用AI预测无人机与地面车交接轨迹，提速三倍还确保成功
Learning-Accelerated Optimization-based Trajectory Planning for Cooperative Aerial-Ground Handover Missions
- 用LSTM网络从任务需求直接预测交接轨迹
- 比传统方法快3倍以上，成功率100%
- 适合需要快速可靠规划的多机器人系统
本文提出一种学习增强型轨迹规划框架，用于协同无人飞行器（UAV）与无人地面车辆（UGV）的交接任务。尽管集中式轨迹优化能保证动态可行性与任务最优性，但其高计算成本限制了实时应用。我们设计了一种神经代理规划器，采用解耦的编码器-解码器LSTM网络，从任务规范生成协调的交接轨迹预测。这些预测作为下游集中优化器的有向热启动，显著加速收敛至动态可行解。基准测试表明，该学习增强规划框架相较冷启动优化实现超过三倍的加速，且优化成功率高达100%。结果表明，数据驱动推理与模型驱动精炼相结合，可为异构多机器人系统提供快速可靠的轨迹生成方案。
原文摘要 · Abstract (English)
This paper presents a learning-augmented trajectory planning framework for cooperative unmanned aerial vehicle (UAV) and unmanned ground vehicle (UGV) handover missions. While centralized trajectory optimization ensures dynamic feasibility and task optimality, its high computational cost limits real-time applicability. We propose a neural surrogate planner utilizing decoupled encoder-decoder long short-term memory (LSTM) networks to generate coordinated handover trajectory predictions from the task specifications. These predictions serve as informed warm starts for the downstream centralized optimizer, thereby accelerating convergence to dynamically feasible solutions. Benchmark evaluations demonstrate that the learning-augmented planning framework achieves more than a threefold speedup and 100% optimization success rate compared to cold start optimization. The results indicate that combining data-driven inference with model-based refinement enables fast and reliable trajectory generation for heterogeneous multi-robot systems.
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