DR. Nav: Semantic-Geometric Representations for Proactive Dead-End Recovery and Navigation

2025-11-16

AI Summary

Navigation failures in unknown unstructured environments—such as corners, vegetation occlusions, and dead ends—remain a critical challenge for autonomous mobile robots. Method: This paper proposes a prior-free active navigation framework that models dead-end risk as a learnable, continuous cost map, unifying prediction and avoidance. It integrates RGB-LiDAR cross-modal perception with attention-based filtering and employs Bayesian inference to dynamically update per-cell dead-end probabilities and estimate recovery points, yielding a semantics-enhanced real-time cost map. Contribution/Results: Evaluated across diverse indoor and outdoor dense scenarios, the method achieves an 83.33% improvement in dead-end detection accuracy. Compared to state-of-the-art planners—including DWA and MPPI—it reduces time-to-goal by 52.4%, significantly enhancing navigation robustness, safety, and efficiency.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Planning, Routing, and Scheduling: Activity and Plan Recognition
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

We present DR. Nav (Dead-End Recovery-aware Navigation), a novel approach to autonomous navigation in scenarios where dead-end detection and recovery are critical, particularly in unstructured environments where robots must handle corners, vegetation occlusions, and blocked junctions. DR. Nav introduces a proactive strategy for navigation in unmapped environments without prior assumptions. Our method unifies dead-end prediction and recovery by generating a single, continuous, real-time semantic cost map. Specifically, DR. Nav leverages cross-modal RGB-LiDAR fusion with attention-based filtering to estimate per-cell dead-end likelihoods and recovery points, which are continuously updated through Bayesian inference to enhance robustness. Unlike prior mapping methods that only encode traversability, DR. Nav explicitly incorporates recovery-aware risk into the navigation cost map, enabling robots to anticipate unsafe regions and plan safer alternative trajectories. We evaluate DR. Nav across multiple dense indoor and outdoor scenarios and demonstrate an increase of 83.33% in accuracy in detection, a 52.4% reduction in time-to-goal (path efficiency), compared to state-of-the-art planners such as DWA, MPPI, and Nav2 DWB. Furthermore, the dead-end classifier functions

Problem

• Proactively handles dead-end detection and recovery in unstructured robot navigation
• Unifies dead-end prediction using semantic-geometric cost maps without prior mapping
• Improves navigation safety by incorporating recovery-aware risk into path planning

Innovation

• Proactive navigation strategy without prior environmental assumptions
• Unified semantic cost map for dead-end prediction and recovery
• Cross-modal RGB-LiDAR fusion with attention-based filtering