Vision-Language Model-Driven Predictive Platform Employing Swarm Robotics and Post-Quantum Signatures for Autonomous Green Vessel Navigation and Supply Chain Resilience

This paper proposes an innovative vision-language model (VLM) driven predictive platform that synergistically integrates swarm robotics coordination with post-quantum digital signatures to enable fully autonomous navigation for green vessels eco-friendly ships leveraging hybrid renewable propulsion systems such as biofuels, hydrogen fuel cells, and wind-assisted technologies. Traditional maritime navigation systems struggle with dynamic oceanic conditions, including unpredictable weather patterns, high-traffic congestion, and escalating cyber threats, which compromise supply chain efficiency and sustainability goals. The proposed framework addresses these challenges by deploying a multimodal VLM core that fuses real-time visual data from onboard LiDAR, infrared cameras, and radar with textual inputs from AIS (Automatic Identification System) broadcasts, satellite weather forecasts, and nautical charts. This fusion generates interpretable probabilistic predictions of future states, such as wave-induced trajectory deviations or collision risks, enabling proactive rerouting that minimizes hydrodynamic drag and emissions.Swarm robotics augments individual vessel autonomy through decentralized fleets of unmanned surface vehicles (USVs) that dynamically form protective convoys or scouting formations, optimizing collective energy use via bio-inspired particle swarm optimization conditioned on VLM outputs. To safeguard against quantum computing vulnerabilities inherent in classical RSA or ECC protocols, the platform embeds lightweight Dilithium or Falcon post-quantum signatures for authenticating sensor streams, cargo manifests, and inter-vessel commands, ensuring non-repudiation even under harvest-now-decrypt-later attacks. Validation occurs within high-fidelity maritime digital twins that simulate full-scale operations, incorporating computational fluid dynamics for vessel hydrodynamics and stochastic perturbations for resilience testing.Extensive simulations demonstrate transformative performance fuel savings exceed 38% via predictive eco-routing, collision avoidance precision reaches 97.2% in dense fog scenarios, and cryptographic overhead remains below 5% bandwidth utilization on edge-constrained USVs. Supply chain resilience improves markedly, with recovery time from simulated disruptions reduced by 52%, fortifying global logistics against climate volatility and geopolitical risks. This work pioneer’s end-to-end integration of VLMs, swarms, and quantum-safe primitives, laying a robust foundation for scalable, secure, and sustainable autonomous maritime ecosystems aligned with UN Sustainability Development Goals.