Anis Koubaa (editor) - Deep Learning for Unmanned Systems (Studies in Computational Intelligence, 984)

The series Studies in Computational Intelligence (SCI) publishes new developments and advances in the various areas of computational intelligencequickly and with a high quality. The intent is to cover the theory, applications, and design methods of computational intelligence, as embedded in the fields of engineering, computer science, physics and life sciences, as well as the methodologies behind them. The series contains monographs, lecture notes and edited volumes in computational intelligence spanning the areas of neural networks, connectionist systems, genetic algorithms, evolutionary computation, artificial intelligence, cellular automata, self-organizing systems, soft computing, fuzzy systems, and hybrid intelligent systems. Of particular value to both the contributors and the readership are the short publication timeframe and the world-wide distribution, which enable both wide and rapid dissemination of research output.

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Deep learning (DL) has been applied to a wide range of research areas, such as prediction, classification, image/talk recognition, and vision, and has greatly surpassed conventional methodologies. The main difference between other approaches and in-depth research is the computational simulation of neural network layers by learning and multilevel representation. Therefore, the dynamic nature of large data sets can be easily understood by deep learning. Deep learning models can therefore provide insights into the complex structures of large data sets. Deep learning methods have been shown to outperform previous state-of-the-art techniques in several tasks because of the abundance of complex data from various sources (e.g., visual, audio, medical, social, and sensor).

The main reason of editing this book is the increasing demand for deep learning (DL), unmanned systems (USs), and the exponential growth and evolution of USs in the last couple of years. This book seeks to investigate the latest deep learning applications in theoretical and practical fields of for any unmanned system, robot, drone, underwater, etc. The book discusses different applications of DL in drones and robotics where reinforcement learning methods have excellent potentials for use.

Both novice and expert readers should find this book a useful reference in the field of deep learning and reinforcement learning for unmanned systems.

This well-structured book consists of 20 full chapters.

The book is primarily meant for researchers from academia and industry, who are working on in the research areas such as engineering, control engineering, robotics, mechatronics, biomedical engineering, mechanical engineering, and computer science. The book can also be used at the graduate or advanced undergraduate level and many others.

As the editors, we hope that the chapters in this well-structured book will stimulate further research in reinforcement learning-based control and deep learning for UAS and utilize them in real-world applications.

We hope sincerely that this book, covering so many different topics, will be very useful for all readers.

We would like to thank all the reviewers for their diligence in reviewing the chapters.

Special thanks go to Springer, especially the book editorial team.