Methods of collecting information using unmanned aerial vehicles for monitoring difficult and remote areas

Abstract

This study presents a new approach to modeling the study area, which involves discretizing its space into rectangular segments. A method for
optimizing the coverage of the study area by unmanned aerial vehicle sensors by forming a minimal set of such rectangular segments is described. The geometric parameters (size, orientation, location) of the segments are taken into account to reduce the overall survey time. A typical unmanned aerial vehicle route is considered as a sequence of straight segments defined by control points. An algorithm for constructing an optimal unmanned aerial vehicle route, based on the ant colony principle, has been developed, along with controls for its movement along a given path. An unmanned aerial vehicle route consists of segments that can be classified as «active» (for scanning the area) and «idle» (for moving over areas not subject to survey). In most cases, such a route consists of alternating active and idle segments. A methodology and algorithm for unmanned aerial vehicle movement between control points corresponding to specified route segments are proposed. The process of finding the optimal solution is implemented using two interconnected algorithms: an ant colony behavior model and a control agent behavior algorithm. The applied control method involves continuous replanning of the unmanned aerial vehicle trajectory in real time, taking into account its parallel movement relative to the vector that specifies the direction.