This paper proposes an algorithm that assesses the angular orientation of a mobile robot with respect to its referential position or a map of the surrounding space. In the framework of the suggested method, the orientation problem is converted to evaluating a dimensional rotation of the object that is abstracted as a polygon (or a closed polygonal chain). The method is based on Hough transform, which transforms the measurement space to a parametric space (in this case, a two-dimensional space [θ, r] of straight-line parameters). The Hough transform preserves the angles between the straight lines during rotation, translation, and isotropic scaling transformations. The problem of rotation assessment then becomes a one-dimensional optimization problem. The suggested algorithm inherits the Hough method’s robustness to noise.
This article describes an approach to solution of a problem of planning a mobile robot's path in 2-dimentional space with obstacles. It gives the problem statement, which implies that there is no prior information about surrounding environment. It is supposed that the robot gathers real-time information via on-board sensors. The article also presents a theoretical analysis of such approach performance, along with comparison of the proposed approach to the existing ones, and demonstration of the suggested one's advantages. The simulation experiment results fully proving the theoretical thesis are also represented.
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