AI-Powered Robotics Navigation

Abstract

Autonomous robot navigation in unstructured, dynamic environments remains one of the most demanding AI integration challenges -- requiring simultaneous competence in perception, mapping, planning, and real-time control under strict latency, energy, and safety constraints. Classical navigation stacks (SLAM plus A*/RRT planning) have well-understood limitations in highly dynamic scenes and semantically rich environments where object recognition is essential for path decisions. Deep learning-based navigation approaches have demonstrated superior performance in complex scenarios but introduce new concerns around sim-to-real transfer, distributional robustness, and explainability. This study benchmarks eight AI-powered navigation systems -- end-to-end deep reinforcement learning (DRL), modular deep learning (perception + classical planning), occupancy map CNNs, transformer-based scene understanding (NavTransformer), vision-language navigation (VLN-BERT), language-conditioned RL (LangNav), Neural SLAM, and a foundation model-integrated approach (NavGPT) -- across four navigation domains: indoor structured environments, outdoor unstructured terrain, dynamic pedestrian-dense scenes, and semantically complex (instruction-following) navigation. Evaluation used the iGibson 2.0 simulator, the Habitat challenge benchmark, real-world corridor trials at two European robotics labs, and the R2R (Room-to-Room) instruction-following dataset. NavTransformer achieves the highest success rate on structured indoor navigation (84.6%). LangNav leads on semantic instruction-following (72.4%). Modular deep learning shows the best sim-to-real transfer (success rate gap of 8.4pp vs. 24.2pp for end-to-end DRL). A deployment readiness framework covering safety, robustness, and explainability for AI navigation systems is proposed.