This paper details the results of a survey on safety and reliability of commercial Unmanned Aerial Systems (UAS), issued to 1500 companies in the UK, with a 10% response rate. The results of this study concur with previous studies that commercial UAS have a failure rate of 1/103 flight hours. Furthermore, we found a reported 49% of companies believe a critical failure will occur within every 500 hours of flight. We investigate the responses concerning UAS subsystems; the experience of onboard intelligent systems, current practices around the frequency of maintenance and servicing of subsystems, and subsystems identified in order of the highest likelihood of failure. Informed by the results of this survey and a state of the art literature review we identify emerging technologies and methods as candidate solutions to the respondents reported challenges, such as; Integrated Vehicle Health Management (IVHM), Formal Methods, Simulations and Fault Tolerant Control (FTC). The integration of these techniques, in a systematic framework with a supporting automatic tool-chain, is identified as a candidate solution to reliability and certification challenges in UAS.@en

The battery is a key component of autonomous robots. Its performance limits the robot’s safety and reliability. Unlike liquid-fuel, a battery, as a chemical device, exhibits complicated features, including (i) capacity fade over successive recharges and (ii) increasing discharge rate as the state of charge (SOC) goes down for a given power demand. Existing formal verification studies of autonomous robots, when considering energy constraints, formalise the energy component in a generic manner such that the battery features are overlooked. In this paper, we model an unmanned aerial vehicle (UAV) inspection mission on a wind farm and via probabilistic model checking in PRISM show (i) how the battery features may affect the verification results significantly in practical cases; and (ii) how the battery features, together with dynamic environments and battery safety strategies, jointly affect the verification results. Potential solutions to explicitly integrate battery prognostics and health management (PHM) with formal verification of autonomous robots are also discussed to motivate future work.@en