Deep Reinforcement Learning for Integrated Non-Linear Control of Autonomous UAVs

Din, Adnan Fayyaz Ud; Mir, Imran; Gul, Faiza; Mir, Suleman; Saeed, Nasir; Althobaiti, Turke; Abbas, Syed Manzar; Abualigah, Laith

doi:10.3390/pr10071307

Cited by 23 publications

(9 citation statements)

References 54 publications

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“…Meanwhile, the environment model reflects the ambient behavior that aids the algorithm's efficiency by enabling it to comprehend its environment. RL algorithms may use agents to explore their environment [31], [148]. When RL is paired with deep learning's superior comprehending capabilities, RL is more efficient at generating decisions than humans in practically unlimited state space [82].…”

Section: Automation and Artificial Intelligencementioning

confidence: 99%

Comprehensive Review of Drones Collision Avoidance Schemes: Challenges and Open Issues

Rezaee,

Hamid,

Hussin

et al. 2024

IEEE Trans. Intell. Transport. Syst.

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In the contemporary landscape, the escalating deployment of drones across diverse industries has ushered in a consequential concern, including ensuring the security of drone operations. This concern extends to a spectrum of challenges, encompassing collisions with stationary and mobile obstacles and encounters with other drones. Moreover, the inherent limitations of drones, namely constraints on energy consumption, data storage capacity, and processing power, present formidable obstacles in developing collision avoidance algorithms. This review paper explores the challenges of ensuring safe drone operations, focusing on collision avoidance. We explore collision avoidance methods for UAVs from various perspectives, categorizing them into four main groups: obstacle detection and avoidance, collision avoidance algorithms, drone swarm, and path optimization. Additionally, our analysis delves into machine learning techniques, discusses metrics and simulation tools to validate collision avoidance systems, and delineates local and global algorithmic perspectives. Our evaluation reveals significant challenges in current drone collision prevention algorithms. Despite advancements, critical UAV network and communication challenges are often overlooked, prompting a reliance on simulation-based research due to cost and safety concerns. Challenges encompass precise detection of small and moving obstacles, minimizing path deviations at minimal cost, high machine learning and automation expenses, prohibitive costs of real testbeds, limited environmental comprehension, and security apprehensions. By addressing these key areas, future research can advance the field of drone collision avoidance and pave the way for safer and more efficient UAV operations.

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Section: Automation and Artificial Intelligencementioning

confidence: 99%

Comprehensive Review of Drones Collision Avoidance Schemes: Challenges and Open Issues

Rezaee,

Hamid,

Hussin

et al. 2024

IEEE Trans. Intell. Transport. Syst.

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show abstract

“…This method is very efficient when working with large problems involving a lot of data or parameters for which the algorithm is concerned. Adam has advantages over other optimizers, namely adaptive gradient, and root mean square propagation (RMSProp) [37], [38]. In determining the weight value if RMSProp uses the first value in the gradient, on the other hand, Adam uses the second value from the gradient.…”

Section: Adam Optimizermentioning

confidence: 99%

Corn Plant Disease Classification Based on Leaf using Residual Networks-9 Architecture

Prasetyo

Desrony

Panjaitan

et al. 2023

IJECE

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Classification on corn plants is used to classify leaf of corn plants that are healthy and have diseases consisting of Northern Leaf Blight, Common Rust and Gray Leaf Spot. Convolutional Neural Network (CNN) is one of algorithms from the branch of deep learning that utilizes artificial neural networks to produce accurate results in classifying an image. In this study, ResNet-9 architecture implemented to build the best model CNN for classification corn plant diseases. After that we doing comparisons of epochs have been carried out to obtain the best model, including comparisons of epochs of 5, 25, 55, 75 and 100. After the epoch comparison, the highest accuracy value was obtained in the 100 epoch experiment so that in this study 100 epochs were used in model formation. The number of datasets used is 9145 data which is divided into two, there are training data (80%) and testing data (20%). In this study, three hyperparameter tuning experiments were carried out and the results of hyperparameter tuning experiments where num_workers is 4 and batch_size is 32. This classification obtained an accuracy rate of 99% and the model is implemented into a web interface.

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“…UAV trajectory (Din et al, 2022) [80] Techniques: (DDPG) algorithm, modified for learning architecture to handle continuous state and control space domains Methodology: Designing an intelligent control architecture for an experimental Unmanned Aerial Vehicle (UAV) (DDPG) algorithm…”

Section: Uav-bs Systemsmentioning

confidence: 99%

Deep Deterministic Policy Gradient Algorithm: A Systematic Review

Sumiea,

AbdulKadir,

Al-Selwi

et al. 2023

Preprint

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Deep Reinforcement Learning (DRL) has gained significant adoption in diverse fields and applications, mainly due to its proficiency in resolving complicated decision-making problems in spaces with high-dimensional states and actions. Deep Deterministic Policy Gradient (DDPG) is a well-known DRL algorithm that adopts an actor-critic approach, synthesizing the advantages of value-based and policy-based reinforcement learning methods. The aim of this study is to provide a thorough examination of the latest developments, patterns, obstacles, and potential opportunities related to DDPG. A systematic search was conducted using relevant academic databases (Scopus, Web of Science, and ScienceDirect) to identify 85 relevant studies published in the last five years (2018-2023). We provide a comprehensive overview of the key concepts and components of DDPG, including its formulation, implementation, and training. Then, we highlight the various applications and domains of DDPG, including Autonomous Driving, Unmanned Aerial Vehicles, Resource Allocation, Communications and the Internet of Things, Robotics, and Finance. Additionally, we provide an in-depth comparison of DDPG with other DRL algorithms and traditional RL methods, highlighting its strengths and weaknesses. We believe that this review will be an essential resource for researchers, offering them valuable insights into the methods and techniques utilized in the field of DRL and DDPG.

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Deep Reinforcement Learning for Integrated Non-Linear Control of Autonomous UAVs

Cited by 23 publications

References 54 publications

Comprehensive Review of Drones Collision Avoidance Schemes: Challenges and Open Issues

Comprehensive Review of Drones Collision Avoidance Schemes: Challenges and Open Issues

Corn Plant Disease Classification Based on Leaf using Residual Networks-9 Architecture

Deep Deterministic Policy Gradient Algorithm: A Systematic Review

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