Modified mayfly algorithm for UAV path planning

The unmanned aerial vehicle (UAV) path planning problem is primarily concerned with avoiding collision with obstacles while determining the best flight path to the target position. This paper first establishes a cost function to transform the UAV route planning issue into an optimization issue that meets the UAV's feasible path requirements and path safety constraints. Then, this paper introduces a modified Mayfly Algorithm (modMA), which employs an exponent decreasing inertia weight (EDIW) strategy, adaptive Cauchy mutation, and an enhanced crossover operator to effectively search the UAV configuration space and discover the path with the lowest overall cost. Finally, the proposed modMA is evaluated on 26 benchmark functions as well as the UAV route planning problem, and the results demonstrate that it outperforms the other compared algorithms.Web of Science65art. no. 13

Communication and Control in Collaborative UAVs: Recent Advances and Future Trends

The recent progress in unmanned aerial vehicles (UAV) technology has significantly advanced UAV-based applications for military, civil, and commercial domains. Nevertheless, the challenges of establishing high-speed communication links, flexible control strategies, and developing efficient collaborative decision-making algorithms for a swarm of UAVs limit their autonomy, robustness, and reliability. Thus, a growing focus has been witnessed on collaborative communication to allow a swarm of UAVs to coordinate and communicate autonomously for the cooperative completion of tasks in a short time with improved efficiency and reliability. This work presents a comprehensive review of collaborative communication in a multi-UAV system. We thoroughly discuss the characteristics of intelligent UAVs and their communication and control requirements for autonomous collaboration and coordination. Moreover, we review various UAV collaboration tasks, summarize the applications of UAV swarm networks for dense urban environments and present the use case scenarios to highlight the current developments of UAV-based applications in various domains. Finally, we identify several exciting future research direction that needs attention for advancing the research in collaborative UAVs

3D Real-Time Energy Efficient Path Planning for a Fleet of Fixed-Wing UAVs

UAV path planning requires finding an optimal (or sub-optimal) collision free path in a cluttered environment, while taking into account geometric, physical and temporal constraints, eventually allowing UAVs to perform their tasks despite several uncertainty sources. This paper reviews the current state-of-the-art in path planning, and subsequently introduces a novel node-based algorithm based on the called EEA*. EEA* is based on the A* Search algorithm and aims at mitigating some of its key limitations. The proposed EEA* deals with 3D environments, it provides robustness quickly converging to the solution, it is energy efficient and it is realtime implementable and executable. Along with the proposed EEA*, a local path planner is developed to cope with unknown dynamic threats in the environment. Applicability and effectiveness is first demonstrated via simulated experiments using a fixed-wing UAV that operates in different mountain-like 3D environments in the presence of several unknown dynamic obstacles. Then, the algorithm is applied in a multi-agent setting with three UAVs that are commanded to follow their respective paths in a safe way. The energy efficiency of the EEA* algorithm has also been tested and compared with the conventional A* algorithm

A Modified Grey Wolf Optimizer For Improving Wind Plant Energy Production

The main problem of existing wind plant nowadays is that the optimum controller of single turbine degrades the total energy production of wind farm when it is located in a large wind plant. This is owing to its greedy control policy that can not cope with turbulence effect between turbines. This paper proposes a Modified Grey Wolf Optimizer (M-GWO) to improvise the controller parameter of an array of turbines such that the total energy production of wind plant is increased. The modification employed to the original GWO is in terms of the updated mechanism. This modification is expected to improve the variation of exploration and exploitation rates while avoiding the premature convergence condition. The effectiveness of the M-GWO is applied to maximize energy production of a row of ten turbines. The model of the wind plant is derived based on the real Horns Rev wind plant in Denmark. The statistical performance analysis shows that the M-GWO provides the highest total energy production as compared to the standard GWO, Particle Swarm Optimization (PSO) and Safe Experimentation Dynamics (SED) methods

A modified grey wolf optimizer for improving wind plant energy production

The main problem of existing wind plant nowadays is that the optimum controller of single turbine degrades the total energy production of wind farm when it is located in a large wind plant. This is owing to its greedy control policy that can not cope with turbulence effect between turbines. This paper proposes a Modified Grey Wolf Optimizer (M-GWO) to improvise the controller parameter of an array of turbines such that the total energy production of wind plant is increased. The modification employed to the original GWO is in terms of the updated mechanism. This modification is expected to improve the variation of exploration and exploitation rates while avoiding the premature convergence condition. The effectiveness of the M-GWO is applied to maximize energy production of a row of ten turbines. The model of the wind plant is derived based on the real Horns Rev wind plant in Denmark. The statistical performance analysis shows that the M-GWO provides the highest total energy production as compared to the standard GWO, Particle Swarm Optimization (PSO) and Safe Experimentation Dynamics (SED) methods

Path planning and control of a quadrotor UAV based on an improved APF using parallel search

Control and path planning are two essential and challenging issues in quadrotor unmanned aerial vehicle (UAV). In this paper, an approach for moving around the nearest obstacle is integrated into an artificial potential field (APF) to avoid the trap of local minimum of APF. The advantage of this approach is that it can help the UAV successfully escape from the local minimum without collision with any obstacles. Moreover, the UAV may encounter the problem of unreachable target when there are too many obstacles near its target. To address the problem, a parallel search algorithm is proposed, which requires UAV to simultaneously detect obstacles between current point and target point when it moves around the nearest obstacle to approach the target. Then, to achieve tracking of the planned path, the desired attitude states are calculated. Considering the external disturbance acting on the quadrotor, a nonlinear disturbance observer (NDO) is developed to guarantee observation error to exponentially converge to zero. Furthermore, a backstepping controller synthesized with the NDO is designed to eliminate tracking errors of attitude. Finally, comparative simulations are carried out to illustrate the effectiveness of the proposed path planning algorithm and controller

A novel approach for estimation of above-ground biomass of sugar beet based on wavelength selection and optimized support vector machine

Timely diagnosis of sugar beet above-ground biomass (AGB) is critical for the prediction of yield and optimal precision crop management. This study established an optimal quantitative prediction model of AGB of sugar beet by using hyperspectral data. Three experiment campaigns in 2014, 2015 and 2018 were conducted to collect ground-based hyperspectral data at three different growth stages, across different sites, for different cultivars and nitrogen (N) application rates. A competitive adaptive reweighted sampling (CARS) algorithm was applied to select the most sensitive wavelengths to AGB. This was followed by developing a novel modified differential evolution grey wolf optimization algorithm (MDE-GWO) by introducing differential evolution algorithm (DE) and dynamic non-linear convergence factor to grey wolf optimization algorithm (GWO) to optimize the parameters c and gamma of a support vector machine (SVM) model for the prediction of AGB. The prediction performance of SVM models under the three GWO, DE-GWO and MDE-GWO optimization methods for CARS selected wavelengths and whole spectral data was examined. Results showed that CARS resulted in a huge wavelength reduction of 97.4% for the rapid growth stage of leaf cluster, 97.2% for the sugar growth stage and 97.4% for the sugar accumulation stage. Models resulted after CARS wavelength selection were found to be more accurate than models developed using the entire spectral data. The best prediction accuracy was achieved after the MDE-GWO optimization of SVM model parameters for the prediction of AGB in sugar beet, independent of growing stage, years, sites and cultivars. The best coefficient of determination (R-2), root mean square error (RMSE) and residual prediction deviation (RPD) ranged, respectively, from 0.74 to 0.80, 46.17 to 65.68 g/m(2) and 1.42 to 1.97 for the rapid growth stage of leaf cluster, 0.78 to 0.80, 30.16 to 37.03 g/m(2) and 1.69 to 2.03 for the sugar growth stage, and 0.69 to 0.74, 40.17 to 104.08 g/m(2) and 1.61 to 1.95 for the sugar accumulation stage. It can be concluded that the methodology proposed can be implemented for the prediction of AGB of sugar beet using proximal hyperspectral sensors under a wide range of environmental conditions

Оптимізація маршрутів групи безпілотних авіаційних систем

Магістерська дисертація: 97 с., 17 рис., 35 табл., 1 додаток, 66 джерел. Актуальність: Безпілотні авіаційні системи (БАС), зокрема безпілотні літальні апарати (БПЛА) набувають все більш широкого застосування. Широкий функціонал БАС і збільшення їх доступності, приводить до розширення сфери використання. Сьогодні БАС використовуються для аудиту земель, боротьби з браконьєрами, моніторингу і контролю сільськогосподарських угідь, за кордоном набуває популярності доставка товарів дронами. Особливо важливим є використання БАС у військовій сфері для проведення тактичної або стратегічної розвідки, оскільки дозволяє армії не ризикувати особовим складом. Часто під час військової операції використовується група БАС. При використанні групи БАС важливо попередньо спланувати маршрут, оскільки противники навмисно можуть створювати перешкоди (приглушення радіозв'язку), що унеможливлюють керування у реальному часі. У дисертації розглянуто планування маршруту групи БАС з використанням рухомих платформ для пуску БПЛА, це є особливо актуальним сьогодні, оскільки агентство передових оборонних дослідницьких проектів США (DARPA) запустило програму Gremlins, що передбачає запуск груп БПЛА з існуючих великих літальних апаратів, таких як бомбардувальники або транспортні літаки, а також з винищувачів, нерухомих платформ буксирування. Зв'язок роботи з науковими програмами, планами, темами. Робота виконувалась на філії кафедри автоматизованих систем обробки інформації та управління Національного технічного університету України «Київський політехнічний інститут ім. Ігоря Сікорського» у рамках науково-дослідницької теми Інституту кібернетики ім. В. М. Глушкова НАН України ВФ.180.11 «Розробити математичний апарат, орієнтований на створення інтелектуальних інформаційних технологій розв'язування проблем комбінаторної оптимізації та інформаційної безпеки» (2017-2021 рр.), що виконується за Постановою бюро Відділення інформатики НАН України від 23.06.2016 р. № 2. Мета дослідження – мінімізація затрат при виконанні поставлених завдань групою БАС, що діють як команда, шляхом оптимізації їх маршрутів. Для досягнення цієї мети необхідно виконати наступні завдання: − виконати огляд відомих задач маршрутизації БПЛА, а також алгоритмів комбінаторної оптимізації, які застосовуються для їх розв’язання; − виконати формалізацію задачі планування маршруту групи БАС з використанням рухомих платформ для пуску БПЛА; − розробити програмну реалізацію алгоритмів комбінаторної оптимізації для планування маршруту; − провести дослідження запропонованих алгоритмів шляхом проведення обчислювальних експериментів. Об’єкт дослідження – функціонування групи БАС при виконанні поставлених завдань. Предмет дослідження – планування маршрутів груп БАС з можливістю використанням рухомих платформ для пуску та приземлення БАС, зокрема БПЛА. Наукова новизна одержаних результатів полягає в розробці математичної моделі та алгоритмів розв'язування проблем оптимізації маршрутів групи БАС, а також реалізації розробленого математичного апарату у вигляді спеціалізованого програмного комплексу для розв’язування досліджуваних задач маршрутизації. Публікації. Матеріали роботи опубліковані у збірнику «Комп’ютерна математика» інституту кібернетики імені В.М. Глушкова НАН України та у фаховому журналі «Науковий вiсник Ужгородського унiверситету. Серiя математика i iнформатика» Ужгородського нацiонального унiверситету та на ІІІ всеукраїнській науково-практичній конференції молодих вчених та студентів «Інформаційні системи та технології управління» (ІСТУ-2019).Master's thesis: 97 p., 17 figures, 35 tables, 1 application, 66 sources. Relevance: Unmanned aerial vehicle systems (UAS), in particular unmanned aerial vehicles (UAV), are becoming more widely used. The wide functionality of the UAS and their increased availability leads to an expansion of the scope. Today UAS is used for land audits, poaching, monitoring and control of agricultural land, the delivery of drones is becoming more popular abroad. Particularly important is the use of UAS in the military sphere for tactical or strategic reconnaissance, as it allows the army not to risk personnel. Often during the military operation, the UAS group is used. When using the UAS group, it is important to plan the route beforehand, as opponents may intentionally create interference (radio jamming) that makes it impossible to control in real time. The dissertation examines the route planning of the UAS group using mobile UAV launch platforms, which is especially relevant today as the United States Defense Advanced Research Projects Agency (DARPA) launched the Gremlins program, which involves launching UAV teams from existing large aircraft such as bombers, transport planes, etc. Connection of the thesis with scientific programs, plans, topics. The thesis was written at the branch of The Department of Computer-aided management and data processing systems of the National Technical University of Ukraine «Igor Sikorsky Kyiv Polytechnic Institute» at the V. M. Glushkov Institute of Cybernetics of the National Academy of Sciences of Ukraine under the topic VF.180.11 «To develop a mathematical apparatus focused on the creation of intelligent information technologies for solving combinatorial optimization and information security problems» (2017-2021 biennium), which is executed by the Resolution of the Bureau of Informatics of the National Academy of Sciences of Ukraine from 23.06.2016 р. № 2. The purpose of the study - to minimize the costs of fulfilling the tasks set by the UAS group acting as a team by optimizing their routes. To achieve this goal, you must complete the following tasks: − review the known UAV routing tasks and the combinatorial optimization algorithms used to solve them; − formalize the task of planning the route of the UAS group using mobile platforms for launching UAV; − develop software implementation of combinatorial optimization algorithms for route planning; − carry out research of the offered algorithms by carrying out computational experiments. The object of study is the functioning of the UAS group when completing the tasks. The subject of study is the planning of routes of UAS groups with the possibility of using mobile platforms for launching and landing UAS, in particular UAV. The scientific novelty of the results is the development of a mathematical model and algorithms for solving the problems of optimization of routes of the UAS group, as well as the implementation of the developed mathematical apparatus in the form of a specialized software complex for solving the studied routing problems. Publications. The materials are published in the compilation "Computer Mathematics" of the Institute of Cybernetics of Glushkov NAS of Ukraine, in the professional journal «Scientific Bulletin of Uzhgorod University. Mathematics and Informatics Series» of Uzhgorod National University and at the III All-Ukrainian Scientific and Practical Conference of Young Scientists and Students "Information Systems and Technologies of Management" (ISTM-2019)

Hybrid harmony search algorithm for continuous optimization problems

Harmony Search (HS) algorithm has been extensively adopted in the literature to address optimization problems in many different fields, such as industrial design, civil engineering, electrical and mechanical engineering problems. In order to ensure its search performance, HS requires extensive tuning of its four parameters control namely harmony memory size (HMS), harmony memory consideration rate (HMCR), pitch adjustment rate (PAR), and bandwidth (BW). However, tuning process is often cumbersome and is problem dependent. Furthermore, there is no one size fits all problems. Additionally, despite many useful works, HS and its variant still suffer from weak exploitation which can lead to poor convergence problem. Addressing these aforementioned issues, this thesis proposes to augment HS with adaptive tuning using Grey Wolf Optimizer (GWO). Meanwhile, to enhance its exploitation, this thesis also proposes to adopt a new variant of the opposition-based learning technique (OBL). Taken together, the proposed hybrid algorithm, called IHS-GWO, aims to address continuous optimization problems. The IHS-GWO is evaluated using two standard benchmarking sets and two real-world optimization problems. The first benchmarking set consists of 24 classical benchmark unimodal and multimodal functions whilst the second benchmark set contains 30 state-of-the-art benchmark functions from the Congress on Evolutionary Computation (CEC). The two real-world optimization problems involved the three-bar truss and spring design. Statistical analysis using Wilcoxon rank-sum and Friedman of IHS-GWO’s results with recent HS variants and other metaheuristic demonstrate superior performance

Інформаційна система з підтримки дослідження задачі складання маршрутів БПЛА з рухомою базою обслуговування

Магістерська дисертація: 102 с., 23 рис., 37 табл., 9 додатків, 49 джерел. Актуальність. Особливий інтерес до планування маршруту БПЛА виявляють військові відомства різних країн. Крім того БПЛА активно використовуються в цивільній сфері, тож обрана тема є актуальною на поточний момент. Метою роботи є розробка алгоритмів для дослідження задачі складання маршрутів БПЛА з рухомою базою обслуговування та дослідження їх ефективності. Об’єктом дослідження знаходження маршрутів БПЛА для задачі, а предмет дослідження – методи знаходження маршрутів. Завдання дослідження: провести аналіз предметної області; сформулювати математичну модель досліджуваної задачі; провести аналіз методів розв’язання задачі; розробити алгоритми розв’язання досліджуваної задачі; розробити інформаційну систему з підтримки проведення досліджень ефективності розроблених алгоритмів; провести аналіз результатів експериментів. Наукова новизна отриманих результатів полягає в розробці нових алгоритмів вирішення поставлених завдань. Практичне значення одержаних результатів. Розроблені алгоритми можна використовувати в системах складання маршрутів БПЛА, що працюють в реальних умовах.Master's thesis: 102 pages, 23 figures, 37 tables, 9 appendices, 49 sources. Topicality. The military departments of various countries show special interest in planning the UAV route. In addition, UAVs are actively used in the civilian sphere, so the chosen topic is relevant at the current moment. The purpose of the work is the development of algorithms for the study of the task of constructing UAV routes with a mobile service base and the study of their effectiveness. The object of research is finding UAV routes for the task, and the subject of research is route finding methods. Objectives of the study: conduct an analysis of the subject area; formulate a mathematical model of the researched problem; conduct an analysis of problem solving methods; develop algorithms for solving the researched problem; develop an information system to support conducting research on the effectiveness of the developed algorithms; analyze the results of experiments. The scientific novelty of the obtained results lies in the development of new algorithms for solving the tasks. Practical significance of the obtained results. The developed algorithms can be used in UAV route planning systems operating in real conditions