ST-DiffPlanner: A Safety-Enhanced Topology-Aware Diffusion Planner for Global Path Planning

Abstract

In complex environments, traditional path planning methods rely on manually defined models, requiring tedious adjustments under varying scenarios or constraints. They also suffer from unstable time overhead and exponentially increasing computational costs as environmental complexity grows. Deep learning-enhanced methods, while optimizing decisions via neural networks, remain constrained by explicit search/sampling frameworks—this leads to unstable real-time performance and failure to capture real-world trajectory distributions. In contrast, diffusion-based planning directly learns trajectory distributions from data, offering predictable inference latency via fixed inversion steps and inherent support for multimodal solutions. However, its lack of explicit safety constraints often leads to trajectory safety issues, resulting in planning failures. To address these limitations, this paper proposes ST-DiffPlanner, a global path planner following the pipeline of ''topology cognition—direction focusing—trajectory generation". It introduces three targeted optimizations: (1) leveraging topological awareness to constrain the diffusion model to focus on collision-free regions; (2) optimizing inference-phase projection to ensure trajectory continuity and safe distances from obstacles; (3) designing a topology anchor-based safety loss to enhance model safety and training stability. Experimental results demonstrate that ST-DiffPlanner exhibits strong generalization across multiple scenarios and modalities, accurately capturing environmental features and learning task-compliant trajectory characteristics. Our method achieves an average trajectory generation success rate of 96.9%, significantly outperforming baseline methods. Moreover, validation in both simulated and real-world robot platforms confirms its applicability across different systems.