Catania（卡塔尼亚大学）
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中文总结 AI 辅助
NeSAM是结合Bekker-Wong地面力学与Transformer残差模型的神经符号框架，可在线估计土壤参数，在模拟与真实越野场景中提升运动预测准确率及轨迹跟踪精度。
中文摘要 AI 辅助
可变形地形上越野车辆运动的准确预测仍具挑战性，因为沉陷、滑移和牵引力随局部土壤条件变化而改变。现有基于学习的运动动力学模型直接从数据近似车辆-地形相互作用，但未明确表示土壤力学，物理可解释性有限。为解决这些局限，我们提出NeSAM，一种神经符号框架，将可微分的Bekker-Wong terramechanics（地面力学）与学习到的地形表示、基于Transformer的残差动力学模型相结合，用于长时程、六自由度运动动力学预测。地面力学组件建模依赖土壤的相互作用力，残差模型校正解析预测与观测车辆动力学之间的偏差。NeSAM还从地形观测中估计具有物理意义的土壤参数，并使用扩展卡尔曼滤波器在线更新。我们在基于Chrono多物理引擎构建的模拟器Verti-Bench中评估NeSAM，并在实物Verti-4-Wheeler平台上验证其性能。与最强的对比基线相比，NeSAM在模拟中预测准确率提升最高达30%，在真实世界数据上提升29%。当与闭环导航控制器集成时，NeSAM通过在线土壤自适应进一步提升通行成功率，同时将参考轨迹的Hausdorff距离降低69.4%，表明轨迹跟踪精度得到提升。
英文摘要
Accurate prediction of off-road vehicle motion over deformable terrain remains challenging because sinkage, slip, and traction vary with local soil conditions. Existing learning-based kinodynamic models directly approximate vehicle-terrain interactions from data but do not explicitly represent soil mechanics and offer limited physical interpretability. To address these limitations, we present NeSAM, a neuro-symbolic framework that combines differentiable Bekker-Wong terramechanics with learned terrain representations and a Transformer-based residual dynamics model for long-horizon, six degree-of-freedom kinodynamic prediction. The terramechanics component models soil-dependent interaction forces, while the residual model corrects discrepancies between the analytical prediction and the observed vehicle dynamics. NeSAM further estimates physically meaningful soil parameters from terrain observations and updates them online using an extended Kalman filter. We evaluate NeSAM in Verti-Bench, a simulator built on the Chrono multiphysics engine, and validate its performance on a physical Verti-4-Wheeler platform. NeSAM improves prediction accuracy by up to 30% in simulation and 29% on real-world data relative to the strongest compared baselines. When integrated with a close-loop navigation controller, NeSAM further improves traversal success rate through online soil adaptation while reduces Hausdorff distance to the reference trajectory by 69.4%, indicating improved trajectory tracking accuracy.
