用多普勒激光雷达实现高速动态环境下的实时避障规划
DPNet: Doppler LiDAR Motion Planning for Highly-Dynamic Environments
- 基于多普勒模型学习，实现对快速移动障碍物的状态跟踪
- 在仿真与真实数据上均达到高精度、高频次的规划性能
- 适合自动驾驶、机器人等需快速响应动态环境的场景
现有运动规划方法在应对快速运动障碍物时表现不佳，因其对环境变化的理解不足。为此，我们提出将运动规划器与多普勒激光雷达结合，后者不仅能提供距离测量，还能获取点云的瞬时速度。然而，这种融合面临高精度与高频率的双重挑战。为此，我们提出多普勒规划网络（DPNet），通过多普勒模型驱动的学习，实现对快速障碍物的追踪与响应。首先，设计了多普勒卡尔曼神经网络（D-KalmanNet），在部分可观测高斯状态空间模型下进行障碍物状态跟踪；随后，利用预测的障碍物运动构建多普勒调谐模型预测控制（DT-MPC）框架，实现对自身运动规划中控制器参数的运行时自动调优。两个模块使DPNet能在少量数据下学习快速环境变化，同时保持轻量级，实现在跟踪与规划中的高频率与高精度。在高保真仿真器和真实世界数据集上的实验表明，DPNet显著优于多种基准方案。代码已开源：https://github.com/UUwei-zuo/DPNet。
原文摘要 · Abstract (English)
Existing motion planning methods often struggle with rapid-motion obstacles due to an insufficient understanding of environmental changes. To address this, we propose integrating motion planners with Doppler LiDARs, which provide not only ranging measurements but also instantaneous point velocities. However, this integration is nontrivial due to the requirements of high accuracy and high frequency. To this end, we introduce Doppler Planning Network (DPNet), which tracks and reacts to rapid obstacles via Doppler model-based learning. We first propose a Doppler Kalman neural network (D-KalmanNet) to track obstacle states under a partially observable Gaussian state space model. We then leverage the predicted motions of obstacles to construct a Doppler-tuned model predictive control (DT-MPC) framework for ego-motion planning, enabling runtime auto-tuning of controller parameters. These two modules allow DPNet to learn fast environmental changes from minimal data while remaining lightweight, achieving high frequency and high accuracy in both tracking and planning. Experiments on high-fidelity simulator and real-world datasets demonstrate the superiority of DPNet over extensive benchmark schemes. Code available at https://github.com/UUwei-zuo/DPNet
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