Model for Intercepting Targets by the Unmanned Aerial Vehicle

Kritsky, D. N.; Ovsiannik, V. M.; Погудина, Ольга Константиновна; Shevel, Vladimir; Дружинин, Е. И.

doi:10.1007/978-3-030-25741-5_20

Cited by 10 publications

(4 citation statements)

References 1 publication

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“…Articles by researchers from Stanford University consider the use of Lyapunov vector fields for autonomous control. They use these fields to create optimal paths for a car to reach a given position [5][6][7].…”

Section: Analysis Of the Latest Research And Publicationsmentioning

confidence: 99%

Development of Control Laws of Unmanned Aerial Vehicles for Performing Group Flight at the Straight-Line Horizontal Flight Stage

Barabash,

Kyrianov

2023

A.I.S.

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The article proposes an improved approach to controlling groups of unmanned aerial vehicles (UAVs) aimed at increasing the overall efficiency and flexibility of the control process. The use of a heterogeneous external field, which varies both in magnitude and direction, allows achieving greater adaptability and accuracy in controlling a group of UAVs. A vector field for unmanned aerial vehicles determines the direction and intensity of the vehicles' movement in space. Such vector fields can be used to develop UAV control laws, including determining optimal flight paths, controlling speed, avoiding obstacles, and ensuring coordination of a group of UAVs. The subject of the study is the methods of controlling groups of autonomous UAVs, where each vehicle may have different speeds and flight directions. To solve this problem, various methods of using a heterogeneous field have been developed and proposed. Instead of using a homogeneous field that provides a constant flight speed, a vector field is used that adapts to different conditions and characteristics of the vehicles in the group. This method allows for effective group management, ensuring the necessary coordination and interaction between the vehicles. An analysis of recent research and publications in the field of autonomous system control indicates the feasibility of using machine learning, vector fields, and a large amount of data to successfully coordinate the movement of autonomous systems. These approaches make it possible to create efficient and reliable control systems. The aim of the study is to develop laws for controlling the movement of a group of autonomous unmanned aerial vehicles at the stage of straight-line horizontal flight based on natural analogues to improve the efficiency and reliability of their coordinated movement in different conditions. The main conclusions of the research are that the proposed method of controlling groups of UAVs based on a heterogeneous field can be implemented. It takes into account a variety of vehicle characteristics and environmental conditions that are typical for real-world use scenarios. This work opens up prospects for further improving the management of UAV groups and their use in various fields of activity. The article emphasises the relevance of technology development for autonomous unmanned systems, especially in the context of autonomous transport systems.

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Section: Analysis Of the Latest Research And Publicationsmentioning

confidence: 99%

Development of Control Laws of Unmanned Aerial Vehicles for Performing Group Flight at the Straight-Line Horizontal Flight Stage

Barabash,

Kyrianov

2023

A.I.S.

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“…In (Triharminto et al, 2013), target interception in 3D is developed using dynamic path planning based on the Dubins algorithm. Trajectory optimization technique is used for target interception in (Kritsky et al, 2019;García et al, 2019). Learning-Based Model Predictive Control (LBMPC) is used to catch a ball with quadrotor in an indoor environment (Bouffard et al, 2012).…”

Section: Relevant Literaturementioning

confidence: 99%

Autonomous Cooperative Multi-Vehicle System for Interception of Aerial and Stationary Targets

Tony¹,

Jana²,

Varun³

et al. 2022

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This paper presents the design, development, and testing of hardware-software systems by the IISc-TCS team for Challenge 1 of the Mohamed Bin Zayed International Robotics Challenge 2020. The goal of Challenge 1 was to grab a ball suspended from a maneuvering UAV and pop balloons anchored to the ground, using suitable manipulators. The important tasks carried out to address this challenge include the design and development of a hardware system with efficient grabbing and popping mechanisms, considering the restrictions in volume and payload, design of accurate target interception algorithms using visual information suitable for outdoor environments, and development of a software architecture for dynamic, multi-agent, aerial systems performing complex tasks. In this paper, we discuss the design of a custom end-effector mounted on a single degree of freedom manipulator, and robust algorithms for the interception of targets in an uncertain environment. Vision-based guidance and tracking strategies are developed based on the concept of pursuit engagement and artificial potential function. The software architecture presented in this work develops an Operation Management System (OMS) architecture that allocates static and dynamic tasks collaboratively among multiple UAVs to perform any given task. An important aspect of this work is that all the systems developed were designed to operate in completely autonomous mode. A detailed description of the architecture along with simulations of complete challenge in the Gazebo environment and field experiment results are also included in this work. The developed hardware-software system is useful for counter-UAV systems and can be used for other applications.

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“…[4,5]. Their use can change the nature of military operations (for example, the use of updated approaches to the interception of the enemy -the use of UAVs in the air, and when the enemy is detected, approach and neutralization [6], new approaches to the detection of enemy UAVs and their classification from the use using the Physical-Layer Protocol Statistical Fingerprint [7], flight planning with ticking of actions in case of enemy detection [8]). But the large-scale use of drones requires the analysis and formation of logistical military operations, which must be planned and executed according to the strategic and operational plans of the military leadership.…”

Section: Introductionmentioning

confidence: 99%

Military logistics planning models for enemy targets attack by a swarm of combat drones

Fedorovych,

Kritskiy,

Malieiev

et al. 2024

reks

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This article describes and investigates the planning aspect of military actions aimed at destroying enemy targets with the help of an attack drone swarm. This study attempts to solve the task of operational-tactical planning of a massive attack on enemy targets with the help of combat drones, which have different combat potential characteristics. It analyzes the problems of unmanned aerial vehicles (UAVs) swarms’ usage, which ensures maximum efficiency during combat operations. The article shows that in order to plan effective military operations, it is necessary to form the following logistical sequence: identification of relevant targets set, formation of drones into a swarm to attack targets, distribution of drones by targets, and planning flight routes of a drone swarm in conditions of military threats. It concludes that for the effective use of a combat drone swarm, it is necessary to plan logistical actions in advance to inflict maximum damage on the enemy and successfully fulfill the operational and tactical goals of the military leadership. The purpose of this study is to create information technology models that will allow planning logistical military actions for the effective use of combat drone swarms to defeat enemy targets. This article describes a systematic representation of logistical military operations for combat drone swarms. It also analyzes enemy targets, which are represented in the form of a priority list with the characteristics of relevance, the necessary combat potential to hit the targets, the risks of approaching the targets, and the flight time of the drones. From the list of targets, a sublist is formed, considering the combat potential of the drone swarm and the necessary potential to defeat the selected enemy targets. The optimization model helps to distribute the swarm of drones into groups to achieve the enemy targets and destroy them. The movement of drones is planned considering flight zones, possible anti-drone actions of the enemy, and the risks of military threats. Any Logic agent simulation platform can be used to create a simulated flight model of a drone swarm to selected enemy targets. Modeling makes it possible to form rational flight routes of a drone swarm under conditions of military threats from the enemy. An example is given to illustrate the formation of logistical actions for planning a massive attack on enemy targets with the help of a drone swarm. The scientific novelty of this study is related to the solution of the urgent problem of planning logistical military operations for the effective use of a combat drone swarm to destroy enemy targets. The results of this study should be used for the operational and tactical planning of logistical military operations to defeat enemy targets with the help of a combat drone swarm.

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Model for Intercepting Targets by the Unmanned Aerial Vehicle

Cited by 10 publications

References 1 publication

Development of Control Laws of Unmanned Aerial Vehicles for Performing Group Flight at the Straight-Line Horizontal Flight Stage

Development of Control Laws of Unmanned Aerial Vehicles for Performing Group Flight at the Straight-Line Horizontal Flight Stage

Autonomous Cooperative Multi-Vehicle System for Interception of Aerial and Stationary Targets

Military logistics planning models for enemy targets attack by a swarm of combat drones

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