Authors V.Ph. Guzik, V.A. Pereverzev, A.O. Pyavchenko, R.V. Saprykin
Month, Year 02, 2016 @en
Index UDC 681.511.4+004.896:519.876.5
Abstract In this report we are describing the design principles for extrapolating multidimensional neural-network planner (EMNP) for a intellectual position-trajectory control system for moving objects. Thus we present the results of our study of a modernized neural-network moving object (MO) movement planning method that is used in the multidimensional space based on the bionic environment sensing in undefined conditions with barriers that can move dynamically. We consid- er a synthetic hierarchical structure of an extrapolating multidimensional neural network (MNN) as the main principal to structurize and build EMNP. Such MNN has separate layers used for different stages of environment model processing provided by the vision system (VS) in the above mentioned conditions. The hierarchical structure of the MNN is based on object-oriented parametric synthesis, synthesis of weighted object position features with time sampling, direction vector plans used to extrapolate such features, that determine probabilistic special position of related objects. We analyzed the results of selected methods used to detect moving barriers of round or spherical type and predict their trajectories in space of the corresponding dimension based on VS data. We present the results of software-based modelling of this approach to build EMNP for the intellectual position-trajectory control system for moving objects in a multidimensional space using the simulation software complex we developed.

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Keywords Robotic mobile unit; bionic environment sensing; extrapolating multidimensional neural-network planner; multidimensional neuronetwork; building principles; weighted object position features; direction vector; extrapolation of values; software-based simulation modelling.
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