Generative models have become increasingly powerful tools for robot motion generation, enabling flexible and multimodal trajectory generation across various tasks. Yet, most existing approaches remain limited in handling multiple types of constraints, such as collision avoidance, actuation limits, and dynamic consistency, which are typically addressed individually or heuristically. In this work, we propose UniConFlow, a unified constrained flow matching-based framework for trajectory generation that systematically incorporates both equality and inequality constraints. Moreover, UniConFlow introduces a novel prescribed-time zeroing function that shapes a time-varying guidance field during inference, allowing the generation process to adapt to varying system models and task requirements. Furthermore, to further address the computational challenges of long-horizon and high-dimensional trajectory generation, we propose two practical strategies for the terminal constraint enforcement and inference process: a violation-segment extraction protocol that precisely localizes and refines only the constraint-violating portions of trajectories, and a trajectory compression method that accelerates optimization in a reduced-dimensional space while preserving high-fidelity reconstruction after decoding. Empirical validation across three experiments, including a double inverted pendulum, a real-to-sim car racing task, and a sim-to-real manipulation task, demonstrates that UniConFlow outperforms state-of-the-art generative planners and conventional optimization baselines, achieving superior performance on certified motion planning metrics such as safety, kinodynamic consistency, and action feasibility. Project page is available at: https://uniconflow.github.io.

翻译：生成模型已成为机器人运动生成日益强大的工具，能够在各种任务中实现灵活且多模态的轨迹生成。然而，大多数现有方法在处理多种类型的约束（例如避障、驱动限制和动态一致性）方面仍然存在局限，这些约束通常被单独或启发式地处理。在本工作中，我们提出了UniConFlow，一个基于约束流匹配的统一轨迹生成框架，它系统地整合了等式和不等式约束。此外，UniConFlow引入了一种新颖的预设时间归零函数，在推理过程中塑造一个时变引导场，使得生成过程能够适应变化的系统模型和任务需求。进一步地，为了应对长时域和高维轨迹生成的计算挑战，我们提出了两种用于终端约束执行和推理过程的实用策略：一种违规段提取协议，能够精确定位并仅优化轨迹中违反约束的部分；以及一种轨迹压缩方法，在降维空间中加速优化，同时在解码后保持高保真重建。在三个实验（包括双倒立摆、实车到仿真赛车任务以及仿真到真实操作任务）中的实证验证表明，UniConFlow优于最先进的生成式规划器和传统优化基线，在认证运动规划指标（如安全性、运动学动态一致性和动作可行性）上实现了更优的性能。项目页面位于：https://uniconflow.github.io。