无需通信的车队追踪新方法，用神经网络+滤波器实现高精度延迟补偿。
Constant Time-Delay Leader Following with Neural Networks and Invariant Extended Kalman Filters for Arbitrary Trajectories
- 融合序列到序列神经网络与不变扩展卡尔曼滤波器，预热轨迹预测过程。
- 在任意非线性路径和长延迟下仍保持高精度相对轨迹估计。
- 适合无人车队控制，减少人工调参需求，实测与仿真均验证有效。
本文提出一种无需车辆间通信、统一坐标系或全球定位的常时延轨迹跟踪方法。该方法将概率性序列到序列（Seq2Seq）神经网络与不变扩展卡尔曼滤波器（IEKF）结合，用于暖启动预测过程，实现对领头车相对轨迹在SE(2)流形上的精确估计。进一步引入几何模型预测控制器，充分挖掘基于流形的轨迹预测优势，提升控制性能。系统可应对任意非线性轨迹、变速及运动模式变化，显著降低对专家领域知识的需求，即使在长时间延迟下仍表现良好。通过与纯IEKF基线、学习型方法及真实轨迹的对比，在运动学仿真和真实机器人实验中验证了其有效性。
原文摘要 · Abstract (English)
This paper proposes a constant time-delay trajectory tracking method for vehicle convoys operating without inter-vehicle communication, a common coordinate system, or global positioning. The method integrates a probabilistic sequence-to-sequence (Seq2Seq) neural network with an invariant extended Kalman filter (IEKF) to warm-start the prediction process, allowing accurate estimation of a leader vehicle's relative trajectory on the SE(2) manifold. A geometric model predictive controller is further incorporated to fully exploit the manifold-based trajectory predictions for improved control performance. The system can handle arbitrary nonlinear trajectories with varying speeds and motion profiles while reducing the need for expert-based domain knowledge for the design of trajectory following systems, even under long trajectory delays. The effectiveness of the method is validated through comparisons with a pure IEKF baseline, learning-based methods, and the ground-truth trajectory in kinematic simulations, as well as in experiments using real robotic vehicles.
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