A VelocitySNN-Fuzzy AI Architecture: Neuromorphic Event-Driven Spiking Neural Network and Fuzzy Logic AI System for 3D Velocity Determination in Martian Flight

We introduce VelocitySNN-Fuzzy AI, a novel neuromorphic system for real-time determination of three-dimensional velocity vectors (Vx, Vy, Vz) during Martian drone flight. Unlike Earth-Based UAVs, Mars operations cannot rely on GPS or conventional visual odometry due to the lack of infrastructure and the extreme lighting and terrain conditions. Our approach integrates a bio-inspired event-based camera, a Spiking Neural Network (SNN) developed using SpikingJelly, and an explainable Fuzzy AI Logic layer to produce reliable and interpretable velocity determination in challenging Mars-Like environments. Validated using publicly available Mars-Style drone datasets, the architecture demonstrates low-latency, high-dynamic-range perception suitable for small, power-constrained UAV platforms. VelocitySNN-Fuzzy AI determines 3D velocity for Martian drones using event-based vision, spiking neural networks, and fuzzy logic. Designed for GPS-denied, low-light environments, it enables real-time, low-power navigation. Validated on FPV drone datasets, it supports autonomous flight with interpretable outputs and deployment on neuromorphic hardware like Loihi and Akida.