11 research outputs found

    Rotational speed control of multirotor UAV's propulsion unit based on fractional-order PI controller

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    In this paper the synthesis of a rotational speed closed-loop control system based on a fractional-order proportional-integral (FOPI) controller is presented. In particular, it is proposed the use of the SCoMR-FOPI procedure as the controller tuning method for an unmanned aerial vehicleโ€™s propulsion unit. In this framework, both the Hermite-Biehler and Pontryagin theorems are used to predefine a stability region for the controller. Several simulations were conducted in order to try to answer the questions โ€“ is the FOPI controller good enough to be an alternative to more complex FOPID controllers? In what circumstances can it be advantageous over the ubiquitous PID? How robust this fractional-order controller is regarding the parametric uncertainty of considered propulsion unit model?info:eu-repo/semantics/publishedVersio

    Exploring micro aerial vehicle mechanism and controller design using webots simulation

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    Quadrotor Micro Aerial Vehicle (MAV) is one of the Unmanned Aerial Vehicle (UAV) which very versatile with their capabilities for specific purposes. Understanding and controlling MAV is important for various applications, from surveillance and delivery services to disaster response and entertainment. In engineering education, providing a hardware platform for understanding MAV mechanism and the controller design is very challenging in terms of cost and safety issue. This paper presents a study on drone mechanisms and controller design using Webots, a versatile robot simulation environment. The primary objectives of this research were to simulate and analyze the behavior of drones in a line following task, employing PID controller as the control strategies, and to evaluate the suitability of Webots as a simulation tool for drone-related teaching and learning as well as research. In the methodology section, the setup of the Webots-based drone simulation, including the selection of a suitable drone model, sensor configurations, and the implementation of control algorithms will be explained. Line following task will be focused to analyze the drone mechanism and the controller design. Data on the drone performance will be collected through the experiment and will be analyzed rigorously. The findings reveal the effectiveness of Webots as a platform for simulating drone behavior. The advantages of using simulation tools like Webots, including cost-effectiveness, safety, and the ability to test and refine control algorithms before deployment on physical drones was proved in this study. It is shown that Webots can be leveraged to teach and train individuals in drone technology without the need for physical hardware

    A Smart UAV Platform for Railroad Inspection

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    Using quadcopters for analysis of an environment has been an intriguing subject of study recently. The purpose of this work is to develop a fully autonomous UAV platform for Railroad inspection The dynamics of the quadrotor is derived using Euler\u27s and Newton\u27s laws and then linearized around the hover position. A PID controller is designed to control the states of the quadrotor in a manner to effectively follow a vision-based path, using the down facing camera on a Parrot Mambo quadrotor. Using computer vision the distance from the position of the quadrotor to the position of the center of the path was found. Using the yaw controller to minimize this distance was found to be an adequate method of vision-based path following, by keeping the area of interest in the field of view of the camera. The downfacing camera is also simultaneously observing the path to detect defects using machine learning. This technique was able to detect simulated defects on the path with around 90% accuracy

    ๋ฉ€ํ‹ฐ๋กœํ„ฐ ๊ธฐ๋ฐ˜ ๋‹ค๋ชฉ์  ๋น„ํ–‰ ๋กœ๋ด‡ ํ”Œ๋žซํผ์„ ์œ„ํ•œ ๊ฐ•๊ฑด ์ œ์–ด ๋ฐ ์™„์ „๊ตฌ๋™ ๋น„ํ–‰ ๋งค์ปค๋‹ˆ์ฆ˜

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :๊ณต๊ณผ๋Œ€ํ•™ ๊ธฐ๊ณ„ํ•ญ๊ณต๊ณตํ•™๋ถ€,2020. 2. ๊น€ํ˜„์ง„.์˜ค๋Š˜๋‚  ๋ฉ€ํ‹ฐ๋กœํ„ฐ ๋ฌด์ธํ•ญ๊ณต๊ธฐ๋Š” ๋‹จ์ˆœํ•œ ๋น„ํ–‰ ๋ฐ ๊ณต์ค‘ ์˜์ƒ ์ดฌ์˜์šฉ ์žฅ๋น„์˜ ๊ฐœ๋…์„ ๋„˜์–ด ๋น„ํ–‰ ๋งค๋‹ˆํ“ฐ๋ ˆ์ด์…˜, ๊ณต์ค‘ ํ™”๋ฌผ ์šด์†ก ๋ฐ ๊ณต์ค‘ ์„ผ์‹ฑ ๋“ฑ์˜ ๋‹ค์–‘ํ•œ ์ž„๋ฌด์— ํ™œ์šฉ๋˜๊ณ  ์žˆ๋‹ค. ์ด๋Ÿฌํ•œ ์ถ”์„ธ์— ๋งž์ถ”์–ด ๋กœ๋ณดํ‹ฑ์Šค ๋ถ„์•ผ์—์„œ ๋ฉ€ํ‹ฐ๋กœํ„ฐ ๋ฌด์ธํ•ญ๊ณต๊ธฐ๋Š” ๋ถ€๊ณผ๋œ ์ž„๋ฌด์— ๋งž์ถ”์–ด ์›ํ•˜๋Š” ์žฅ๋น„ ๋ฐ ์„ผ์„œ๋ฅผ ์ž์œ ๋กœ์ด ํƒ‘์žฌํ•˜๊ณ  ๋น„ํ–‰ํ•  ์ˆ˜ ์žˆ๋Š” ๋‹ค๋ชฉ์  ๊ณต์ค‘ ๋กœ๋ด‡ ํ”Œ๋žซํผ์œผ๋กœ ์ธ์‹๋˜๊ณ  ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ํ˜„์žฌ์˜ ๋ฉ€ํ‹ฐ๋กœํ„ฐ ํ”Œ๋žซํผ์€ ๋Œํ’ ๋“ฑ์˜ ์™ธ๋ž€์— ๋‹ค์†Œ ๊ฐ•๊ฑดํ•˜์ง€ ๋ชปํ•œ ์ œ์–ด์„ฑ๋Šฅ์„ ๋ณด์ธ๋‹ค. ๋˜ํ•œ, ๋ณ‘์ง„์šด๋™์˜ ์ œ์–ด๋ฅผ ์œ„ํ•ด ๋น„ํ–‰ ์ค‘ ์ง€์†์ ์œผ๋กœ ๋™์ฒด์˜ ์ž์„ธ๋ฅผ ๋ณ€๊ฒฝํ•ด์•ผ ํ•ด ์„ผ์„œ ๋“ฑ ๋™์ฒด์— ๋ถ€์ฐฉ๋œ ํƒ‘์žฌ๋ฌผ์˜ ์ž์„ธ ๋˜ํ•œ ์ง€์†์ ์œผ๋กœ ๋ณ€ํ™”ํ•œ๋‹ค๋Š” ๋‹จ์ ์„ ๊ฐ€์ง€๊ณ  ์žˆ๋‹ค. ์œ„์˜ ๋‘ ๊ฐ€์ง€ ๋ฌธ์ œ๋“ค์„ ํ•ด๊ฒฐํ•˜๊ณ ์ž ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์™ธ๋ž€์— ๊ฐ•๊ฑดํ•œ ๋ฉ€ํ‹ฐ๋กœํ„ฐ ์ œ์–ด๊ธฐ๋ฒ•๊ณผ, ๋ณ‘์ง„์šด๋™๊ณผ ์ž์„ธ์šด๋™์„ ๋…๋ฆฝ์ ์œผ๋กœ ์ œ์–ดํ•  ์ˆ˜ ์žˆ๋Š” ์ƒˆ๋กœ์šด ํ˜•ํƒœ์˜ ์™„์ „๊ตฌ๋™ ๋ฉ€ํ‹ฐ๋กœํ„ฐ ๋น„ํ–‰ ๋งค์ปค๋‹ˆ์ฆ˜์„ ์†Œ๊ฐœํ•œ๋‹ค. ๊ฐ•๊ฑด ์ œ์–ด๊ธฐ๋ฒ•์˜ ๊ฒฝ์šฐ, ๋จผ์ € ์ •ํ™•ํ•œ ๋ณ‘์ง„์šด๋™ ์ œ์–ด๋ฅผ ์œ„ํ•œ ๋ณ‘์ง„ ํž˜ ์ƒ์„ฑ ๊ธฐ๋ฒ•์„ ์†Œ๊ฐœํ•˜๊ณ  ๋’ค์ด์–ด ๋ณ‘์ง„ ํž˜ ์™ธ๋ž€์— ๊ฐ•๊ฑดํ•œ ์ œ์–ด๋ฅผ ์œ„ํ•œ ์™ธ๋ž€๊ด€์ธก๊ธฐ ๊ธฐ๋ฐ˜ ๊ฐ•๊ฑด ์ œ์–ด ์•Œ๊ณ ๋ฆฌ์ฆ˜์˜ ์„ค๊ณ„ ๋ฐฉ์•ˆ์„ ๋…ผ์˜ํ•œ๋‹ค. ์ œ์–ด๊ธฐ์˜ ํ”ผ๋“œ๋ฐฑ ๋ฃจํ”„ ์•ˆ์ •์„ฑ์€ mu ์•ˆ์ •์„ฑ ๋ถ„์„ ๊ธฐ๋ฒ•์„ ํ†ตํ•ด ๊ฒ€์ฆ๋˜๋ฉฐ, mu ์•ˆ์ •์„ฑ ๋ถ„์„์ด ๊ฐ€์ง€๋Š” ์—„๋ฐ€ํ•œ ์•ˆ์ •์„ฑ ๋ถ„์„์˜ ๊ฒฐ๊ณผ๋ฅผ ๊ฒ€์ฆํ•˜๊ธฐ ์œ„ํ•ด ์Šค๋ชฐ๊ฒŒ์ธ ์ด๋ก  (Small Gain Theorem) ๊ธฐ๋ฐ˜์˜ ์•ˆ์ •์„ฑ ๋ถ„์„ ๊ฒฐ๊ณผ๊ฐ€ ๋™์‹œ์— ์ œ์‹œ ๋ฐ ๋น„๊ต๋œ๋‹ค. ์ตœ์ข…์ ์œผ๋กœ, ๊ฐœ๋ฐœ๋œ ์ œ์–ด๊ธฐ๋ฅผ ๋„์ž…ํ•œ ๋ฉ€ํ‹ฐ๋กœํ„ฐ์˜ 3์ฐจ์› ๋ณ‘์ง„ ๊ฐ€์†๋„ ์ œ์–ด ์„ฑ๋Šฅ ๋ฐ ํž˜ ๋ฒกํ„ฐ์˜ ํ˜•ํƒœ๋กœ ์ธ๊ฐ€๋˜๋Š” ๋ณ‘์ง„ ์šด๋™ ์™ธ๋ž€์— ๋Œ€ํ•œ ๊ทน๋ณต ์„ฑ๋Šฅ์„ ์‹คํ—˜์„ ํ†ตํ•ด ๊ฒ€์ฆํ•˜์—ฌ, ์ œ์•ˆ๋œ ์ œ์–ด๊ธฐ๋ฒ•์˜ ํšจ๊ณผ์ ์ธ ๋น„ํ–‰ ์ง€์  ๋ฐ ๊ถค์  ์ถ”์ข… ๋Šฅ๋ ฅ์„ ํ™•์ธํ•œ๋‹ค. ์™„์ „ ๊ตฌ๋™ ๋ฉ€ํ‹ฐ๋กœํ„ฐ์˜ ๊ฒฝ์šฐ, ๊ธฐ์กด์˜ ์™„์ „๊ตฌ๋™ ๋ฉ€ํ‹ฐ๋กœํ„ฐ๊ฐ€ ๊ฐ€์ง„ ๊ณผ๋„ํ•œ ์ค‘๋Ÿ‰ ์ฆ๊ฐ€ ๋ฐ ์ €์กฐํ•œ ์—๋„ˆ์ง€ ํšจ์œจ์„ ๊ทน๋ณตํ•˜๊ธฐ ์œ„ํ•œ ์ƒˆ๋กœ์šด ๋งค์ปค๋‹ˆ์ฆ˜์„ ์†Œ๊ฐœํ•œ๋‹ค. ์ƒˆ๋กœ์šด ๋งค์ปค๋‹ˆ์ฆ˜์€ ๊ธฐ์กด ๋ฉ€ํ‹ฐ๋กœํ„ฐ์™€ ์ตœ๋Œ€ํ•œ ์œ ์‚ฌํ•œ ํ˜•ํƒœ๋ฅผ ๊ฐ€์ง€๋˜ ์™„์ „๊ตฌ๋™์„ ์œ„ํ•ด ์˜ค์ง ๋‘ ๊ฐœ์˜ ์„œ๋ณด๋ชจํ„ฐ๋งŒ์„ ํฌํ•จํ•˜๋ฉฐ, ์ด๋กœ ์ธํ•ด ๊ธฐ์กด ๋ฉ€ํ‹ฐ๋กœํ„ฐ์™€ ๋น„๊ตํ•ด ์ตœ์†Œํ•œ์˜ ํ˜•ํƒœ์˜ ๋ณ€ํ˜•๋งŒ์„ ๊ฐ€์ง€๋„๋ก ์„ค๊ณ„๋œ๋‹ค. ์ƒˆ๋กœ์šด ํ”Œ๋žซํผ์˜ ๋™์  ํŠน์„ฑ์— ๋Œ€ํ•œ ๋ถ„์„๊ณผ ํ•จ๊ป˜ ์œ ๋„๋œ ์šด๋™๋ฐฉ์ •์‹์„ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•œ 6์ž์œ ๋„ ๋น„ํ–‰ ์ œ์–ด๊ธฐ๋ฒ•์ด ์†Œ๊ฐœ๋˜๋ฉฐ, ์ตœ์ข…์ ์œผ๋กœ ๋‹ค์–‘ํ•œ ์‹คํ—˜๊ณผ ๊ทธ ๊ฒฐ๊ณผ๋“ค์„ ํ†ตํ•ด ํ”Œ๋žซํผ์˜ ์™„์ „๊ตฌ๋™ ๋น„ํ–‰ ๋Šฅ๋ ฅ์„ ๊ฒ€์ฆํ•œ๋‹ค. ์ถ”๊ฐ€์ ์œผ๋กœ ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์™„์ „๊ตฌ๋™ ๋ฉ€ํ‹ฐ๋กœํ„ฐ๊ฐ€ ๊ฐ€์ง€๋Š” ์—ฌ๋ถ„์˜ ์ œ์–ด์ž…๋ ฅ(redundancy)๋ฅผ ํ™œ์šฉํ•œ ์ฟผ๋“œ์ฝฅํ„ฐ์˜ ๋‹จ์ผ๋ชจํ„ฐ ๊ณ ์žฅ ๋Œ€๋น„ ๋น„์ƒ ๋น„ํ–‰ ๊ธฐ๋ฒ•์„ ์†Œ๊ฐœํ•œ๋‹ค. ๋น„์ƒ ๋น„ํ–‰ ์ „๋žต์— ๋Œ€ํ•œ ์ž์„ธํ•œ ์†Œ๊ฐœ ๋ฐ ์‹คํ˜„ ๋ฐฉ๋ฒ•, ๋น„์ƒ ๋น„ํ–‰ ์‹œ์˜ ๋™์—ญํ•™์  ํŠน์„ฑ์— ๋Œ€ํ•œ ๋ถ„์„ ๊ฒฐ๊ณผ๊ฐ€ ์†Œ๊ฐœ๋˜๋ฉฐ, ์‹คํ—˜๊ฒฐ๊ณผ๋ฅผ ํ†ตํ•ด ์ œ์•ˆ๋œ ๊ธฐ๋ฒ•์˜ ํƒ€๋‹น์„ฑ์„ ๊ฒ€์ฆํ•œ๋‹ค.Recently, multi-rotor unmanned aerial vehicles (UAVs) are used for a variety of missions beyond its basic flight, including aerial manipulation, aerial payload transportation, and aerial sensor platform. Following this trend, the multirotor UAV is recognized as a versatile aerial robotics platform that can freely mount and fly the necessary mission equipment and sensors to perform missions. However, the current multi-rotor platform has a relatively poor ability to maintain nominal flight performance against external disturbances such as wind or gust compared to other robotics platforms. Also, the multirotor suffers from maintaining a stable payload attitude, due to the fact that the attitude of the fuselage should continuously be changed for translational motion control. Particularly, unstabilized fuselage attitude can be a drawback for multirotor's mission performance in such cases as like visual odometry-based flight, since the fuselage-attached sensor should also be tilted during the flight and therefore causes poor sensor information acquisition. To overcome the above two problems, in this dissertation, we introduce a robust multirotor control method and a novel full-actuation mechanism which widens the usability of the multirotor. The goal of the proposed control method is to bring robustness to the translational motion control against various weather conditions. And the goal of the full actuation mechanism is to allow the multi-rotor to take arbitrary payload/fuselage attitude independently of the translational motion. For robust multirotor control, we first introduce a translational force generation technique for accurate translational motion control and then discuss the design method of disturbance observer (DOB)-based robust control algorithm. The stability of the proposed feedback controller is validated by the mu-stability analysis technique, and the results are compared to the small-gain theorem (SGT)-based stability analysis to validate the rigorousness of the analysis. Through the experiments, we validate the translational acceleration control performance of the developed controller and confirm the robustness against external disturbance forces. For a fully-actuated multirotor platform, we propose a new mechanism called a T3-Multirotor that can overcome the excessive weight increase and poor energy efficiency of the existing fully-actuated multirotor. The structure of the new platform is designed to be as close as possible to the existing multi-rotor and includes only two servo motors for full actuation. The dynamic characteristics of the new platform are analyzed and a six-degree-of-freedom (DOF) flight controller is designed based on the derived equations of motion. The full actuation of the proposed platform is then validated through various experiments. As a derivative study, this paper also introduces an emergency flight technique to prepare for a single motor failure scenario of a multi-rotor using the redundancy of the T3-Multirotor platform. The detailed introduction and implementation method of the emergency flight strategy with the analysis of the dynamic characteristics during the emergency flight is introduced, and the experimental results are provided to verify the validity of the proposed technique.1 Introduction 1 1.1 Motivation 1 1.2 Literature survey 3 1.2.1 Robust translational motion control 3 1.2.2 Fully-actuated multirotor platform 4 1.3 Research objectives and contributions 5 1.3.1 Goal #I: Robust multirotor motion control 5 1.3.2 Goal #II: A new fully actuated multirotor platform 6 1.3.3 Goal #II-A: T3-Multirotor-based fail-safe flight 7 1.4 Thesis organization 7 2 Multi-Rotor Unmanned Aerial Vehicle: Overview 9 2.1 Platform overview 9 2.2 Mathematical model of multi-rotor UAV 10 3 Robust Translational Motion Control 13 3.1 Introduction 14 3.2 Translational force/acceleration control 14 3.2.1 Relationship between \mathbf{r} and \tilde{\ddot{\mathbf{X}}} 15 3.2.2 Calculation of \mathbf{r}_d from \tilde{\ddot{\mathbf{X}}}_d considering dynamics 16 3.3 Disturbance observer 22 3.3.1 An overview of the disturbance-merged overall system 22 3.3.2 Disturbance observer 22 3.4 Stability analysis 26 3.4.1 Modeling of P(s) considering uncertainties 27 3.4.2 \tau-determination through \mu-analysis 30 3.5 Simulation and experimental result 34 3.5.1 Validation of acceleration tracking performance 34 3.5.2 Validation of DOB performance 34 4 Fully-Actuated Multirotor Mechanism 39 4.1 Introduction 39 4.2 Mechanism 40 4.3 Modeling 42 4.3.1 General equations of motion of TP and FP 42 4.3.2 Simplified equations of motion of TP and FP 46 4.4 Controller design 49 4.4.1 Controller overview 49 4.4.2 Independent roll and pitch attitude control of TP and FP 50 4.4.3 Heading angle control 54 4.4.4 Overall control scheme 54 4.5 Simulation result 56 4.5.1 Scenario 1: Changing FP attitude during hovering 58 4.5.2 Scenario 2: Fixing FP attitude during translation 58 4.6 Experimental result 60 4.6.1 Scenario 1: Changing FP attitude during hovering 60 4.6.2 Scenario 2: Fixing FP attitude during translation 60 4.7 Applications 63 4.7.1 Personal aerial vehicle 63 4.7.2 High MoI payload transportation platform - revisit of [1] 63 4.7.3 Take-off and landing on an oscillating landing pad 64 5 Derived Research: Fail-safe Flight in a Single Motor Failure Scenario 67 5.1 Introduction 67 5.1.1 Related works 68 5.1.2 Contributions 68 5.2 Mechanism and dynamics 69 5.2.1 Mechanism 69 5.2.2 Platform dynamics 70 5.3 Fail-safe flight strategy 75 5.3.1 Fail-safe flight method 75 5.3.2 Hardware condition for single motor fail-safe flight 80 5.4 Controller design 83 5.4.1 Faulty motor detection 83 5.4.2 Controller design 84 5.4.3 Attitude dynamics in fail-safe mode 86 5.5 Experiment result 90 5.5.1 Experimental settings 90 5.5.2 Stability and control performance review 92 5.5.3 Flight results 93 6 Conclusions 96 Abstract (in Korean) 107Docto

    Um modelo de otimizaรงรฃo para planejamento dinรขmico de voo para grupos de drones por meio de sistema multiagente e leilรตes recursivos

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    Orientador: Eduardo TodtTese (doutorado) - Universidade Federal do Paranรก, Setor de Ciรชncias Exatas, Programa de Pรณs-Graduaรงรฃo em Informรกtica. Defesa : Curitiba, 03/07/2020Inclui referรชnciasรrea de concentraรงรฃo: Ciรชncia da ComputaรงรฃoResumo: Este trabalho apresenta um modelo aplicado de cooperacao para otimizar voos de veiculos aereos nao tripulados do tipo quadricoptero, tambem conhecidos como Drones, com aplicacao na agricultura de precisao. O modelo utiliza Sistema Multiagente para permitir a abertura, que e a propriedade de inserir e retirar elementos do modelo a qualquer momento. Para garantir a dinamicidade, que e a caracteristica que o modelo tem de se recuperar de eventos adversos ou falhas, agentes cognitivos com BDI foram utilizados. Para garantir a troca de mensagens independente da quantidade de elementos no modelo, foi utilizado o protocolo FIPA Contract-NET. Um algoritmo distribuido de otimizacao utilizando leiloes recursivos tambem foi desenvolvido, o qual visa otimizar o tempo de voo, assim como o uso da bateria dos Drones, sendo a bateria a grande limitacao destes e inibindo sua utilizacao na agricultura de precisao. Esse algoritmo foi testado em seu modelo original e, posteriormente, refinado a partir de heuristicas e metodologias visando diminuir o numero de leiloes recursivos, assim como o tempo de processamento, em comparacao ao modelo original. Este modelo, apos aplicacao das heuristicas e metodologias, foi testado. Em cenarios contendo multiplos Drones, o desempenho foi 30% superior ao algoritmo dinamico encontrado na literatura que tambem pode ser aplicado em ambientes dinamicos. Do ponto de vista de abertura e dinamicidade, o modelo foi testado no simulador MultiDrone Simulator, permitindo gerar novos planos de voo, mesmo com eventos adversos. Os resultados dos testes em simulacao realizados sustentam que o modelo proposto apresenta comportamento como esperado, mostrando-se como uma plataforma promissora de pesquisa para uso de Drones em cenarios da agricultura de precisao, uma vez que este modelo permite a utilizacao de multiplos Drones em ambientes dinamicos e abertos, garantindo a otimizacao do tempo de voo, o que garante economia da bateria dos Drones. Palavras-chave: Drones, Sistema Multiagente, BDI, Leilao RecursivoAbstract: This work presents an applied model of cooperation to optimize flights of unmanned aerial vehicles like quadcopters, also known as Drones, involved in precision agriculture. This model uses a Multiagent System to allow up the opening, which is the property of inserting and removing elements from the model at any time. To allow dynamism, which is the characteristic that the model has to recover from adverse events or failures, cognitive agents with BDI structure were used. To guarantee the exchange of messages in dynamic number of elements, the FIPA Contract-NET protocol were used. A distributed optimization algorithm using recursive auctions was also developed, which aims to optimize the number of points covered by Drones. This model aims to optimize the flight time, which directly reflects the optimization of the Drone's battery use. This is a great limitation of this kind of aerial vehicle and which inhibits its use in precision agriculture. This algorithm was tested as original proposed and, later, refined from heuristics and methodologies in order to decrease the number of auctions, as well as the processing time. This model, after applying the heuristics and methodologies, was tested, and in scenarios containing multiple Drones, the performance was 30 % higher than the dynamic algorithm found in the literature that can also be applied in dynamic environments. From the point of view of openness and dynamics, the model was tested in the MultiDrone Simulator, allowing to generate new flight plans, even with the simulated adverse events. The results of the simulation tests carried out maintain that the proposed model behaves as expected, showing itself as a promising research platform for the use of drones in precision agriculture scenarios, since this model allows the use of multiple Drones in environments dynamic and open, guaranteeing the flight optimization, which ensures battery saving for Drones. Keywords: Drones, Multiagent System, BDI, Recursive Auction
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