Support for autonomous navigation of mobile robot based on its internal neural network model

E.Yu. Shmalko, I.V. Prokopiev, A.I. Diveev


Autonomous navigation is one of the key tasks in the development of control systems for real autonomous mobile robots. The main source of information about the location of the robot is its sensory system. However, any sensory system has weaknesses. In this regard, there is a need to support the navigation system in some way that does not depend on the readings of the sensors. The paper presents the developed technology for determining the position of a mobile robot in an autonomous mode based on an internal model. The approach involves using on board an exact model of a real robot identified by a neural network. Under the conditions of modern computing power, obtaining such a model is much easier than its analytical derivation. At the same time, the model takes into account various dynamic properties of the model, which are difficult to take into account manually. When identifying, it is proposed to use a mixed approach, when the kinematic part of the robot model is considered known, and the dynamic properties of the model are identified by a neural network. Having a fairly accurate model on board, the robot adjusts its position determined by the sensors in accordance with its position obtained using the neural network model. In case of failures in the operation of the sensors or their disconnection, the robot can continue the autonomous execution of the task for some time. In the experimental part, the problem of autonomous movement of a mobile robot along a given trajectory is considered.

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