BRiDGENav: Belief-Driven Risk-Aware Dynamic Guidance and Event-Triggered Navigation

Human–robot interaction (HRI) and navigation in crowded indoor spaces require robots to reason not only about where humans may be but also about whether uncertain human motion conflicts with the robot’s intended path. Existing context-aware particle filtering (CA-PF) provides a useful belief-estimation backbone by maintaining multimodal human-state uncertainty under occlusion, missed detections, and close interaction. However, a navigation layer must further convert this belief into path-aware risk and determine when replanning is necessary. This paper extends the CA-PF framework with three navigation modules: a path-conflict risk module that evaluates weighted human particles against the committed robot path, a directional interaction-aware risk grid that combines particle occupancy with human motion direction and conflict likelihood, and a belief-aware event-triggered replanning strategy that updates the path when conflict, directional path risk, or risk growth exceeds safety thresholds. Evaluation on four sequences from the Bi3 dataset demonstrates a mean human-tracking error of 0.237 m and an RMSE of 0.308 m. In the ablation study, the complete framework reduced mean path-conflict risk by 25.2% and mean directional path risk by 42.35% relative to the configuration without event-triggered replanning, averaged over 12 matched trials. These results demonstrate that propagating particle-level human beliefs into path-conflict reasoning, directional risk-aware planning, and selective replanning can improve the handling of interaction risk in dynamic human environments.