Determining UAV Flight Trajectory for Target Recognition Using EO/IR and SAR

Abstract

The paper presents the concept of planning the optimal trajectory of fixed-wing unmanned aerial vehicle (UAV) of a short-range tactical class, whose task is to recognize a set of ground objects as a part of a reconnaissance mission. Tasks carried out by such systems are mainly associated with an aerial reconnaissance using Electro-Optical/Infrared (EO/IR) systems and Synthetic Aperture Radars (SARs) to support military operations. Execution of a professional reconnaissance of the indicated objects requires determining the UAV flight trajectory in the close neighborhood of the target, in order to collect as much interesting information as possible. The paper describes the algorithm for determining UAV flight trajectories, which is tasked with identifying the indicated objectives using the sensors specified in the order. The presence of UAV threatening objects is taken into account. The task of determining the UAV flight trajectory for recognition of the target is a component of the planning process of the tactical class UAV mission, which is also presented in the article. The problem of determining the optimal UAV trajectory has been decomposed into several subproblems: determining the reconnaissance flight method in the vicinity of the currently recognized target depending on the sensor used and the required parameters of the recognition product (photo, film, or SAR scan), determining the initial possible flight trajectory that takes into account potential UAV threats, and planning detailed flight trajectory considering the parameters of the air platform based on the maneuver planning algorithm designed for tactical class platforms. UAV route planning algorithms with time constraints imposed on the implementation of individual tasks were used to solve the task of determining UAV flight trajectories. The problem was formulated in the form of a Mixed Integer Linear Problem (MILP) model. For determining the flight path in the neighborhood of the target, the optimal control algorithm was also presented in the form of a MILP model. The determined trajectory is then corrected based on the construction algorithm for determining real UAV flight segments based on Dubin curves.

A Framework for Planning and Execution of Drone Swarm Missions in a Hostile Environment

“…The model presented in this article plans drone swarm missions in guided networks consisting of several groups of constraints. The model is based on the source model presented in Stecz [ 1 , 14 ]. The article shows the model’s extensions that allow an analyst to plan a swarm mission.…”

“…The representation of the terrain model on which drone swarm missions are planned depends on the adopted method of modeling the swarm optimization task. This article uses the tasks planned on the network of connections, which requires the preparation of the network described in [ 1 , 14 ].…”

“…The network model presented in this article has been extended in relation to the base model used in the works [ 1 , 14 ]. The article takes into account that the vertices of the network can be located at three different altitudes, which allows for efficient recognition of objects of various types.…”

“…The present article introduces a number of new constraints related to the modeling of a swarm of drones. Some of the constraints presented in the articles [ 1 , 14 ] have been modified. These constraints will be discussed in detail in this article.…”

“…In order to speed up the solver calculations in the drone swarm mission planning task, time windows should be defined only for selected vertices (Constraints ( 2 )–( 3 )). This only applies to those vertices that are assigned to the objects that need to be recognized (see [ 1 , 14 ]). The analyst should remember not to impose unnecessary time constraints.…”