Identification of Neural Network Model of Robot to Solve the Optimal Control Problem

To calculate the optimal control, a satisfactory mathematical model of the control object is required. Further, when implementing the calculated controls on a real object, the same model can be used in robot navigation to predict its position and correct sensor data, therefore, it is important that the model adequately reflects the dynamics of the object. Model derivation is often time-consuming and sometimes even impossible using traditional methods. In view of the increasing diversity and extremely complex nature of control objects, including the variety of modern robotic systems, the identification problem is becoming increasingly important, which allows you to build a mathematical model of the control object, having input and output data about the system. The identification of a nonlinear system is of particular interest, since most real systems have nonlinear dynamics. And if earlier the identification of the system model consisted in the selection of the optimal parameters for the selected structure, then the emergence of modern machine learning methods opens up broader prospects and allows you to automate the identification process itself. In this paper, a wheeled robot with a differential drive in the Gazebo simulation environment, which is currently the most popular software package for the development and simulation of robotic systems, is considered as a control object. The mathematical model of the robot is unknown in advance. The main problem is that the existing mathematical models do not correspond to the real dynamics of the robot in the simulator. The paper considers the solution to the problem of identifying a mathematical model of a control object using machine learning technique of the neural networks. A new mixed approach is proposed. It is based on the use of well-known simple models of the object and identification of unaccounted dynamic properties of the object using a neural network based on a training sample. To generate training data, a software package was written that automates the collection process using two ROS nodes. To train the neural network, the PyTorch framework was used and an open source software package was created. Further, the identified object model is used to calculate the optimal control. The results of the computational experiment demonstrate the adequacy and performance of the resulting model. The presented approach based on a combination of a well-known mathematical model and an additional identified neural network model allows using the advantages of the accumulated physical apparatus and increasing its efficiency and accuracy through the use of modern machine learning tools.

Methodology for Ensuring a Comfortable Microclimate State in a Smart Home Using an Ensemble of Fuzzy Artificial Neural Networks

Analysis of the application of smart home technology indicates an insufficient level of controllability of its infrastructure, which leads to excessive consumption of energy and information resources. The problem of managing the digital infrastructure of human living space, is associated with a large number of highly specialized solutions for home automation, which complicate the management process. Smart home is considered as a set of independent cyber-physical devices aimed at achieving its goal. For coordinated work of cyber-physical devices it is proposed to provide their joint work through a single information center. Simulation of device operation modes in a digital environment preserves the resource of physical devices by making a virtual calculation for all possible variants of interaction of devices between themselves and the physical environment. A methodology for controlling the microclimate of a smart home using an ensemble of fuzzy artificial neural networks is developed, with the example of joint use of air conditioning, ventilation and heating. The neural network algorithm allows you to monitor the parameters of the physical environment, predict the modes of cyber-physical devices and generate control signals for each of them, ensuring the joint operation of devices with minimal resource consumption and information traffic. A variant of practical implementation of a smart home climate control system on the example of a multifunctional educational computer class is proposed. Hybrid neural networks of air conditioning, ventilation and heating systems were developed. The testing of the microclimate control system of a multifunctional university classroom using hybrid neural networks was carried out, a programmable logic controller of domestic production was used as a control device. The goal of management based on cooperating cyber-physical devices is to achieve a minimum of power and information traffic when they work together.

Synthesis of Line of Sight Angle Coordinate Filter on the Basis of Interactive Multi-Model Evaluation Algorithm

On the basis of the tracking multi-loop target angle coordinate system, the article has selected and proposed a interactive multi-model adaptive filter algorithm to improve the quality of the target phase coordinate filter. In which, the 3 models selected to design the line of sight angle coordinate filter; Constant velocity (CV) model, Singer model and constant acceleration model, characterizing 3 different levels of maneuverability of the target. As a result, the evaluation quality of the target phase coordinates is improved because the evaluation process has redistribution of the probabilities of each model to suit the actual maneuvering of the target. The structure of the filters is simple, the evaluation error is small and the maneuvering detection delay is significantly reduced. The results are verified through simulation, ensuring that in all cases the target is maneuvering with different intensity and frequency, the line of sight angle coordinate filter always accurately determines the target angle coordinates.

Spoofing Attack Detection Method for UAV Navigation System

An implementation of methods for protecting unmanned aerial vehicles (UAVs) from spoofing attacks of the global positioning system (GPS) to ensure safe navigation is discussed in this paper. The Global Navigation Satellite System (GNSS) is widely used to locate UAVs and is by far the most popular navigation solution. This is due to the simplicity and relatively low cost of this technology, as well as the accuracy of the transmitted coordinates. However, there are many security threats to GPS navigation. Primarily this is due to the nature of the GPS signal, the signal is transmitted in the clear, so an attacker can block or tamper with it. This study analyzes the existing GPS protection methods. As part of the study, an experimental stand and scenarios of attacks on the UAV GPS system were developed. Data from the UAV flight logbook was collected and an analysis of cyber-physical parameters was carried out to see an effect of the attack on the on-board sensors readings. Based on this, a new method for detecting UAV anomalies was proposed, based on an analysis of changes in UAV internal parameters. This self-diagnosis method allows the UAV to independently assess the presence of changes in its subsystems and identify signs of a cyberattack. To detect an attack, the UAV collects data on changes in cyber-physical parameters over a certain period of time, then updates this data. As a result it is necessary for the UAV to determine the degree of difference between the two time series of the collected data. The greater the degree of difference between the updated data and the previous ones, the more likely the UAV is under attack.

An Innovative Approach to Automated Photo-Activation of Crop Acreage Using UAVs to Stimulate Crop Growth

Photoactivation of plants by laser treatment is a promising direction in the development of modern agricultural production. Treatment of plants with radiation with specified characteristics stimulates the development of plants, the formation of generative traits and an increase in yield. An approach based on the use of a specialized laser installation mounted on an unmanned aerial vehicle (UAV) is proposed to automate the process of photoactivation of large cultivated areas. It is possible to perform laser activation of large areas with minimal expenditure of time and human resources due to autonomous processing of the field with the help of UAVs. An algorithm for calculating a covering trajectory for covering large rectangular areas with a laser spot with given characteristics is proposed in the paper. A methodology for calculating the required power of the laser installation depending on the altitude and flight time of the UAV is presented. The advantage of the developed approach is its versatility, since this approach takes into account the characteristics of a laser installation and can be used with devices of various types. Depending on the laser parameters, the algorithm builds such a trajectory for the UAV so that the irradiation of plant seedlings is uniform throughout the entire processing process. Field experiments were conducted when the UAV moved along the calculated trajectory at a speed of 0.3 m/s and the average processing time for a field 200 m long and 1 m wide was 9 minutes. The results of field experiments show that laser irradiation on most of the studied crops increased the yield and height of the stand (in cereals - in four out of six crops, in legumes - in four out of five studied crops). The proposed algorithm for constructing a path for uniform laser irradiation of a site takes into account the area of the laser spot to ensure the required radiation characteristics when using any laser installation.

Method for Estimating Time of Wireless Transfer of Energy Resources Between Two Robots

The energy capacity of the batteries used as the main power source in mobile robotic devices determines the autonomous operation of the robot. To plan the execution of tasks by a group of robotic tools in terms of time consumption, it is important to take into account the time during which the battery of each individual robot is charged. When using wireless power transfer, this time depends on the efficiency of the power transfer system, on the power of the transferring part of the system, as well as on the level of charge required to recharge. In this paper, we propose a method for estimating the time of transfer of energy resources between two robots, taking into account these parameters. The proposed method takes into account the application of the algorithm for the final positioning of robots, the assessment of linear offsets between robots, includes the calculation of efficiency, as well as the determination of the battery charge time, taking into account the parameters obtained at the previous stages of the method. The final positioning algorithm for robots uses algorithms for processing data from a robot vision system to search for fiducial markers and determine their spatial characteristics to ensure the final positioning of mobile robotic platforms. These characteristics are also used to determine the linear offsets between robots, on which the efficiency of energy transfer depends. To determine it, the method uses a mathematical model of the energy characteristics of the wireless power transfer system and the obtained linear offsets. At the last stage of the method, the time for charging the battery of the mobile robot is calculated, taking into account the data from the previous stages. Application of the proposed method to simulate the positioning of robots in a certain set of points in the working space will reduce the time spent on charging the robot battery when using wireless power transfer. As a result of the simulation, it was determined that the transfer of energy resources between robots took place with an efficiency in the range from 58.11% to 68.22%, and out of 14 positioning points, 3 were identified with the shortest energy transfer time.

Effective Functioning of a Mixed Heterogeneous Team in a Collaborative Robotic System

The study describes a collaborative robot (cobot) as one of the types of intelligent robotics and its distinctive features compared to other types of robots. The paper presents a collaborative robotic system as a single complex system in which actors of different types – cobots and human workers – perform collaborative actions to achieve a common goal. Elements of a collaborative robotic system, as well as processes and entities that directly influence it are represented. The key principles of Human-Robot Collaboration are described. A collaborative robotic system is analyzed both as a multi-agent system and as a mixed team, whose members are heterogeneous actors. The relevance of the work lies in a weak level of research on issues of formation of mixed teams of people and cobots and distribution of tasks in such teams, taking into account features of these two types of participants and requirements of their safe collaboration. This work focused on a formation of mixed teams of elements of a single complex human-cobot system, the distribution of tasks among the members of such teams, taking into account the need to minimize costs for its participants and the heterogeneity of the team. As part of the study, the problem of forming a mixed heterogeneous team of people and cobots, and distribution of work among its members, as well as the corresponding mathematical description are presented. Specific cases of the problem, including different cost functions of different types of participants, a limited activity of the team’s members, the dependence of the cost function of the participants of one type on the number of participants of another type, as well as an arbitrary number of works assigned to the team’s members are considered.

Forecasting Development of COVID-19 Epidemic in European Union Using Entropy-Randomized Approach

The paper is devoted to the forecasting of the COVID-19 epidemic by the novel method of randomized machine learning. This method is based on the idea of estimation of probability distributions of model parameters and noises on real data. Entropy-optimal distributions correspond to the state of maximum uncertainty which allows the resulting forecasts to be used as forecasts of the most "negative" scenario of the process under study. The resulting estimates of parameters and noises, which are probability distributions, must be generated, thus obtaining an ensemble of trajectories that considered to be analyzed by statistical methods. In this work, for the purposes of such an analysis, the mean and median trajectories over the ensemble are calculated, as well as the trajectory corresponding to the mean over distribution values of the model parameters. The proposed approach is used to predict the total number of infected people using a three-parameter logistic growth model. The conducted experiment is based on real COVID-19 epidemic data in several countries of the European Union. The main goal of the experiment is to demonstrate an entropy-randomized approach for predicting the epidemic process based on real data near the peak. The significant uncertainty contained in the available real data is modeled by an additive noise within 30%, which is used both at the training and predicting stages. To tune the hyperparameters of the model, the scheme is used to configure them according to a testing dataset with subsequent retraining of the model. It is shown that with the same datasets, the proposed approach makes it possible to predict the development of the epidemic more efficiently in comparison with the standard approach based on the least-squares method.

Underwater Robot Manipulator Control

This paper deals with the problem of bringing the end effector (grip center) of an underwater vehicle anthropomorphic manipulator to a predetermined position in a given time using the terminal state method. A dynamic model with the account of joint drives dynamics is formulated on the basis of obtained kinematic model constructed by using the Denavit-Hartenberg method (DH model). The DH model is used in a terminal nonlinear criterion that displays estimate of the proximity of the effector's orientation and position to the specified values. The dynamic model is adapted for effective application of the author's terminal state method (TSM) so that it forms a system of differential equations for the rotation angles of manipulator links around the longitudinal and transverse axes, having only desired TSM-controls in the right parts. The converted model provides simplifications of controls calculation by eliminating the numerical solution of special differential equations, that is needed in the case of using in TSM nonlinear dynamic models in general form. The found TSM-controls are further used in expressions for control actions on joints electric drives obtained on the basis of electric drives dynamic models. Unknown drives parameters as functions of links rotation angles or other unknown factors, are proposed to be determined experimentally. Such two-step procedure allowed to get drive control in the form of algebraic and transcendental expressions. Finally, by applying the developed software, simulation results of the manipulator end effector moving to the specified positions on the edge of the working area are presented. The resulting error (without accounting measurement error) does not exceed 2 centimeters at the 1.2 meters distance by arm reaching maximum of length ability. The work was performed under the Federal program of developing a robotic device for underwater research in shallow depths (up to 10 meters).

Teleoperated Service Robot with an Immersive Mixed Reality Interface

Teleoperated service robots can perform more complex and precise tasks as they combine robot skills and human expertise. Communication between the operator and the robot is essential for remote operation and strongly affects system efficiency. Immersive interfaces are being used to enhance teleoperation experience. However, latency or time delay can impair the performance of the robot operation. Since remote visualization involves transmitting a large amount of video data, the challenge is to decrease communication instability. Then, an efficient teleoperation system must have a suitable operation interface capable of visualizing the remote environment, controlling the robot, and having a fast response time. This work presents the development of a service robot teleoperation system with an immersive mixed reality operation interface where the operator can visualize the real remote environment or a virtual 3D environment representing it. The virtual environment aims to reduce the latency on communication by reducing the amount of information sent over the network and improve user experience. The robot can perform navigation and simple tasks autonomously or change to the teleoperated mode for more complex tasks. The system was developed using ROS, UNITY 3D, and sockets to be exported with ease to different platforms. The experiments suggest that having an immersive operation interface provides improved usability for the operator. The latency appears to improve when using the virtual environment. The user experience seems to benefit from the use of mixed reality techniques; this may lead to the broader use of teleoperated service robot systems.