HYBRID METHOD FOR MOBILE ROBOT TRAJECTORY PLANNING IN A DYNAMIC UNDETERMINED ENVIRONMENT

І. В. Гребеннік, О. О. Чала, В. В. Невлюдова, Н. П. Демська

Abstract


This article presents the results of a study aimed at developing a hybrid method for planning the trajectories of a mobile robot in a dynamic, uncertain environment, based on a combination of Risk-Aware MPC and neural networks for predicting the movement of obstacles. The aim of the study is to improve the efficiency and safety of autonomous navigation by taking into account collision risks and environmental uncertainty. The subject of the study is the process of planning trajectories for a mobile robot under conditions of dynamic environmental changes. The subject of the study is mathematical models and hybrid planning algorithms that integrate obstacle behaviour prediction and risk-aware optimal control. The study utilises methods of mathematical modelling, optimal control theory, Model Predictive Control, machine learning and numerical simulation in the Python environment. The scientific novelty of the work lies in the development of an integrated approach that combines neural network prediction with covariance estimation and Risk-Aware MPC, allowing for both the expected behaviour of obstacles and the level of uncertainty in the forecast to be taken into account. The results of numerical modelling confirm improved motion accuracy, reduced integral errors and the assurance of safe navigation whilst minimising the risk of collision. The proposed method demonstrates stability and effectiveness under complex dynamic conditions and can be used in autonomous navigation tasks, robotic systems and civil security systems.


Keywords


This article presents the results of a study aimed at developing a hybrid method for planning the trajectories of a mobile robot in a dynamic, uncertain environment, based on a combination of Risk-Aware MPC and neural networks for predicting the movement

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References


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DOI: https://doi.org/10.32620/oikit.2026.108.05

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