让机器人实时适应复杂地形，提升越野导航精度与速度
VertiAdaptor: Online Kinodynamics Adaptation for Vertically Challenging Terrain
- 用神经微分方程融合高程与语义信息，构建可自适应的运动模型
- 在新环境中预测误差降低23.9%，适应速度提升5倍
- 适合需在多变野外环境运行的自动驾驶机器人研发者
非结构化地形下的自主驾驶面临动态性和不可预测性的挑战。传统运动学动力学模型难以跨不同几何与语义地形泛化，亟需实时适应以保障安全可靠导航。我们提出VertiAdaptor（VA），一种新型在线自适应框架，通过函数编码器高效融合高程与语义嵌入，实现地形感知的运动学动力学建模与规划。VA学习由一组神经常微分方程基函数构成的运动学空间，捕捉车辆-地形间的复杂交互。离线训练后，该方法可通过计算高效的最小二乘法快速识别新环境中的运动学特性，实现实时自适应。我们在基于Chrono多物理引擎构建的Verti-Bench仿真平台中评估了VA，并在物理的Verti-4-Wheeler平台上验证其性能。结果表明，VA将预测精度提升最高达23.9%，适应时间缩短至1/5，显著增强了自主机器人在复杂多变的越野环境中的鲁棒性与可靠性。
原文摘要 · Abstract (English)
Autonomous driving in off-road environments presents significant challenges due to the dynamic and unpredictable nature of unstructured terrain. Traditional kinodynamic models often struggle to generalize across diverse geometric and semantic terrain types, underscoring the need for real-time adaptation to ensure safe and reliable navigation. We propose VertiAdaptor (VA), a novel online adaptation framework that efficiently integrates elevation with semantic embeddings to enable terrain-aware kinodynamic modeling and planning via function encoders. VA learns a kinodynamic space spanned by a set of neural ordinary differential equation basis functions, capturing complex vehicle-terrain interactions across varied environments. After offline training, the proposed approach can rapidly adapt to new, unseen environments by identifying kinodynamics in the learned space through a computationally efficient least-squares calculation. We evaluate VA within the Verti-Bench simulator, built on the Chrono multi-physics engine, and validate its performance both in simulation and on a physical Verti-4-Wheeler platform. Our results demonstrate that VA improves prediction accuracy by up to 23.9% and achieves a 5X faster adaptation time, advancing the robustness and reliability of autonomous robots in complex and evolving off-road environments.
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