Vol 22, No 1 (2023)
Robotics, automation and control systems
Cooperative Control of Traffic Signals and Vehicle Trajectories
Abstract
The transportation system is one of the most important parts of the country's economy. At the same time, the growth in road traffic has a significant negative impact on the economic performance of the industry. One of the ways to increase the efficiency of using the transportation infrastructure is to manage traffic flows, incl. by controlling traffic signals at signalized intersections. One of the trends in the development of intelligent transportation systems is the creation of vehicular ad hoc networks that allow the exchange of information between vehicles and infrastructure, as well as the development of autonomous vehicles. As a result, it becomes possible to formulate the problem of cooperative control of vehicle trajectories and traffic signals to increase the capacity of intersections and reduce fuel consumption and travel time. This paper presents a method for managing traffic flow at an intersection, which consists of the cooperative control of traffic signals and trajectories of connected/autonomous vehicles. The developed method combines an algorithm for the adaptive control of traffic signals based on a deterministic model for predicting the movement of vehicles and a two-stage algorithm for constructing the trajectory of vehicles. The objective optimization function used to construct the optimal trajectories takes into account fuel consumption, travel time on the road lane, and waiting time at the intersection. Experimental studies of the developed method were carried out in the microscopic traffic simulation package SUMO using three simulation scenarios, including two synthetic scenarios and a scenario in a real urban environment. The results of experimental studies confirm the effectiveness of the developed method in terms of fuel consumption, travel time, and waiting time in comparison with the adaptive traffic signal control algorithm.
Informatics and Automation. 2023;22(1):5-32
5-32
Navigation Data Exchange for Traffic Control
Abstract
An increase in the number of cars is higher than rates of transport infrastructure development, resulting in a reduction of cargo and passenger transportation efficiency in city conditions. Simulation of flow irregularity in time (peak hour) shows the key role of a car motion interval as a factor of overcoming accumulation at average speed reduction in conditions of highly loaded roads. To reduce the effective time of driver reaction, defining the least distance between cars, it is necessary to minimize the influence of human factors. Automation of the process (unmanned control) requires an effective exchange of navigation and route data between traffic participants. A summary of requirements for such an information exchange system defines the priority of the suggested communication and navigation system (CNS) on the base of radio broadcast communication. Its application gives an opportunity to rise simultaneously traffic safety and efficiency. An increase in neighbor driver action predictability leads to traffic safety ensuring. The exchange of data with traffic control centers (TCC) enables the centralization of motion regulation. A distributed network of transceiver stations forms a local positioning system based on trilateration principles. Algorithms of onboard positioning result verification and automatic resolution of communication conflicts ensure high reliability of CNS functioning. Refusal from point-to-point communication principles allows it to operate even in conditions of high car density up to several thousand per square kilometer. In cooperation with advanced technologies of traffic organization (formation of city highway grid and “total green wave” mode), CNS and TCC are capable of rising the average speed in city conditions higher than 45 km/hour. The aggregate economy of expense on last mile transportation because of the suggested innovations is to be at the level of several GDP percent due to a decrease in accidents and congestion even without accounting for social and ecological effects.
Informatics and Automation. 2023;22(1):33-56
33-56
Review on Automatic Variable-Rate Spraying Systems Based on Orchard Canopy Characterization
Abstract
Pesticide consumption and environmental pollution in orchards can be greatly decreased by combining variable-rate spray treatments with proportional control systems. Nowadays, farmers can use variable-rate canopy spraying to apply weed killers only where they are required which provides environmental friendly and cost-effective crop protection chemicals. Moreover, restricting the use of pesticides as Plant Protection Products (PPP) while maintaining appropriate canopy deposition is a serious challenge. Additionally, automatic sprayers that adjust their application rates to the size and shape of orchard plantations has indicated a significant potential for reducing the use of pesticides. For the automatic spraying, the existing research used an Artificial Intelligence and Machine Learning. Also, spraying efficiency can be increased by lowering spray losses from ground deposition and off-target drift. Therefore, this study involves a thorough examination of the existing variable-rate spraying techniques in orchards. In addition to providing examples of their predictions and briefly addressing the influences on spraying parameters, it also presents various alternatives to avoiding pesticide overuse and explores their advantages and disadvantages.
Informatics and Automation. 2023;22(1):57-86
57-86
Machine-Synthesized Control of Nonlinear Dynamic Object Based on Optimal Positioning of Equilibrium Points
Abstract
When solving an optimal control problem with both direct and indirect approaches, the main technique is to transfer the optimal control problem from the class of infinite-dimensional optimization to a finite-dimensional one. However, with all these approaches, the result is an open-loop program control that is sensitive to uncertainties, and for the implementation of which in a real object it is necessary to build a stabilization system. The introduction of the stabilization system changes the dynamics of the object, which means that the optimal control and the optimal trajectory should be calculated for the object already taking into account the stabilization system. As a result, it turns out that the initial optimal control problem is complex, and often the possibility of solving it is extremely dependent on the type of object and functionality, and if the object becomes more complex due to the introduction of a stabilization system, the complexity of the problem increases significantly and the application of classical approaches to solving the optimal control problem turns out to be time-consuming or impossible. In this paper, a synthesized optimal control method is proposed that implements the designated logic for developing optimal control systems, overcoming the computational complexity of the problem posed through the use of modern machine learning methods based on symbolic regression and evolutionary optimization algorithms. According to the approach, the object stabilization system is first built relative to some point, and then the position of this equilibrium point becomes a control parameter. Thus, it is possible to translate the infinite-dimensional optimization problem into a finite-dimensional optimization problem, namely, the optimal location of equilibrium points. The effectiveness of the approach is demonstrated by solving the problem of optimal control of a mobile robot.
Informatics and Automation. 2023;22(1):87-109
87-109
Artificial intelligence, knowledge and data engineering
Vectorization Method of Satellite Images Based on Their Decomposition by Topological Features
Abstract
Vectorization of objects from an image is necessary in many areas. The existing methods of vectorization of satellite images do not provide the necessary quality of automation. Therefore, manual labor is required in this area, but the volume of incoming information usually exceeds the processing speed. New approaches are needed to solve such problems. The method of vectorization of objects in images using image decomposition into topological features is proposed in the article. It splits the image into separate related structures and relies on them for further work. As a result, already at this stage, the image is divided into a tree-like structure. This method is unique in its way of working and is fundamentally different from traditional methods of vectorization of images. Most methods work using threshold binarization, and the main task for them is to select a threshold coefficient. The main problem is the situation when there are several objects in the image that require a different threshold. The method departs from direct work with the brightness characteristic in the direction of analyzing the topological structure of each object. The proposed method has a correct mathematical description based on algebraic topology. On the basis of the method a geoinformation technology has been developed for automatic vectorization of raster images in order to search for objects located on it. Testing was carried out on satellite images from different scales. The developed method was compared with a special tool for vectorization R2V and showed a higher average accuracy. The average percentage of automatic vectorization of the proposed method was 81%, and the semi-automatic vectorizing module R2V was 73%.
Informatics and Automation. 2023;22(1):110-145
110-145
Machine Learning Model for Determination of the Optimal Strategy in an Online Auction
Abstract
We apply a machine learning model to determine the optimal strategy in an online auction for the rent of computing resources using the best-choice model. The best-choice model allows clients to minimize the expected cost of renting a computing resource based on the spot price distribution function. The spot price dynamics platform is investigated. The most suitable price distributions in an auction are the normal distribution and its mixtures. In this case, the problems of determining the number of components in the mixture and estimating its parameters arise. One of the well-known methods for determining the number of components in a mixture of normal distributions is the BIC criterion. The EM algorithm is a basic tool for estimating the parameters of a mixture of distributions if we know the number of components. However, parameter estimation by this method takes more time when both the sample size and the number of components of the mixture increase. To automate and expedite the process of determining the number of components for a mixture of normal distributions and estimating its parameters, a classification machine learning model based on a convolutional neural network is developed. The results of the model training and validation are presented. The suggested model is compared with other algorithms which do not use neural networks. The results show that the suggested model performs well in determining the most appropriate number of components for a mixture of normal distributions and in reducing the time spent on applying the EM algorithm to estimate its parameters. This model can be used in different arias, for example, in finance or for determination of the optimal strategy in an online auction for the rent of computing resources.
Informatics and Automation. 2023;22(1):146-167
146-167
The Method of Forming a Digital Shadow of the Human Movement Process Based on the Combination of Motion Capture Systems
Abstract
The article deals with the problem of forming a digital shadow of the process of moving a person. An analysis of the subject area was carried out, which showed the need to formalize the process of creating digital shadows to simulate human movements in virtual space, testing software and hardware systems that operate on the basis of human actions, as well as in various systems of musculoskeletal rehabilitation. It was revealed that among the existing approaches to the capture of human movements, it is impossible to single out a universal and stable method under various environmental conditions. A method for forming a digital shadow has been developed based on combining and synchronizing data from three motion capture systems (virtual reality trackers, a motion capture suit, and cameras using computer vision technologies). Combining the above systems makes it possible to obtain a comprehensive assessment of the position and condition of a person regardless of environmental conditions (electromagnetic interference, illumination). To implement the proposed method, a formalization of the digital shadow of the human movement process was carried out, including a description of the mechanisms for collecting and processing data from various motion capture systems, as well as the stages of combining, filtering, and synchronizing data. The scientific novelty of the method lies in the formalization of the process of collecting data on the movement of a person, combining and synchronizing the hardware of the motion capture systems to create digital shadows of the process of moving a person. The obtained theoretical results will be used as a basis for software abstraction of a digital shadow in information systems to solve the problems of testing, simulating a person, and modeling his reaction to external stimuli by generalizing the collected data arrays about his movement.
Informatics and Automation. 2023;22(1):168-189
168-189
Identification of Characteristics of Employee’s Individual Human Capital with Data on Self-Reports of Professional Skills and Personal Characteristics
Abstract
In the field of recruitment and human resources management, the problem arises of automatization of the assessment process of the characteristics of human capital, taking into account, among other things, the personality characteristics of the employee. The article is devoted to the problem of identification of such characteristics that have the greatest contribution to some indicators of the effectiveness of an employee of an organization with self-reported data on professional skills and answers to questions–statements about various psychological aspects of personality. The general structure of the survey tools based on self-reports of employees is proposed, as well as the formalization of the proposed methods of data analysis. The cluster analysis was used for the identification of groups with similar professional skills. Special psychometric scales based on the questions–statements are selected and analyzed via the item response theory approach, giving the estimates of the latent variable, that reflects personal characteristics. At the final stage of the study, the relationship between the estimated factors (identified clusters and estimated latent variables) and the indicator of employee effectiveness was assessed. As such indicator, the fact of a managerial position was used. The proposed approach is a structure of a pilot study that allows to identify the characteristics of human capital (professional skills and personality traits) that have the greatest contribution to the performance indicators of an employee or organization, and is aimed at reducing labor costs at subsequent stages of a more detailed and targeted study. The possibilities of the proposed approach are demonstrated with data collected among state civil servants in Russia. The fact of having a managerial position is used as an indicator of effectiveness.
Informatics and Automation. 2023;22(1):190-214
190-214