Time-Consistent Predictive Navigation for Autonomous Mobile Robots in Dynamic Indoor Environments: A D* Lite–DWA Framework

Autonomous mobile robots in dynamic indoor environments must manage future conflicts absent from current occupancy. We propose a time-consistent predictive navigation framework based on D* Lite and the Dynamic Window Approach (DWA). Its contribution is a unified temporal interface coordinating segmented prediction, continuous spatiotemporal validation, local risk handling, protected waiting, and time-matched D* Lite replanning. Assessment comprised 1026 core evaluations and a separate 90-case paired external benchmark, totaling 1116 runs in controlled numerical simulation. In the core paired comparison, navigation success was 97.8% for the full method and 34.4% for the current-occupancy fusion baseline. In the external benchmark, navigation success was 97.8% for the full method and 30.0% for a classical A*–DWA architecture. The full method had no collisions or missed physical-conflict detections, and the remaining 2.2% ended in active safe termination. Removing time-domain local risk handling reduced navigation success to 56.7%, with collisions in 10.0% of cases and missed physical-conflict detections in 6.7%. Across 180 single-factor perturbation cases, navigation success was 99.4%, and the remaining 0.6% ended in active safe termination. Within the tested scenarios, the coordinated mechanisms supported future-conflict handling and recovery from persistent blockages. Deadline violations remained, and all evidence comes from controlled deterministic simulation.