Mathematical model of a mobile robot digital twin for autonomous navigation and energy consumption monitoring
Igor Nevlyudov, Artem Bronnikov, Olena Chala, Nataliia Demska, Matvii Bilousov
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Source: Crossref
Published: Sep 30, 2026
DOI: 10.30837/2522-9818.2026.3.079
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The subject of this study is the methods and parameters of virtual modeling of a mobile robot digital twin for autonomous navigation and energy-consumption monitoring. The aim of the work is to improve the efficiency of mobile robot control by developing a mathematical and computer model of its digital twin for autonomous navigation and energy-consumption monitoring. The tasks are to analyze modern digital twin technologies; substantiate the parameters of a three-dimensional simulation environment; develop a virtual model of a mobile robot in the selected environment; develop a mathematical model of the mobile robot digital twin; conduct experimental studies using the developed models; and evaluate motion correction, navigation safety, virtual sensor operation, and energy consumption. The methods include computer modeling in the Webots environment, analysis of differential-drive kinematics and dynamics, parametric tuning of a proportional-integral-derivative (PID) controller, modeling of LiDAR and infrared sensors, and modeling of battery-discharge dynamics. Results. A mathematical model of the mobile robot digital twin was developed as a six-dimensional state vector integrating robot pose, wheel angular velocities, and battery energy. For a rectangular track with 90° turns, the proportional gain had to be increased by a factor of 1.8 and the derivative gain by a factor of 4.5 compared with the circular track , . The system provided an emergency LiDAR stop at a distance of 0.3 m. In maze navigation, a threshold of 0.6 m enabled collision-free completion in 60 of 60 runs across three starting positions and two maze configurations. The experiments also confirmed a quadratic dependence of power consumption on wheel angular velocity. Conclusions. The proposed model integrates mechatronics, autonomous navigation, sensor modeling, and energy-consumption calculations within a digital twin framework. Sharp turns were identified as the most energy-intensive maneuvers. The model provides a basis for further research on neural-network control and SLAM-based route planning.
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