This article is devoted to the development of a collision detection technique using a polygonal mesh and neural networks. Collisions are an important aspect of realistically simulating physical interactions. Traditional collision detection methods have certain limitations related to computational accuracy and computational complexity. A new approach based on the use of neural networks for collision detection with polygonal meshes is proposed. Neural networks have shown excellent results in various computer vision and image processing tasks, and in this context they can be effectively applied to polygon pattern analysis and collision detection. The main idea of ​​the technique is to train a neural network on a large data set containing information about the geometry of objects and their movement for automatic collision detection. To train the network, it is necessary to create a special module responsible for storing and preparing the dataset. This module will provide collection, structuring and storage of data about polygonal models, their movements and collisions. The work includes the development and testing of a neural network training algorithm on the created dataset, as well as assessing the quality of network predictions in a controlled environment with various collision conditions.

Keywords: modeling, collision detection techniques using polygonal meshes and neural networks, dataset, assessing the quality of network predictions

The article explores the problem of creating aircraft flight models in the Simulink environment. The reference systems in which transformations are carried out are considered. The equations of motion used in the simplest converters are given. The initial conditions for the equations are determined: the speed of the body, the angular orientation of the body's pitch position, the angle between the velocity vector and the body, the speed of rotation of the body, the initial position, the mass and inertia of the body, the source of gravity, the acceleration due to gravity, the curb and total mass of the body, speed of air flow, inertia of an empty and full body, flight trajectory, etc. An analysis of converters of aerodynamic forces and moments into the trajectory of motion as part of an aerospace package in the Simulink environment was carried out. Recommendations are given for their use for various modeling purposes. The results of modeling a simple converter with three degrees of freedom are presented.

Keywords: modeling, MatLab, Simulink, equations of motion, aerodynamic torque, flight path, coordinate transformations, reference system, degrees of freedom

A Simulink model of a lightweight aircraft is being studied as part of the Aerospace Blockset package, including a system model of the aircraft, an environmental model, a model of pilot influences, and a visualization block. The structure of the flight model is considered and models of the effects of the environment and wind are disclosed in detail, consisting of blocks of physical terrain features, wind models and an atmospheric model, a gravity model, each of which is set to an altitude. The Wind Shear Model block calculates the amount of wind shear as a function of altitude and measured speed wind. The Discrete Wind Gust Model block determines the resulting wind speed as a function of the distance traveled, the amplitude and length of the gust. The turbulence equations comply with the MIL-F-8785C specification, which describes turbulence as a random process determined by velocity spectra. Simulation results are presented that reflect changes in the trajectory of movement under various wind influences specified in the wind speed gradient block.

Keywords: modeling, airplane flight, Simulink, Aerospace Blockset, crosswind, turbulence, turbulence equations, gravity model, motion trajectory

The problem of vulnerabilities in the Robot Operating System (ROS) operating system when implementing a multi-agent system based on the Turtlebot3 robot is considered. ROS provides powerful tools for communication and data exchange between various components of the system. However, when exchanging data between Turtlebot3 robots, vulnerabilities may arise that can be used by attackers for unauthorized access or attacks on the system. One of the possible vulnerabilities is the interception and substitution of data between robots. An attacker can intercept the data, change it and resend it, which can lead to unpredictable consequences. Another possible vulnerability is unauthorized access to the commands and control of Turtlebot3 robots, which can lead to loss of control over the system. To solve these vulnerabilities, methods of protection against possible security threats arising during the operation of these systems have been developed and presented.

Keywords: Robotic operating system (ROS), multi-agent system, system packages, encryption, SSL, TLS, authentication and authorization system, communication channel, access restriction, threat analysis, Turtlebot3

The article explores the interaction of Tello EDU, a small-sized educational drone, with Turtlebot3, an unmanned ground vehicle, in rooms with a weak signal. The article examines how, using a local network and the robot operating system (ROS), it is possible to achieve effective collaboration between these two devices. It analyzes how a local network can be used to broadcast data and monitor devices in conditions of a weak external signal. The role of ROS as the main tool for managing and interacting with devices is being investigated. In addition, the article examines specific use cases, including interaction and coordination between Tello EDU and Turtlebot3. A diagram of the interaction of two unmanned vehicles is also presented, a detailed description of their operation is described, and Python code is presented using various libraries based on the ROS robotic operating system.

Keywords: Tello Edu, operating system (ROS), UAV, local area network, Wi-fi, nodes, SLAM, weak signal, route planning, autonomous robot, Turtlebot3