用数字孪生与科普曼算子提升越野自动驾驶的泛化能力
Digital Twins Meet the Koopman Operator: Data-Driven Learning for Robust Autonomy
- 构建车辆数字孪生，基于科普曼算子从仿真数据学习动态模型
- 实测导航性能提升5.84倍，样本效率提高3.2倍，仿真到现实差距缩小5.2倍
- 适合做越野自动驾驶、仿真驱动控制的科研与工程人员
与公路自动驾驶不同，越野自主导航受感知挑战和地形变化等多重因素影响。数据驱动方法虽能有效建模复杂车-环境交互，但其效果依赖高质量、高数量的数据，而越野环境的多样性常导致数据质量下降。为此，本文提出一种新方法，通过数字孪生技术精确复现车辆及其目标运行条件，实现领域特定的数据生成。基于此，利用科普曼算子理论从仿真数据中建模越野车辆动力学，并应用于局部运动规划与最优控制。在1:5比例车辆的自主导航任务中验证了该方法：采用地形感知规划器进行全局任务规划，实验结果表明，所提算法使越野导航性能提升5.84倍，样本效率提高3.2倍，仿真到现实的差距缩小5.2倍。
原文摘要 · Abstract (English)
Contrary to on-road autonomous navigation, off-road autonomy is complicated by various factors ranging from sensing challenges to terrain variability. In such a milieu, data-driven approaches have been commonly employed to capture intricate vehicle-environment interactions effectively. However, the success of data-driven methods depends crucially on the quality and quantity of data, which can be compromised by large variability in off-road environments. To address these concerns, we present a novel methodology to recreate the exact vehicle and its target operating conditions digitally for domain-specific data generation. This enables us to effectively model off-road vehicle dynamics from simulation data using the Koopman operator theory, and employ the obtained models for local motion planning and optimal vehicle control. The capabilities of the proposed methodology are demonstrated through an autonomous navigation problem of a 1:5 scale vehicle, where a terrain-informed planner is employed for global mission planning. Results indicate a substantial improvement in off-road navigation performance with the proposed algorithm (5.84x) and underscore the efficacy of digital twinning in terms of improving the sample efficiency (3.2x) and reducing the sim2real gap (5.2%).
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