Existing motion planning methods often struggle with rapid-motion obstacles due to an insufficient understanding of environmental changes. To address this limitation, 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 dual requirements of high accuracy and high frequency. To this end, we introduce Doppler Planning Network (DPNet), which tracks and reacts to rapid obstacles using Doppler model-based learning. Particularly, we first propose a Doppler Kalman neural network (D-KalmanNet) to track the future states of obstacles under partially observable Gaussian state space model. We then leverage the estimated motions to construct a Doppler-tuned model predictive control (DT-MPC) framework for ego-motion planning, enabling runtime auto-tuning of the controller parameters. These two model-based learners allow DPNet to maintain lightweight while learning fast environmental changes using minimum data, and achieve both high frequency and high accuracy in tracking and planning. Experiments on both high-fidelity simulator and real-world datasets demonstrate the superiority of DPNet over extensive benchmark schemes.

翻译：现有运动规划方法因对环境变化理解不足，常难以应对高速运动障碍物。为克服这一局限，我们提出将运动规划器与多普勒激光雷达相结合，后者不仅能提供测距信息，还可获取瞬时点速度。然而，由于高精度与高频率的双重要求，该集成面临挑战。为此，我们提出多普勒规划网络（DPNet），通过基于多普勒模型的学习实现对快速障碍物的跟踪与响应。具体而言，我们首先提出多普勒卡尔曼神经网络（D-KalmanNet），用于在部分可观测的高斯状态空间模型下跟踪障碍物的未来状态。随后利用估计的运动信息构建多普勒调谐模型预测控制（DT-MPC）框架进行本体运动规划，实现控制器参数的运行时自动调优。这两个基于模型的学习器使DPNet能够以轻量化架构、通过最少数据学习快速环境变化，并在跟踪与规划中同时实现高频率与高精度。在高保真仿真器与真实数据集上的实验表明，DPNet在多项基准方案中均表现出优越性能。