Verifying Autonomous Mobile Robots with Quantum Machine Learning Test Oracles
Murali Krishna Pasupuleti · International Journal of Academic and Industrial Research Innovations(IJAIRI) · 2021
Abstract: This study addresses the oracle problem in autonomous mobile robot (AMR) verification by introducing quantum machine learning (QML)–based test oracles designed for safety-critical perception, localization, and navigation tasks. Concept analysis identifies requirements for AMR oracles—semantic correctness against task specifications, tolerance to partial observability, calibrated uncertainty, and robustness to distribution shift. Building on these requirements, a layered framework is articulated that couples (i) formal property capture using temporal logic over robot signals, (ii) scenario and metamorphic relation generation for label construction, and (iii) hybrid quantum–classical learners that map multi-modal telemetry into a decision about pass/fail with confidence. The central problem considered is the insufficiency of heuristic or proxy metrics to deliver reliable verdicts under sensor noise, sim-to-real gap, and rare hazard modes. Methodology integrates quantum feature maps or variational circuits with classical encoders, multi-objective training for accuracy–calibration trade-offs, and conformal prediction for uncertainty quantification; oracle outputs are consumed by a runtime monitor for gated execution. Evaluation on benchmarks representative of AMR perception and navigation indicates statistically significant gains in defect detection and earlier hazard discovery relative to classical baselines, with improved calibration and resilience to out-of-distribution inputs. The impact is a verification pathway that elevates oracle reliability without prohibitive runtime cost, enabling auditability and tighter safety cases for deployment. Implications include standard-aligned V&V artefacts, ROS2-compatible runtime hooks, and a blueprint for integrating QML oracles into certification workflows for mobile robotics. Keywords: quantum machine learning, test oracle, autonomous mobile robots, verification and validation, metamorphic testing, temporal logic specifications, uncertainty quantification, out-of-distribution detection, hybrid quantum–classical models, runtime monitoring, safety assurance, ROS2 integration, sim-to-real robustness