Drone Automation: An Overview of Recent Progress and Research

Sailaja, Neelima; Priya, T. Hari; Yashwanth, G.; Vihari, V.; Reddy, Y. Naveen; Yashaswi, M. Raja; Ram, B. Janaki

doi:10.1109/icac3n56670.2022.10074234

Cited by 4 publications

References 7 publications

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Deep Reinforcement Learning-Based Wind Disturbance Rejection Control Strategy for UAV

Ma,

Wu,

Shoukat

et al. 2024

Drones

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Unmanned aerial vehicles (UAVs) face significant challenges in maintaining stability when subjected to external wind disturbances and internal noise. This paper addresses these issues by introducing a real-time wind speed fitting algorithm and a wind field model that accounts for varying wind conditions, such as wind shear and turbulence. To improve control in such conditions, a deep reinforcement learning (DRL) strategy is developed and tested through both simulations and real-world experiments. The results indicate a 65% reduction in trajectory tracking error with the DRL controller. Additionally, a UAV built for testing exhibited enhanced stability and reduced angular deviations in wind conditions up to level 5. These findings demonstrate the effectiveness of the proposed DRL-based control strategy in increasing UAV resilience to wind disturbances.

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Deep Reinforcement Learning-Based Wind Disturbance Rejection Control Strategy for UAV

Ma,

Wu,

Shoukat

et al. 2024

Drones

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Challenges and Innovations in YOLO-Based Drone Detection for the Industry 5.0 Revolution

Dankan Gowda,

Jagtap,

Tarambale

et al. 2024

Drone Applications for Industry 5.0

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The Industry 5.0 revolution emerges as an amalgamation of advanced technological systems with human touch onset. In this transformative time, drone technologies have become essential pieces for varied uses and their detection poses a reliable identification. Several challenges that arise when implementing YOLO-based models for drone detection are thoroughly analyzed in this chapter. It clarifies the advanced innovations that got introduced to address these challenges making effective drone detection in the framework of Industry 5.0. By studying practical cases, innovations in deep learning and YOLO models' adaptability, the chapter provides an overview of the present situation and potential future for drone detection as we move into a new stage of industrial progress.

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Advancing Drone Communication Frameworks With Blockchain in Industry 5.0 Applications

Dankan Gowda,

Kauser,

Shaik

et al. 2024

Drone Applications for Industry 5.0

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In Industry 5.0 a number of technological developments are seen to support human-machine collaboration. This evolution is central to the inclusion of drones, which are increasingly becoming irreplaceable resources in multiple industrial fields. The ability to communicate well and securely is vital for them. This chapter explores the revolutionary frameworks that utilize the power of blockchain technology to improve drone communication in the scope of Industry 5.0 applications. By investigating the operational structure of blockchain, its built-in security provisions and how it effectively fits with drone networks, a thorough comprehension of its underlying influence in drone communications is developed. Real-world examples and case studies also help to illuminate the practicalities and advantages of these frameworks, taking a look at how interconnected aerial systems in Industry 5.0 will evolve from here on out.

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Drone Automation: An Overview of Recent Progress and Research

Cited by 4 publications

References 7 publications

Deep Reinforcement Learning-Based Wind Disturbance Rejection Control Strategy for UAV

Deep Reinforcement Learning-Based Wind Disturbance Rejection Control Strategy for UAV

Challenges and Innovations in YOLO-Based Drone Detection for the Industry 5.0 Revolution

Advancing Drone Communication Frameworks With Blockchain in Industry 5.0 Applications

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