2025/07/06 by Wule Mao, Mao, Wule, Zhouheng Li +7 · 2 citations
Computer Science · Engineering · #Autonomous Vehicle Technology and Safety #Human Motion and Animation #Robotic Path Planning Algorithms #cs.RO #cs.SY #eess.SY
paper · pdf · doi:10.48550/arxiv.2507.04384
openalex publication_date 2025/07/06 · openalex created_date 2025/10/16 · openalex updated_date 2026/07/28
Achieving safe, efficient, and kinematically feasible planning in dynamic environments remains a significant challenge, as planners must simultaneously handle moving obstacles, sensor uncertainty, and strict motion constraints. To address this problem, we propose an energy-parameterized diffusion planning framework that learns a conservative energy field to realize safe and stable generalization across diverse scenarios. The energy-parameterized diffusion formulation enables flexible integration of multiple constraints, allowing the planner to generalize to previously unseen environments without retraining. To ensure real-time safety during deployment, we further incorporate a lightweight safety filter that enforces safety and kinematic feasibility constraints in real-time. Additionally, we develop a scene-agnostic, MPC-based data generation pipeline to produce large-scale, dynamically feasible training trajectories. In simulation, the proposed method achieves real-time performance with a mean planning time of 0.21s and a low planning failure rate of 0.57%. Real-world experiments on the F1TENTH platform further validate the effectiveness of the proposed framework. Under sensor uncertainty in previously unseen dynamic environments, the planner consistently generates collision-free trajectories, which remain safe after being tracked by a simple controller, maintaining a mean obstacle clearance of 0.26 m, demonstrating strong robustness and practical applicability. Project page: https://rstp-comp-diffuser.github.io.