In this paper, the results and methodology of a framework to enable run-time safety compliance andself-certification of robotics is presented. This transferable framework is verified within a practicaldemonstration scenario, based on asset inspection within a confined space, and representing a Beyond VisualLine of Sight (BVLOS) use case. The methodology of the framework is based on computationally efficientanalysis to support run-time, front-end, data analysis and adaptive decision-making. Utilizing the HuskyA200 platform, manufactured by Clearpath, front-end datasets on the mission status and diagnostics ofcritical sub-systems within the Husky platform are used to update run-time system ontologies. The holisticand hierarchical-relational model of the robot integrates the automata of the sensed and some non-sensedcomponents, using prior knowledge, such as risk assessments and offline reliability data, to support run-timeanalysis, such as fault prognosis, detection, isolation and diagnosis. These computationally efficient dataand system analyses then enable faults to be translated into failure modes that can affect decision makingduring the mission. With respect to challenges of a dynamic environment, namely ambient conditions orthe presence of unexpected people, Frequency Modulated Continuous Wave (FMCW) sensing is integratedonto the husky platform. The FMCW supports localization in opaque environments and detection of peoplewithin and out-with of the confined space, as well as enabling integrity analysis of the infrastructure. Theframework presents its results within a symbiotic digital twin of the infrastructure and robotic platform.With fully synchronized communication and data streams, the interactive digital twin provides operationaldecision support and trust for human in the loop operators of varying skill levels. The presentation ofactionable information to the end user is used to support improvements in productivity associated with assetintegrity as well as supporting user trust in safety during a BVLOS mission.