Designing a computerized information processing system to build a movement trajectory of an unmanned aircraft

Volodymyr Kvasnikov,Oleksii Shelukha,Dmytro Ornatskyi,Maryna Graf

doi:10.15587/1729-4061.2021.225501

Abstract

This paper addresses the issue of developing a computerized system for processing information in the construction of the trajectory of an unmanned aircraft (UAC), a remotely-piloted aviation system (RPAS), or another robotic system. Resolving this task involves the neural network learning algorithms based on the mathematical model of movement. The construction of such a trajectory between two specified destinations has been considered that provides for the possibility of bypassing static and dynamic obstacles. The specified trajectory is divided into several smaller parts. The possibility of restructuring when changing the position of obstacles in space has been considered. A UAC flight control algorithm has been developed, which implies training a neural network for bypassing obstacles of different sizes. To predict the development of the situation when an object moves between two specified points in space, it is proposed to use the Q-Learning algorithm. It has been shown that the smallest number of steps required for moving along a specified trajectory is 18, the largest is 273 steps. In case of distortion during data transmission, the training of the neural network makes it possible to reduce the possibility of collision with obstacles by improving the accuracy and speed of information transfer between the on-board computer and operator. A system of the video support to moving objects was modeled; dependence charts of the normalized frame size at different parameter values were built. Using the charts makes it possible to determine the function of the maneuver intensity. Existing neural network learning methods such as CNN and LSTM were compared. It has been proven that the success rate reaches 74 % when using CNN only, while it amounts to 92 % at the hybrid application of CNN+LSTM. The simulation results have demonstrated the high efficiency of the developed algorithm

Highlights

The application of computer systems and components over recent years has been characterized by an increase in productivity, performance, and energy efficiency
It has been proven that the success rate reaches 74 % when using convolutional neural network (CNN) only, while it amounts to 92 % at the hybrid application of CNN+long short-term memory (LSTM)
When considering an unmanned aircraft vehicle (UAV) or a remotely-piloted aviation system (RPAS), those processes include the planning of a natural motion trajectory with respect to the observed objects

Summary

Introduction

The application of computer systems and components over recent years has been characterized by an increase in productivity, performance, and energy efficiency. New technologies and design algorithms are used to modernize and build new computerized systems, in particular for solving tasks of information transfer and processing, flight trajectory planning, video data processing, etc. Most of these tasks relate to the process of observing and bypassing fixed objects [1]. When considering an unmanned aircraft vehicle (UAV) or a remotely-piloted aviation system (RPAS), those processes include the planning of a natural motion trajectory with respect to the observed objects To resolve such tasks, the most used tools are the neural network planning algorithms, the construction of algorithms on graphs [2, 3], as well as the application of random tree methods [4, 5]. There is a need to design a computerized information processing system for the construction of UAV movement trajectory with improved characteristics

Literature review and problem statement

The aim and objectives of the study

The study materials and methods

Findings

Conclusions