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ARVS: Adaptive Robust Verification System

Authors: Rathi, Chirag;

ARVS: Adaptive Robust Verification System

Abstract

ARVS v1.0.0 — Initial Release Adaptive Robust Verification System — A research-grade autonomy architecture for spacecraft, rovers, and safety-critical autonomous systems. 🔬 What is ARVS? ARVS is a full-stack autonomy framework built for environments where failure is not an option — planetary rovers, orbital spacecraft, and multi-robot systems. It implements a five-stage pipeline: Sense → Think → Verify → Optimise → Enforce Every stage is formally grounded: Bayesian state estimation, a frozen axiom constitution, QUBO-based optimization, and a sub-millisecond C++ safety gate. ✨ Key Features 25-Axiom Frozen Constitution — Immutable safety axioms enforced identically in both Python and C++, with a SHA-256 cryptographic audit chain 8-Dimensional Risk Quantifier — Risk is embedded directly into the MDP reward function: R(s,a) = gain − λ·risk − μ·energy ±15% Bounded Adaptive Learning — Parameters cannot drift beyond safe bounds per cycle; risk_penalty is floor-guarded by Axiom L1 Real-Time C++ Enforcement — Zero-heap hot path, fixed-size arrays, SCHED_FIFO watchdog, latching emergency stop, full ROS2 node wrappers Quantum-Ready Optimization — QUBO solver targeting D-Wave Leap with automatic Tabu fallback Multi-Source Telemetry — Live loaders for NASA REMS (Mars weather), SPICE (orbital mechanics), ESA Mars Express, and ISS OSDR 4 Validated Scenarios — Normal ops, fault injection (1.8× torque spike), communication blackout (300s), and axiom cascade → safe hold 69-Test CI Suite — Full simulation test coverage with forensic JSON audit trails 📦 What's Included Component Description ARVS/ Python core (HAL, EKF, risk, planning, learning, audit) cpp/ C++ real-time components + ROS2 nodes simulation/ 4-scenario simulation engine + pre-generated outputs Docs/ Architecture, API reference, axioms, reliability analysis 🛰️ Tested Against Real Space Telemetry Validated across 16 runs (4 scenarios × 4 data sources) using actual NASA and ESA mission data as input. Author: Chirag Rathi — Independent Researcher ORCID: 0009-0008-1682-4369 License: Apache-2.0

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Keywords

: autonomous systems, spacecraft autonomy, formal verification, safety-critical systems , fault detection

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
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