Autonomous drone control system for object tracking: Flexible system design with implementation example

Smyczyński, Paweł; Starzec, Lukasz; Granosik, Grzegorz

doi:10.1109/mmar.2017.8046919

Cited by 20 publications

(6 citation statements)

References 7 publications

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“…The accuracy of the proposed control strategy is therefore commendable and the controller can thus be recommended for autonomous flying. These findings corroborate previous studies in the same line including [7], [9], [19], [22], [24], [26], [27], [29]- [31].…”

Section: Resultssupporting

confidence: 92%

“…Their proposed technique can handle different types of uncertainties, such as external forces and modeling errors using Lyapunov energy function. [24], [26], [27] applied the Non-linear Model Predictive Control (NMPC) technique which is an optimal control method used to solve the trajectory determination problem and resulted in better-quality results.…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Design and Implementation of a Quadcopter Based on a Linear Quadratic Regulator (LQR)

Acakpovi¹,

Fifatin²,

Aza-Gnandji³

et al. 2020

JD-FEWS

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This paper presents the design and construction and control of a quadcopter drone for Aerial Data Collection (ADC). The frame of the drone was designed using CadDian Software and the parts were printed using a 3D printer. The flight controller was based on Arduino board using an Atmega328p microprocessor with GSM, GPS and GPRS for sending data over the internet and also enhancing long range flight. A feedback control system was developed and tested to control the stability of drone. The proposed control strategy of the drone was tested for a case of pursuit of trajectory and also for speed of response and the findings were very positive confirming the appropriateness of the control measures for independent and autonomous flying with promising precision. This Unmanned Aerial Vehicle (UAV) fitted with IoT has the capability of collecting and sending data over the internet and therefore can be used in many applications including risk assessment, forestry management, urban planning, coastal zone management, infrastructure monitoring, post-disaster damage assessment and delivery of medical supplies.

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Section: Resultssupporting

confidence: 92%

Section: Literature Reviewmentioning

confidence: 99%

Design and Implementation of a Quadcopter Based on a Linear Quadratic Regulator (LQR)

Acakpovi¹,

Fifatin²,

Aza-Gnandji³

et al. 2020

JD-FEWS

View full text Add to dashboard Cite

show abstract

“…As cutting-edge technology trends continue to emerge, UAVs are being integrated into wireless mobile networks, fifth-generation (5G) systems, and beyond, making UAV management an essential aspect of mobile communication development [8]. New network models, such as edge computing, cloud computing, and cellular networks, can help UAVs handle high-speed flight control applications, while hardware vendors allow the integration of different microprocessor architectures into UAVs [9]. This enables UAVs to handle real-time applications and optimize radio resources for orbital control.…”

Section: Introductionmentioning

confidence: 99%

Utilizing Deep Reinforcement Learning to Control UAV Movement for Environmental Monitoring

Nguyen,

Chien

et al. 2023

IJEETC

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Unmanned aerial vehicles (UAVs) are increasingly used in various applications, including infrastructure inspection, traffic monitoring, remote sensing, mapping, and rescue. However, many applications have required UAVs to function autonomously, without human intervention to improve system performance. In this study, we propose a new approach to environmental monitoring using a group of UAVs equipped with sensors under the support of reinforcement learning. Regarding the communication system model, we assume that UAVs can cooperate with each other to learn and share information about the environment, and then relocate to an optimal position while managing connectivity and coverage. After that, we exploit reinforcement learning with a deep deterministic policy gradient (DDPG) algorithm to optimize environmental monitoring with the proposed algorithm. Specifically, the proposed algorithm aims to simulate an environmental monitoring system using UAVs with basic parameters. We further apply the proposed algorithm to evaluate network performance under different parameter settings. Numerical results validate the effectiveness of the proposed learning-based framework in monitoring and sensing data.

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“…However, some reviews point out the limitations of the current dominating technology. Smyczynski [33] mentioned that using PID regulators for the following platform(AR Drone) was successful only with a movement speed of less than 5 km/h. However, to achieve satisfactory results with higher speed, other types of regulators should be tested [33].…”

Section: Introductionmentioning

confidence: 99%

“…Smyczynski [33] mentioned that using PID regulators for the following platform(AR Drone) was successful only with a movement speed of less than 5 km/h. However, to achieve satisfactory results with higher speed, other types of regulators should be tested [33]. We considered this issue was due to the feedback architecture, and the low-level approach it takes.…”

Section: Introductionmentioning

confidence: 99%

Event-Based Emergency Detection for Safe Drone

Kim¹,

Kim²,

Kim³

et al. 2022

Preprint

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Quadrotor drones have rapidly gained interest recently. Numerous studies are underway for the commercial use of autonomous drones, and especially the distribution businesses are taking serious reviews on drone delivery services. However, there are still many concerns about urban drone operations. The risk of failures and accidents makes it difficult to provide drone-based services in the real world with ease. There have been many studies that introduced supplementary methods to handle drone failures and emergencies. However, we discovered the limitation of the existing methods. The majority of approaches were improving PID-based control algorithms which is the dominant drone control method. This type of low-level approach lacks situation awareness and the ability to handle unexpected situations. This study introduces an event-based control methodology that takes a high-level diagnosing approach that can implement situation awareness via time-window. While leaving the low-level controller to involve in operating the drone for most of the time in normal situations, our controller operates at a higher level and detects unexpected behaviors and abnormal situations of the drone. We tested our method with real-time 3D computer simulation environments with Unreal Engine[15] and AirSim[31]. We were able to verify that our approach can provide enhanced double safety and better ensure safe drone operations. We hope our discovery to possibly contribute to the advance of real-world drone services in the near future.

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Autonomous drone control system for object tracking: Flexible system design with implementation example

Cited by 20 publications

References 7 publications

Design and Implementation of a Quadcopter Based on a Linear Quadratic Regulator (LQR)

Design and Implementation of a Quadcopter Based on a Linear Quadratic Regulator (LQR)

Utilizing Deep Reinforcement Learning to Control UAV Movement for Environmental Monitoring

Event-Based Emergency Detection for Safe Drone

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