標(biāo)題: Titlebook: Deep Learning for Unmanned Systems; Anis Koubaa,Ahmad Taher Azar Book 2021 The Editor(s) (if applicable) and The Author(s), under exclusiv [打印本頁] 作者: 去是公開 時間: 2025-3-21 18:09
書目名稱Deep Learning for Unmanned Systems影響因子(影響力)
書目名稱Deep Learning for Unmanned Systems影響因子(影響力)學(xué)科排名
書目名稱Deep Learning for Unmanned Systems網(wǎng)絡(luò)公開度
書目名稱Deep Learning for Unmanned Systems網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Deep Learning for Unmanned Systems被引頻次
書目名稱Deep Learning for Unmanned Systems被引頻次學(xué)科排名
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書目名稱Deep Learning for Unmanned Systems年度引用學(xué)科排名
書目名稱Deep Learning for Unmanned Systems讀者反饋
書目名稱Deep Learning for Unmanned Systems讀者反饋學(xué)科排名
作者: CUB 時間: 2025-3-21 23:09
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作者: Decibel 時間: 2025-3-22 03:56
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作者: 熱烈的歡迎 時間: 2025-3-22 04:55
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.作者: Keshan-disease 時間: 2025-3-22 09:51 作者: esculent 時間: 2025-3-22 15:27 作者: esculent 時間: 2025-3-22 19:41
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作者: 使無效 時間: 2025-3-23 01:10
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作者: 喚起 時間: 2025-3-23 05:14
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作者: 結(jié)束 時間: 2025-3-23 06:55 作者: 法律 時間: 2025-3-23 11:45 作者: fodlder 時間: 2025-3-23 13:57 作者: meritorious 時間: 2025-3-23 21:57
Playing Doom with Anticipator-A3C Based Agents Using Deep Reinforcement Learning and the ViZDoom Ga by adding an anticipator network to the original model structure. The goal of doing this is to make the agent act more like human players. It will generate anticipation before making decisions, then combine the real-time game screen with anticipation images together as a whole input of the network 作者: morale 時間: 2025-3-24 00:21
Deep Reinforcement Learning for Quadrotor Path Following and Obstacle Avoidance,ocity according to the path’s shape. For the obstacle avoidance problem, a combination of a DDPG agent that avoids obstacles and another one that follows the path is presented. The obstacle avoidance approach uses the LIDAR information to detect obstacles around the vehicle. LIDAR data is processed 作者: 俗艷 時間: 2025-3-24 03:27 作者: insurgent 時間: 2025-3-24 07:30 作者: Inertia 時間: 2025-3-24 11:11
,Detection and Recognition of Vehicle’s Headlights Types for Surveillance Using Deep Neural Networksify the vehicles which are violating the traffic laws. Various problems exist in the recognition and detection of headlights, such as erroneous detection of street lights, reflection of water in rain, sign lights and the reflection plate. Some other techniques are also used for this kind of problems作者: escalate 時間: 2025-3-24 18:10
Desk Reference for Neurosciencene 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作者: 誓言 時間: 2025-3-24 21:38 作者: Boycott 時間: 2025-3-25 00:08 作者: 敲竹杠 時間: 2025-3-25 03:38 作者: Bucket 時間: 2025-3-25 10:22
: Desktop Publishing am laufenden Bandion 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作者: 倫理學(xué) 時間: 2025-3-25 15:21
Desktop Publishing mit FrameMakertively 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作者: 遺忘 時間: 2025-3-25 19:10
Rechtschreibhilfe und Thesaurus,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作者: forebear 時間: 2025-3-25 22:18 作者: 暴行 時間: 2025-3-26 02:39
Rechtschreibhilfe und Thesaurus, the image registration process, we propose to increase the accuracy of mobile robot positioning by analyzing three different optimization algorithms devoted to the registration of categorical images. The standard gradient descent algorithm is compared to the OnePlusOneEvolutionary algorithm, and si作者: 痛苦一生 時間: 2025-3-26 06:18
https://doi.org/10.1007/978-3-662-06567-9analyze the structured and unstructured environment based on solving the search-based planning and then we move to discuss interested in reinforcement learning-based model to optimal trajectory in order to apply to autonomous systems.作者: 英寸 時間: 2025-3-26 10:43
Marken, Variablen, Querverweise, by adding an anticipator network to the original model structure. The goal of doing this is to make the agent act more like human players. It will generate anticipation before making decisions, then combine the real-time game screen with anticipation images together as a whole input of the network 作者: Hangar 時間: 2025-3-26 15:32 作者: Scleroderma 時間: 2025-3-26 19:33 作者: avulsion 時間: 2025-3-26 23:10 作者: landfill 時間: 2025-3-27 03:49
Clinical Presentation of Desmoid Tumorsify the vehicles which are violating the traffic laws. Various problems exist in the recognition and detection of headlights, such as erroneous detection of street lights, reflection of water in rain, sign lights and the reflection plate. Some other techniques are also used for this kind of problems作者: leniency 時間: 2025-3-27 09:08 作者: 填滿 時間: 2025-3-27 10:48
1860-949X etc..Includes selected and extended high-quality papers rel.This book is used at the graduate or advanced undergraduate level and many others. Manned and unmanned ground, aerial and marine vehicles enable many promising and revolutionary civilian and military applications that will change our life 作者: 團結(jié) 時間: 2025-3-27 14:35
https://doi.org/10.1007/978-1-4612-2802-8od from the perspective of optimization. The other is to combined with reinforcement learning to propose a method of avoiding action selection. In this paper, simulation experiments and comparative experiments are carried out to prove the effectiveness of the method.作者: alleviate 時間: 2025-3-27 19:57
https://doi.org/10.1007/978-1-4612-2802-8-Adapt-Learn extends the deliberative cycle of Sense-Decide-Act by adding situation awareness, adaptation and learning capabilities to autonomous vehicles. Potential applications of deep learning and major challenges are highlighted in this chapter.作者: 外來 時間: 2025-3-27 21:59
https://doi.org/10.1007/978-3-642-97496-0ptimization with derivatives, where the path’s height is a criteria to minimize a route. This work validated the proposed method through computer simulations, which showed feasibility and effectiveness for assembling tasks.作者: calorie 時間: 2025-3-28 04:52
Deep Learning for Unmanned Autonomous Vehicles: A Comprehensive Review,-Adapt-Learn extends the deliberative cycle of Sense-Decide-Act by adding situation awareness, adaptation and learning capabilities to autonomous vehicles. Potential applications of deep learning and major challenges are highlighted in this chapter.作者: 金盤是高原 時間: 2025-3-28 08:21 作者: PHON 時間: 2025-3-28 13:42 作者: Ostrich 時間: 2025-3-28 15:47
Marken, Variablen, Querverweise,easibility of the proposed models, together with the energy and cost savings attained. Results demonstrate that Deep Q-Learning based algorithms represent a viable and economically convenient solution for enabling energy self-sustainability of mobile networks grouped in micro-grids.作者: 事情 時間: 2025-3-28 21:04 作者: 體貼 時間: 2025-3-28 23:38 作者: 難聽的聲音 時間: 2025-3-29 03:11 作者: 的事物 時間: 2025-3-29 07:38
Deep Learning for Unmanned Systems978-3-030-77939-9Series ISSN 1860-949X Series E-ISSN 1860-9503 作者: FOIL 時間: 2025-3-29 14:37 作者: Cerumen 時間: 2025-3-29 17:46 作者: 不近人情 時間: 2025-3-29 20:27
https://doi.org/10.1007/978-1-4612-2802-8ous systems with the ability to automatically learn underlying features in data at different levels of abstractions, to build complex concepts out of simpler ones and to get better with experience without being explicitly programmed. This book chapter provides a comprehensive review on the applicati作者: Decimate 時間: 2025-3-30 01:53
Desk Reference for Neurosciencetill depends on a remote human controller with robust wireless links to perform several of these applications. The lack of autonomy restricts the domains of application and tasks for which a UAS can be deployed. This is even more relevant in tactical and rescue scenarios where the UAS needs to opera作者: nascent 時間: 2025-3-30 08:00 作者: Abbreviate 時間: 2025-3-30 11:05
Desk Reference for Neuroscience-based control systems are able to provide guarantees on performance specifications, while learning-based control systems through the training process can further improve performances. The goal of this chapter is to propose control design frameworks with performance guarantees on primary (i.e. safet作者: Essential 時間: 2025-3-30 15:17
Desk Reference for Neuroscienceformance of the vision control system is deeply dependent on the image processing model and algorithm. Before deep learning is widely used in computer vision, the traditional image processing methods are successful in handling the low dimension information of image features, but the traditional imag作者: 空中 時間: 2025-3-30 17:14 作者: Mammal 時間: 2025-3-30 21:09
: Desktop Publishing am laufenden Bandrogram. Reinforcement learning is a machine learning paradigm that is widely used in autonomous robotics, since their principles are very similar. Reinforcement learning is based on how humans and animals perceive, reason and act on their environments by trial-and-error, learning which actions are b作者: 出生 時間: 2025-3-31 01:53 作者: aphasia 時間: 2025-3-31 06:56 作者: altruism 時間: 2025-3-31 10:50
https://doi.org/10.1007/978-3-642-97496-0 locking cable harnesses in palettes using nylon ties. This work is motivated by two biologically inspired approaches. The general .-. theory for trajectory tracking and a recurrent bi-layer Hopfield artificial neural networks (HANN) for visual feedback of multiple palette’s elements. Equidistant Ca作者: Confidential 時間: 2025-3-31 13:50
Rechtschreibhilfe und Thesaurus,le localization algorithms. Usually, the global positioning system (GPS) and motion capture cameras are employed to provide robot swarms with absolute position data with high precision. However, such infrastructures make the robots dependent on certain areas and hence reduce robustness. Thus, robots作者: 災(zāi)難 時間: 2025-3-31 17:31 作者: 確保 時間: 2025-3-31 21:48
Rechtschreibhilfe und Thesaurus,tilize its potential. Deep learning represents a framework capable of learning the most complex models necessary to carry out various robotic tasks. We propose to integrate deep learning and one of the fundamental robotic algorithms—visual servoing. Fully convolutional neural networks are used for s作者: 泛濫 時間: 2025-4-1 04:21 作者: 懶洋洋 時間: 2025-4-1 08:56
Marken, Variablen, Querverweise,dvantage of unfair information, instead of acting flexibly like human players, who make decisions only based on game screens. This chapter focuses on studying the application of Deep Learning and Reinforcement Learning in games agents and the improvement of the related algorithms. The goal is to dev作者: THROB 時間: 2025-4-1 10:16 作者: painkillers 時間: 2025-4-1 16:33 作者: PHONE 時間: 2025-4-1 22:30 作者: FEAS 時間: 2025-4-2 01:09 作者: 不能和解 時間: 2025-4-2 06:40 作者: Phagocytes 時間: 2025-4-2 09:35
