Новая архитектура SFPathFormer повышает точность навигации роботов в сложных средах

Safe path planning and obstacle avoidance remain core challenges for robots operating in unknown or semi-structured environments, where illumination shifts, complex layouts, and dynamic disturbances are common. Traditional geometric and rule-based methods perform well under ideal conditions but degrade when perception is noisy or information is incomplete. Deep-learning approaches, particularly convolutional neural network (CNN)-based pipelines, have improved obstacle detection and short-range avoidance, yet they suffer from multi-scale perception limits, severe feature redundancy, and weak global structural modeling.  These gaps lead to unstable path decisions in complex corridors, occluded spaces, and dynamic obstacle scenarios. To address them, SFPathFormer integrates spatial-frequency perception, dynamic feature selection, and vision transformer (ViT)-based global modeling into a single end-to-end framework, bridging local safety with holistic traversability understanding. The Architecture Behind SFPathFormer SFPathFormer processes visual input through three sequential stages, each addressing a distinct limitation in existing navigation systems. The first stage, parametric wavelet downsampling (PWD), decomposes incoming feature maps into low-frequency components that preserve overall environmental structure and high-frequency components that capture obstacle boundaries, textures, and local geometric detail. Unlike standard downsampling, which can blur critical edges, PWD applies learnable filters that adapt during training to emphasize information most relevant to safe navigation. A weighted fusion step then reduces resolution while retaining discriminative structural cues. This frequency-selective approach is especially valuable in robotics, where obstacle boundaries appear as high-frequency transients that conventional multi-scale modules often discard or dilute. The second stage, domain-related feature selection (DFS), tackles feature redundancy by assigning each feature dimension a continuous importance weight based on the current environmental context, such as scene complexity or obstacle density. Rather than applying static reweighting, DFS dynamically suppresses dimensions that contribute little to path safety while amplifying those strongly correlated with safe navigation decisions. A contrastive learning loss further enforces separation between safe and unsafe path states in feature space, ensuring the selected representation remains discriminative across diverse environmental domains and resistant to transient noise. Download a free copy of this page here! The final stage introduces a ViT for global path modeling. After positional information is embedded into feature tokens, multiple self-attention layers allow each spatial region to attend to all others, capturing long-range obstacle relationships and the overall traversability layout. Stacked transformer blocks progressively integrate local and global context, producing a structurally consistent environmental representation for downstream path planning. This compensates for the limited receptive field of conventional CNNs, which struggle to model spatial dependencies across extended or occluded scenes. Evaluating Performance Across Four Datasets SFPathFormer was evaluated on four datasets spanning simulated and real-world navigation. TartanAir and CARLA provide high-fidelity synthetic environments for controlled testing, while KITTI and Oxford RobotCar offer real-world urban driving data with genuine sensor noise and long-term appearance changes. This dual-track setup was designed to assess whether the framework could, in the future, bridge the simulation-to-reality gap. Data was split at the trajectory level (70% training, 10% validation, 20% testing) to prevent information leakage between correlated frames from the same navigation sequence. Augmentations such as brightness shifts, noise injection, and cropping were applied only to training data. Performance was measured using path-planning success rate, path length, recall, and F1 score. Across all four datasets, SFPathFormer consistently outperformed baseline planners and recent visual navigation approaches such as ViNT, NoMaD, and Diffusion Policy, achieving higher success rates, shorter paths, and lower variance across repeated runs. Computational efficiency also favored SFPathFormer, with inference times ranging from approximately 81 to 88 ms and parameter counts between 152 and 164 million, substantially lower than most competitors. Ablation studies confirmed that removing any major component (PWD, DFS, or ViT) caused significant performance drops. The removal of ViT produced the most severe degradation, underscoring the importance of global structural modeling.  Component-level ablations further validated the contribution of learnable wavelet parameters, high-frequency enhancement, domain-aware weighting, multi-head attention, and positional encoding. Attention visualizations showed the complete model focuses precisely on obstacle boundaries and traversable regions, whereas ablated variants produced scattered, less reliable attention patterns. Promising Results, Remaining Challenges SFPathFormer demonstrates that unifying spatial-frequency perception, dynamic feature selection, and global structural modeling into a single end-to-end framework significantly improves robot path planning and obstacle avoidance in complex environments. Experimental results across four datasets, both simulated and real-world, show consistent gains in path safety, planning stability, and environmental adaptability over existing methods. However, the framework has notable limitations. Computational overhead from wavelet decomposition and self-attention may constrain deployment on resource-limited platforms. Feature selection stability depends on the diversity of the training data. Critically, no validation has yet been performed on a physical robot, leaving the sim-to-real transfer unverified. Future work must prioritize real-world deployment, lightweight optimization, multi-modal sensing, and tighter integration with low-level control. Journal Reference Hu, Z., and Ma, C. (2026). SFPathFormer for spatial frequency robot navigation and obstacle avoidance. Scientific Reports, 16(1). DOI:10.1038/s41598-026-62838-z. https://www.nature.com/articles/s41598-026-62838-z. 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