ForexNav: Foresight Exploratory Navigation in Complex and Unknown Indoor Environments

Abstract

Autonomous navigation in unknown, complex indoor environments remains challenging due to limited sensing range and severe partial observability. Conventional methods rely on local maps without foresight, causing dead-ends and long detours, while local goal selection based on Euclidean distance or frontier coverage fails to balance efficiency with directionality. To address these challenges, we propose ForexNav, a foresight-enabled exploratory navigation framework. To handle structural ambiguity in unseen regions, we introduce Foresight Hypothesis Fusion (FHF), which maintains multiple WGAN-based map predictions and reweights them via temporal evidence accumulation. A Traversability-aware A* search then quantifies predictive traversability on the fused map, enabling a multi-objective planner to synthesize path feasibility, kinodynamic conformity, monotonic-progress consistency, and geometric distance for optimal intermediate goal selection and dynamically consistent trajectory generation. Experiments in four simulated indoor scenes of up to 3,300 m<inline-formula><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> demonstrate navigation success while reducing total travel time by 25.0% and improving average velocity by 13.3% over the strongest baseline, with path ratio improvements of 22.2% on average in large-scale environments (<inline-formula><tex-math notation="LaTeX">$\geq$</tex-math></inline-formula>2,000 m<inline-formula><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula>). Real-world deployment on a quadruped robot supports practical feasibility, and extension to a fixed-altitude micro-UAV further suggests preliminary cross-platform transferability.