Safe trajectory planning in complex environments must balance stringent collision avoidance with real-time efficiency, which is a long-standing challenge in robotics. In this work, we present a diffusion-based trajectory planning framework that is both rapid and safe. First, we introduce a scene-agnostic, MPC-based data generation pipeline that efficiently produces large volumes of kinematically feasible trajectories. Building on this dataset, our integrated diffusion planner maps raw onboard sensor inputs directly to kinematically feasible trajectories, enabling efficient inference while maintaining strong collision avoidance. To generalize to diverse, previously unseen scenarios, we compose diffusion models at test time, enabling safe behavior without additional training. We further propose a lightweight, rule-based safety filter that, from the candidate set, selects the trajectory meeting safety and kinematic-feasibility requirements. Across seen and unseen settings, the proposed method delivers real-time-capable inference with high safety and stability. Experiments on an F1TENTH vehicle demonstrate practicality on real hardware. Project page: https://rstp-comp-diffuser.github.io/.

翻译：复杂环境中的安全轨迹规划必须在严格的碰撞规避与实时效率之间取得平衡，这是机器人学中长期存在的挑战。在本研究中，我们提出了一种基于扩散的轨迹规划框架，兼具快速性与安全性。首先，我们引入了一种与场景无关、基于模型预测控制（MPC）的数据生成流程，能够高效生成大量运动学可行的轨迹。基于该数据集，我们集成的扩散规划器将原始车载传感器输入直接映射至运动学可行的轨迹，在保持强碰撞规避能力的同时实现高效推理。为泛化至多样化、先前未见过的场景，我们在测试时组合多个扩散模型，从而无需额外训练即可实现安全行为。我们进一步提出了一种轻量级的、基于规则的安全过滤器，从候选轨迹集中选择满足安全性与运动学可行性要求的轨迹。在已见及未见场景中，所提方法均能实现具备实时能力的推理，并保持高安全性与稳定性。在F1TENTH车辆平台上进行的实验验证了其在真实硬件上的实用性。项目页面：https://rstp-comp-diffuser.github.io/。