Path Planning and Static Obstacle Avoidance for Unmanned Aerial Systems

Gajjar, Pranshav; Dodia, Virensinh; Mandaliya, Siddharth; Shah, Pooja; Ukani, Vijay; Shukla, Madhu

doi:10.1007/978-3-031-23095-0_19

Cited by 3 publications

(3 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Di X. et al focused on the driving behavior of human drivers and reviewed the learning methods for autonomous driving strategies [79]. Gajjar P. et al focused on reviewing the strategy learning for path planning and obstacle avoidance behaviors of unmanned aerial vehicle systems [80].…”

Section: Existing Reviewsmentioning

confidence: 99%

Data-Driven Policy Learning Methods from Biological Behavior: A Systematic Review

Wang,

Hayashibe,

Owaki

2024

Applied Sciences

View full text Add to dashboard Cite

Policy learning enables agents to learn how to map states to actions, thus enabling adaptive and flexible behavioral generation in complex environments. Policy learning methods are fundamental to reinforcement learning techniques. However, as problem complexity and the requirement for motion flexibility increase, traditional methods that rely on manual design have revealed their limitations. Conversely, data-driven policy learning focuses on extracting strategies from biological behavioral data and aims to replicate these behaviors in real-world environments. This approach enhances the adaptability of agents to dynamic substrates. Furthermore, this approach has been extensively applied in autonomous driving, robot control, and interpretation of biological behavior. In this review, we survey developments in data-driven policy-learning algorithms over the past decade. We categorized them into the following three types according to the purpose of the method: (1) imitation learning (IL), (2) inverse reinforcement learning (IRL), and (3) causal policy learning (CPL). We describe the classification principles, methodologies, progress, and applications of each category in detail. In addition, we discuss the distinct features and practical applications of these methods. Finally, we explore the challenges these methods face and prospective directions for future research.

show abstract

Section: Existing Reviewsmentioning

confidence: 99%

Data-Driven Policy Learning Methods from Biological Behavior: A Systematic Review

Wang,

Hayashibe,

Owaki

2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Advancements in computational intelligence have paved the way for the development of surrogate or predictive systems, which exhibit superior accuracy levels [21][22][23][24]. These developments span various domains, yielding heuristic implications and observable utility [25]. Deep learning, a subset of machine learning algorithms, utilizes neural architectures capable of identifying inherent patterns and extracting valuable information from data [26,27].…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Deep Learning Architectures and Vision Transformers for Musical Key Estimation

Garg,

Gajjar,

Shah

et al. 2023

Information

Self Cite

View full text Add to dashboard Cite

The musical key serves as a crucial element in a piece, offering vital insights into the tonal center, harmonic structure, and chord progressions while enabling tasks such as transposition and arrangement. Moreover, accurate key estimation finds practical applications in music recommendation systems and automatic music transcription, making it relevant across academic and industrial domains. This paper presents a comprehensive comparison between standard deep learning architectures and emerging vision transformers, leveraging their success in various domains. We evaluate their performance on a specific subset of the GTZAN dataset, analyzing six different deep learning models. Our results demonstrate that DenseNet, a conventional deep learning architecture, achieves remarkable accuracy of 91.64%, outperforming vision transformers. However, we delve deeper into the analysis to shed light on the temporal characteristics of each deep learning model. Notably, the vision transformer and SWIN transformer exhibit a slight decrease in overall performance (1.82% and 2.29%, respectively), yet they demonstrate superior performance in temporal metrics compared to the DenseNet architecture. The significance of our findings lies in their contribution to the field of musical key estimation, where accurate and efficient algorithms play a pivotal role. By examining the strengths and weaknesses of deep learning architectures and vision transformers, we can gain valuable insights for practical implementations, particularly in music recommendation systems and automatic music transcription. Our research provides a foundation for future advancements and encourages further exploration in this area.

show abstract

“…Additionally, researchers have investigated the integration of artificial intelligence and machine learning in image processing systems, leading to significant advancements in image classification [14], object detection [15], and image generation [16]. The constant evolution of image processing 2 techniques has paved the way for groundbreaking applications, such as medical image analysis for disease diagnosis and treatment [17], facial recognition for security and authentication purposes [18][19][20], and satellite image processing for environmental monitoring and disaster management [21]. This introduction aims to provide an overview of the diverse and rapidly evolving landscape of image processing based on the findings of various research papers in the field.…”

Section: Introductionmentioning

confidence: 99%

Image Processing Systems for Unmanned Aerial Vehicle: State-of-the-art

Mokhtar,

Sultan,

Shahar

et al. 2023

Preprint

View full text Add to dashboard Cite

The dependence on Unmanned Aerial Vehicles (UAVs) has dramatically increased in many sectors around the globe. UAVs are in high demand, and their technology is developing quickly due to their sophisticated ability to handle various issues. UAVs are capable of replacing labor-intensive tasks with conducive and safe regulation. Additional tools or sensors need to be added to the UAVs system to ensure the implementation of UAVs able to serve into industrial level. The paper aims to consolidate and present a thorough understanding of the various stages of image processing pipelines deployed in UAV applications, including image acquisition, preprocessing, feature extraction, object detection and tracking, and decision-making processes. Throughout this paper, several aspects were deliberate such as strengths, limitations, and performance metrics of existing approaches, this paper seeks to provide researchers, engineers, and practitioners with valuable insights into the challenges and opportunities of image processing systems for UAVs. Ultimately, the synthesis of this knowledge will contribute to enhancing the effective-ness, autonomy, and applicability of UAVs in diverse fields such as surveillance, agriculture, disaster management, and environmental monitoring.

show abstract

Path Planning and Static Obstacle Avoidance for Unmanned Aerial Systems

Cited by 3 publications

References 28 publications

Data-Driven Policy Learning Methods from Biological Behavior: A Systematic Review

Data-Driven Policy Learning Methods from Biological Behavior: A Systematic Review

Comparative Analysis of Deep Learning Architectures and Vision Transformers for Musical Key Estimation

Image Processing Systems for Unmanned Aerial Vehicle: State-of-the-art

Contact Info

Product

Resources

About