Titlebook: Deep Learning for Unmanned Systems; Anis Koubaa,Ahmad Taher Azar Book 2021 The Editor(s) (if applicable) and The Author(s), under exclusiv

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Book 2021ecent years, deep learning research has received an increasing amount of attention from researchers in academia, government laboratories and industry. These research activities have borne some fruit in tackling some of the challenging problems of manned and unmanned ground, aerial and marine vehicle

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Deep Learning and Reinforcement Learning for Autonomous Unmanned Aerial Systems: Roadmap for Theoryne learning, specifically deep learning and reinforcement learning can be leveraged to develop next-generation autonomous UAS. We first begin motivating this chapter by discussing the application, challenges, and opportunities of the current UAS in the introductory section. We then provide an overvi

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Guaranteed Performances for Learning-Based Control Systems Using Robust Control Theory,nts can be taken into consideration. The effectiveness of the design frameworks are illustrated through simulation examples, e.g. cruise control design for autonomous vehicles and the positioning of an arm of a mobile robot.

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Uncertainty-Aware Autonomous Mobile Robot Navigation with Deep Reinforcement Learning,ion by automatically discovering relevant features and representations in raw and high-dimensional data. This combination results in a new paradigm known as deep reinforcement learning, that has been successfully employed in robotic tasks such as navigation and manipulation. Developments in robotics

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Reinforcement Learning for Autonomous Morphing Control and Cooperative Operations of UAV Cluster,tively through complementary capabilities and mutual coordination, the capability of UAV can be expanded and the overall combat effectiveness can also be improved. Therefore, it is an urgent problem to study an efficient autonomous cooperative control intelligent algorithm. In order to truly achieve

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Deep Learning Based Formation Control of Drones,ances between the pairs of drones in a cyclic formation where each drone follows its coleader. We equip each drone with a monocular camera sensor and derive the bearing angle between a drone and its coleader with the recently developed deep learning algorithms. The onboard measurements are then rela

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