用扩散模型生成既平滑又精准的机器人轨迹
DRIFT: Diffusion-based Rule-Inferred For Trajectories
- 融合图神经网络与时间感知机制，兼顾全局平滑与局部精准
- 轨迹误差仅0.041米，抖动值27.19，平衡精度与顺滑性
- 适合需要高精度执行的移动机器人路径规划任务
在非结构化环境中，移动机器人轨迹生成面临运动平滑性与终端精度的权衡难题。现有生成式规划方法常导致路径平滑但不精准，或几何准确但运动混乱。本文提出DRIFT（基于扩散的规则推理轨迹生成），一种条件扩散框架，通过引入两种互补的归纳偏置实现高保真参考轨迹生成：首先，基于GNN的结构化场景感知（SSP）模块引入关系归纳偏置，编码全局拓扑约束以保证整体平滑；其次，通过新型图条件时间感知GRU（GTGRU）实现时序注意力偏置，动态关注稀疏障碍物与目标以实现精确局部操作。定量结果表明，DRIFT成功调和上述矛盾，在末端误差（FDE）达0.041米、抖动值（Jerk）为27.19的同时保持优异平滑性，生成可直接用于下游控制的高可执行参考轨迹。
原文摘要 · Abstract (English)
Trajectory generation for mobile robots in unstructured environments faces a critical dilemma: balancing kinematic smoothness for safe execution with terminal precision for fine-grained tasks. Existing generative planners often struggle with this trade-off, yielding either smooth but imprecise paths or geometrically accurate but erratic motions. To address the aforementioned shortcomings, this article proposes DRIFT (Diffusion-based Rule-Inferred for Trajectories), a conditional diffusion framework designed to generate high-fidelity reference trajectories by integrating two complementary inductive biases. First, a Relational Inductive Bias, realized via a GNN-based Structured Scene Perception (SSP) module, encodes global topological constraints to ensure holistic smoothness. Second, a Temporal Attention Bias, implemented through a novel Graph-Conditioned Time-Aware GRU (GTGRU), dynamically attends to sparse obstacles and targets for precise local maneuvering. In the end, quantitative results demonstrate that DRIFT reconciles these conflicting objectives, achieving centimeter-level imitation fidelity (0.041m FDE) and competitive smoothness (27.19 Jerk). This balance yields highly executable reference plans for downstream control.
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