2,024 research outputs found

    Optimizing UAV Navigation: A Particle Swarm Optimization Approach for Path Planning in 3D Environments

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    This study explores the application of Particle Swarm Optimization (PSO) in Unmanned Aerial Vehicle (UAV) path planning within a simulated three-dimensional environment. UAVs, increasingly prevalent across various sectors, demand efficient navigation solutions that account for dynamic and unpredictable elements. Traditional pathfinding algorithms often fall short in complex scenarios, hence the shift towards PSO, a bio-inspired algorithm recognized for its adaptability and robustness. We developed a Python-based framework to simulate the UAV path planning scenario. The PSO algorithm was tasked to navigate a UAV from a starting point to a predetermined destination while avoiding spherical obstacles. The environment was set within a 3D grid with a series of waypoints, marking the UAV's trajectory, generated by the PSO to ensure obstacle avoidance and path optimization. The PSO parameters were meticulously tuned to balance the exploration and exploitation of the search space, with an emphasis on computational efficiency. A cost function penalizing proximity to obstacles guided the PSO in real-time decision-making, resulting in a collision-free and optimized path. The UAV's trajectory was visualized in both 2D and 3D perspectives, with the analysis focusing on the path's smoothness, length, and adherence to spatial constraints. The results affirm the PSO's effectiveness in UAV path planning, successfully avoiding obstacles and minimizing path length. The findings highlight PSO's potential for practical UAV applications, emphasizing the importance of parameter optimization. This research contributes to the advancement of autonomous UAV navigation, indicating PSO as a viable solution for real-world path planning challenges

    Enhancing UAV Navigation in Dynamic Environments: A Detailed Integration of Fick's Law Algorithm for Optimal Pathfinding in Complex Terrains

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    In the realm of Unmanned Aerial Vehicles (UAVs), efficient navigation in complex environments is crucial, necessitating advanced pathfinding algorithms. This study introduces the Fick's Law Algorithm (FLA) for UAV path optimization, drawing inspiration from the principles of molecular diffusion, and positions it in the context of existing algorithms such as A* and Dijkstra's. Through a comparative analysis, we highlight FLA's unique approach and advantages in terms of computational efficiency and adaptability to dynamic obstacles. Our experiment, conducted in a simulated three-dimensional space with static and dynamic obstacles, involves an extensive quantitative analysis. FLA's performance is quantified through metrics like path length reduction, computation time, and obstacle avoidance efficacy, demonstrating a marked improvement over traditional methods. The technical foundation of FLA is detailed, emphasizing its iterative adaptation based on a cost function that accounts for path length and obstacle avoidance. The algorithm's rapid convergence towards an optimal solution is evidenced by a significant decrease in the cost function, supported by data from our convergence graph. Visualizations in both 2D and 3D effectively illustrate the UAV’s trajectory, highlighting FLA's efficiency in real-time path correction and obstacle negotiation. Furthermore, we discuss FLA's practical implications, outlining its adaptability in various real-world UAV applications, while also acknowledging its limitations and potential challenges. This exploration extends FLA's relevance beyond theoretical contexts, suggesting its efficacy in real-world scenarios. Looking ahead, future work will not only focus on enhancing FLA's computational efficiency but also on developing specific methodologies for real-world testing. These include adaptive scaling for different UAV models and environments, as well as integration with UAV hardware systems. Our study establishes FLA as a potent tool for autonomous UAV navigation, offering significant contributions to the field of dynamic path optimization

    Simultaneous Localization and Mapping and Tag-Based Navigation for Unmanned Aerial Vehicles

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    This paper presents navigation techniques for an Unmanned Aerial Vehicle (UAV) in a virtual simulation of an indoor environment using Simultaneous Localization and Mapping (SLAM) and April Tag markers to reach a target destination. In many cases, UAVs can access locations that are inaccessible to people or regular vehicles in indoor environments, making them valuable for surveillance purposes. This study employs the Robot Operating System (ROS) to simulate SLAM techniques using LIDAR and GMapping packages for UAV navigation in two different environments. In the Tag-based simulation, the input topic for April Tag in ROS is camera images, and the calibration of position with a tag is done through assigning a message to each ID and its marker image. On the other hand, navigation in SLAM was achieved using a global and local planner algorithm. For localization, an Adaptive Monte-Carlo Localization (AMCL) technique has been used to identify factors contributing to inconsistent mapping results, such as heavy computational load, grid mapping accuracy, and inadequate UAV localization. Furthermore, this study analyzed the April Tag-based navigation algorithm, which showed satisfactory outcomes due to its lighter computing requirements. It can be ascertained that by using ROS packages, the simulation of SLAM and Tag-based UAV navigation inside a building can be achieved. &nbsp

    Спосіб організації засобів управління літальним апаратом

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    Актуальність теми. Безпілотні літальні апарати, зокрема, їх різновид – квадрокоптери останнім часом все активніше використовуються у різних сферах людського життя. Застосування цим пристроям знаходять люди багатьох професій – від фотографів до військових. Основною сферою застосування апаратів такого типу є фото та відеозйомка, моніторинг різних параметрів навколишнього середовища з використанням додаткового обладнання. Безпілотні літальні апарати завдяки своєму розміру можуть виконувати польоти в умовах обмеженого простору, у місцях, де є загроза життю людини, а також там, де недоцільно чи надто дорого використовувати повноцінні літальні апарати, наприклад, гелікоптери. Особливо актуальними вони стали при використанні в умовах воєнних дій. Об’єктом дослідження є аналіз ройових алгоритмів для застосування їх у пошуку оптимального маршруту у безпілотних літальних апаратах. Предметом дослідження є програмне забезпечення для керування квадрокоптером та пошуку оптимального маршруту за допомогою ройових алгоритмів. Мета роботи: покращення способів керування літальним апаратом ( квадрокоптером) завдяки використанню метаевристичних методів, зокрема мурашиного методу. Наукова новизна полягає у модифікації мурашиного алгоритму для його використання у безпілотному літальному апарату та пошуку оптимального маршруту. Практична цінність полягає у застосуванні апаратного та програмного забезпечення для роботи алгоритму мурашиної колонії для пошуку оптимального маршруту польоту безпілотного літального апарата. Апробація роботи. Основні положення і результати роботи були представлені та обговорювались на XIV науковій конференції магістрантів та аспірантів «Прикладна математика та комп’ютинг» ПМК-2021 (Київ, 17-19 листопада 2021 р.), Міжнародній мультидисциплінарній науковій інтернет конференції «Світ наукових досліджень. Випуск 18» (м.Тернопіль (Україна) - м.Переворськ (Польща), 20-21 квітня 2023 р.), на LXVII міжнародній науково практичній конференція «Інформаційне суспільство: технологічні, економічні та технічні аспекти становлення» (Тернопіль, 12 травня 2022 р.).. Структура та обсяг роботи. Магістерська дисертація складається з вступу, чотирьох розділів та висновків. У вступі подано загальну характеристику роботи, зроблено оцінку сучасного стану проблеми, обґрунтовано актуальність напрямку досліджень, сформульовано мету і задачі досліджень, показано наукову новизну отриманих результатів і практичну цінність роботи, наведено відомості про апробацію результатів і їхнє впровадження. У першому розділі розглянуто загальні відомомсті про штучний інтелект, безпілотні літальні апарати та пошук оптимального маршруту. У другому розділі наведено існуючі технології для розробки як апаратної так програмної частини безпілотного літального апарата. У третьому та четвертому розділах формулюється основна методика створення літального апарата та мурашиного алгоритму, описується його розробка, модифікація та використання для керування квадрокоптером. У висновках представлені результати проведеної роботи. Робота представлена на 116 аркушах, містить посилання на список використаних літературних джерел.Actuality of theme. Unmanned aerial vehicles, in particular, their variety - Recently, quadcopters have been increasingly used in various spheres of human life. People find use for these devices many professions - from photographers to military personnel. The main field the application of devices of this type is photo and video recording, monitoring of various environmental parameters using an additional equipment. Due to their size, unmanned aerial vehicles can perform flights in conditions of limited space, in places where there is a threat to human life, and also where it is impractical or too expensive to use full-fledged ones aircraft, for example, helicopters. They became especially relevant when used in military conditions actions The object of the study is the analysis of swarm algorithms for their application in search of the optimal route in unmanned aerial vehicles. The subject of research is management software by quadcopter and finding the optimal route with the help of swarms algorithms. The purpose of the work: improving the methods of controlling the aircraft ( quadcopter) thanks to the use of metaheuristic methods, in particular ant method. The scientific novelty consists in modifying the ant algorithm for it use in an unmanned aerial vehicle and finding the optimal one route The practical value lies in the application of hardware and software support for the operation of the ant colony algorithm for searching optimal flight route of an unmanned aerial vehicle. Approbation of work. The main provisions and results of the work were were presented and discussed at the XIV scientific conference of master's students and graduate students "Applied mathematics and computing" PMK-2021 (Kyiv, 17-19 November 2021), to the international multidisciplinary scientific internet conference "The world of scientific research. Issue 18" (Ternopil (Ukraine) - Perevorsk (Poland), April 20-21, 2023), at the LXVII international scientific and practical conference "Information society: technological, economic and technical aspects of formation" (Ternopil, May 12, 2022). Structure and scope of work. The master's thesis consists of introduction, four chapters and conclusions. In the introduction, a general description of the work is presented, an assessment is made the current state of the problem, the relevance of the research direction is substantiated, the purpose and tasks of research are formulated, scientific novelty is shown of the obtained results and the practical value of the work, information about approbation of the results and their implementation. The first chapter deals with general information about artificial intelligence, unmanned aerial vehicles and finding the optimal route. The second section presents existing technologies for development as hardware so the software part of the unmanned aerial vehicle. In the third and fourth sections, the main methodology is formulated the creation of a flying machine and an ant algorithm, it is described development, modification and use for controlling a quadcopter. The results of the work are presented in the conclusions. The work is presented on 116 sheets, contains a link to the list used literary sources

    Optimally Distributed Receiver Placements Versus an Environmentally Aware Source: New England Shelf Break Acoustics Signals and Noise Experiment

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    This article describes the results of the Spring of 2021 New England Shelf Break Acoustics (NESBA) Signals and Noise experiment as they pertain to the optimization of a field of passive receivers versus an environmentally aware source with end-state goals. A discrete optimization has been designed and used to demonstrate providing an acoustic system operator with actionable guidance relating to optimally distributed receiver locations and depths and likely mean source detection times and associated uncertainties as a function of source and receiver levels of environmental awareness. The uncertainties considered here are those due to the imperfect spatial and temporal sensing of the water column, ambient noise (AN), and the seabed, and the impact this has on ocean forecasting and acoustic performance prediction accuracy. As a part of the NESBA experiment, high-resolution (1 km spatial) regional Navy Coastal Ocean Model ensemble forecasts were generated to capture oceanographic variability and uncertainty. Passive AN-based seabed measurements were conducted to estimate seabed properties including variability and uncertainty. Extensive AN and conductivity, temperature, and depth measurements were also conducted. In this article, operationally relevant metrics are employed to estimate the potential value-added of optimal receiver location and depth placements as a function of source end-state goals and assumed level of environmental awareness. A concept for generating stochastic acoustic prediction metrics and associated optimally distributed receiver locations and depths in an operational environment is proposed

    An Optimised Shortest Path Algorithm for Network Rotuting & SDN: Improvement on Bellman-Ford Algorithm

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    Network routing algorithms form the backbone of data transmission in modern network architectures, with implications for efficiency, speed, and reliability. This research aims to critically investigate and compare three prominent routing algorithms: Bellman-Ford, Shortest Path Faster Algorithm (SPFA), and our novel improved variant of Bellman-Ford, the Space-efficient Cost-Balancing Bellman-Ford (SCBF). We evaluate the performance of these algorithms in terms of time and space complexity, memory utilization, and routing efficacy, within a simulated network environment. Our results indicate that while Bellman-Ford provides consistent performance, both SPFA and SCBF present improvements in specific scenarios with the SCBF showing notable enhancements in space efficiency. The innovative SCBF algorithm provides competitive performance and greater space efficiency, potentially making it a valuable contribution to the development of network routing protocols. Further research is encouraged to optimize and evaluate these algorithms in real-world network conditions. This study underscores the continuous need for algorithmic innovation in response to evolving network demands

    A Novel Method for Solving Multi-objective Shortest Path Problem in Respect of Probability Theory

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    Transportation process or activity can be considered as a multi-objective problem reasonably. However, it is difficult to obtain an absolute shortest path with optimizing the multiple objectives at the same time by means of Pareto approach. In this paper, a novel method for solving multi-objective shortest path problem in respect of probability theory is developed, which aims to get the rational solution of multi-objective shortest path problem. Analogically, each objective of the shortest path problem is taken as an individual event, thus the concurrent optimization of many objectives equals to the joint event of simultaneous occurrence of the multiple events, and therefore the simultaneous optimization of multiple objectives can be solved on basis of probability theory rationally. The partial favorable probability of each objective of every scheme (routine) is evaluated according to the actual preference degree of the utility indicator of the objective. Moreover, the product of all partial favorable probabilities of the utility of objective of each scheme (routine) casts the total favorable probability of the corresponding scheme (routine), which results in the decisively unique indicator of the scheme (routine) in the multi-objective shortest path problem in the point of view of system theory. Thus, the optimum solution of the multi-objective shortest path problem is the scheme (routine) with highest total favorable probability. Finally, an application example is given to illuminate the approach

    Advancing Carbon Sequestration through Smart Proxy Modeling: Leveraging Domain Expertise and Machine Learning for Efficient Reservoir Simulation

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    Geological carbon sequestration (GCS) offers a promising solution to effectively manage extra carbon, mitigating the impact of climate change. This doctoral research introduces a cutting-edge Smart Proxy Modeling-based framework, integrating artificial neural networks (ANNs) and domain expertise, to re-engineer and empower numerical reservoir simulation for efficient modeling of CO2 sequestration and demonstrate predictive conformance and replicative capabilities of smart proxy modeling. Creating well-performing proxy models requires extensive human intervention and trial-and-error processes. Additionally, a large training database is essential to ANN model for complex tasks such as deep saline aquifer CO2 sequestration since it is used as the neural network\u27s input and output data. One major limitation in CCS programs is the lack of real field data due to a lack of field applications and issues with confidentiality. Considering these drawbacks, and due to high-dimensional nonlinearity, heterogeneity, and coupling of multiple physical processes associated with numerical reservoir simulation, novel research to handle these complexities as it allows for the creation of possible CO2 sequestration scenarios that may be used as a training set. This study addresses several types of static and dynamic realistic and practical field-base data augmentation techniques ranging from spatial complexity, spatio-temporal complexity, and heterogeneity of reservoir characteristics. By incorporating domain-expertise-based feature generation, this framework honors precise representation of reservoir overcoming computational challenges associated with numerical reservoir tools. The developed ANN accurately replicated fluid flow behavior, resulting in significant computational savings compared to traditional numerical simulation models. The results showed that all the ML models achieved very good accuracies and high efficiency. The findings revealed that the quality of the path between the focal cell and injection wells emerged as the most crucial factor in both CO2 saturation and pressure estimation models. These insights significantly contribute to our understanding of CO2 plume monitoring, paving the way for breakthroughs in investigating reservoir behavior at a minimal computational cost. The study\u27s commitment to replicating numerical reservoir simulation results underscores the model\u27s potential to contribute valuable insights into the behavior and performance of CO2 sequestration systems, as a complimentary tool to numerical reservoir simulation when there is no measured data available from the field. The transformative nature of this research has vast implications for advancing carbon storage modeling technologies. By addressing the computational limitations of traditional numerical reservoir models and harnessing the synergy between machine learning and domain expertise, this work provides a practical workflow for efficient decision-making in sequestration projects

    Optimized Path Planning for USVs under Ocean Currents

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    The proposed work focuses on the path planning for Unmanned Surface Vehicles (USVs) in the ocean enviroment, taking into account various spatiotemporal factors such as ocean currents and other energy consumption factors. The paper proposes the use of Gaussian Process Motion Planning (GPMP2), a Bayesian optimization method that has shown promising results in continuous and nonlinear path planning algorithms. The proposed work improves GPMP2 by incorporating a new spatiotemporal factor for tracking and predicting ocean currents using a spatiotemporal Bayesian inference. The algorithm is applied to the USV path planning and is shown to optimize for smoothness, obstacle avoidance, and ocean currents in a challenging environment. The work is relevant for practical applications in ocean scenarios where an optimal path planning for USVs is essential for minimizing costs and optimizing performance.Comment: 9 pages and 7 figures, submitted for IEEE Transactions on Man, systems ,and Cybernetic
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