Cyber-Resilient Mapless Navigation for Autonomous Mobile Robots: Cloud-Independent Edge Inference with Distilled Vision–Language Semantic Embeddings and LiDAR Fusion

Autonomous mobile robots (AMRs) that depend on remote inference introduce communication and service availability into the navigation control path. This paper presents a cloud-independent mapless-navigation architecture integrating visual features distilled offline from a vision–language model, PointNet++ light detection and ranging (LiDAR) features, and a Twin Delayed Deep Deterministic Policy Gradient (TD3) controller. An 8-bit integer (INT8)-quantized Vision Transformer student processes red–green–blue (RGB) images on an NVIDIA Jetson Orin Nano, while late fusion projects visual and geometric features into a common 512-dimensional policy input. Runtime navigation uses RGB and LiDAR observations without natural-language commands. A four-class threat model distinguishes the remote-inference interfaces removed by this arrangement from residual sensor, local-interface, and supply-chain risks. The evaluation combines public benchmark inputs with a fixed-seed synthetic profiling sequence and reports descriptive timing, power, and path-deviation summaries. Analytical checks establish the projection dimensions, nominal weight-storage reduction, and power–time calculations. The results provide a descriptive characterization of this integration, with sensor-attack mitigation reserved for future evaluation. The contribution is an explicit perception–control architecture, an analysis of its remote-inference dependencies, and a conceptual mapping from deployment objectives to security and safety frameworks.