RAKSHAK - Autonomy Evaluation Framework

RAKSHAK is an evaluation and benchmarking framework that validates autonomous robots before real-world deployment. As autonomous systems enter disaster zones, hospitals, warehouses, food and medicine delivery networks, agricultural pesticide spraying, and public infrastructure, failures are no longer minor bugs — they can result in injuries, recalls, lawsuits, and lost trust. Most autonomy failures occur in edge-case conditions not covered by standard testing. Field validation can cost $50K–$500K per failure iteration. RAKSHAK exposes these failures safely in simulation before deployment risk exists. Built on top of Webots for real-time 3D simulation, RAKSHAK transforms simulation into adversarial validation infrastructure. Instead of testing robots under ideal conditions, it injects 50+ structured chaos scenarios including battery degradation, sensor blackouts, communication loss, environmental hazards, network latency, and multi-agent conflicts derived from real-world robotics failure modes. The platform integrates LLM-driven autonomy using the Gemini API and runs cloud-deployed simulations on Vultr infrastructure with WebSocket-based real-time telemetry. It performs live stress injection and generates a quantified Trust Score (0–100) across safety, resilience, efficiency, communication reliability, and task completion. Example: A delivery drone carrying food or emergency medicine passes obstacle avoidance tests but crashes when battery drops below 20% during evasive maneuvers. RAKSHAK’s structured power-drop scenario exposes this weakness before first flight — preventing potential six-figure losses in hardware, liability, operational downtime, and public trust. This is not just simulation. This is measurable deployment readiness. As autonomous systems scale globally, validation must scale with them. RAKSHAK ensures robots are trusted before they are deployed.