Markov modelling of human-machine interaction in an augmented reality environment for UAV/UGV-based hazardous area monitoring systems
Year: 2025
Type: article
Abstract: The subject of the study is Markov processes used for the formal description of the dynamics of states of unmanned vehicles controlled by an operator through augmented reality–based human–machine interfaces. Within the scope of the research, unmanned aerial and ground vehicles are considered as complex multi-state technical systems whose functioning is determined both by their technical characteristics and by the specific features of human interaction with the control interface. The aim of the study is to assess the impact of augmented reality–based human–machine interaction interfaces on the error-free decision-making of unmanned system operators, as well as on their responsiveness during control and reaction to changes in system states. The objectives of this study are to develop Markov models for the following scenarios: (a) without considering system failures and operator errors, with full recovery; (b) without considering failures, but allowing for operator errors, with full recovery; (c) considering system failures without operator errors, with full recovery; (d) considering both system failures and operator errors, with full recovery; and (e) without considering failures, allowing for operator errors, with the presence of a redundant unmanned system. The resulting Markov chains are intended to be used for modelling and subsequent comparison of the impact of different operating conditions on the system. As a result of the study, the following were obtained: (a) a classifier of states of unmanned aerial vehicles within a hazardous environment monitoring system based on the possible presence of failures, operator errors, and system redundancy; (b) Markov models for various system operation scenarios; and (c) simulation results of system operation based on the developed Markov models. Conclusion. The scientific novelty is as follows: a method for assessing the availability of monitoring systems with augmented reality–based human–machine interaction interfaces is proposed, which is based on single- and multi-fragment Markov models that take into account operator actions, partial failures, and the availability of reserve unmanned aerial vehicles. The proposed method quantitatively evaluates the impact of augmented reality not only on subjective indicators but also on the overall system availability and reliability indicators.
Language: English
Subfield: Human Factors and Ergonomics
Field: Social Sciences
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $107
OpenAlex ID: W7124548411