Abstract
The article deals with a possibility of using a genetic algorithm in solving the tasks of optimal selection of routing for unmanned aerial vehicle (UAV) in autonomous flight. Complexity of resolving this task is the need to evaluate the situation at observed objects, to take a decision to change and recalculate the flight path in real time on board of UAV by means of onboard computers. The article reflects the relationship between the application task of monitoring of extended graph on terrain and the mathematical model of optimization by means of genetic algorithm.
Highlights
The article deals with a possibility of using a genetic algorithm in solving the tasks of optimal selection of routing for unmanned aerial vehicle (UAV) in autonomous flight
Complexity of resolving this task is the need to evaluate the situation at observed objects, to take a decision to change and recalculate the flight path in real time on board of UAV by means of onboard computers
The article reflects the relationship between the application task of monitoring of extended graph on terrain and the mathematical model of optimization by means of genetic algorithm
Summary
Процесс сходимости целевой функции к минимуму min f(m) = 2 при изменении числа поколений. Процесс поиска минимума сходится при всех значениях кроссовера из промежутка фактора кроссовера [0,14; 0,3] с шагом 0.02. Процесс сходится при малом числе поколений – 300, при котором достигается минимум целевой функции. Среди оптимальных маршрутов третьего столбца встречаются одинаковые. 2. Число реперных точек на графе перекрестка при n = 5. Приведем график целевой функции при оптимизации на графе «перекресток» по 5 точкам Подсчет числа оптимальных маршрутов дает следующее множество вариантов при начале полета с концевых точек графа «перекресток»: 4 · 3 · 2 = = 24. Всего вариантов выбора оптимального маршрута будет также 24 = 4 · 3 · 2.
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