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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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31#
發(fā)表于 2025-3-26 23:10:23 | 只看該作者
32#
發(fā)表于 2025-3-27 03:49:30 | 只看該作者
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
33#
發(fā)表于 2025-3-27 09:08:03 | 只看該作者
34#
發(fā)表于 2025-3-27 10:48:29 | 只看該作者
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
35#
發(fā)表于 2025-3-27 14:35:00 | 只看該作者
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.
36#
發(fā)表于 2025-3-27 19:57:02 | 只看該作者
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.
37#
發(fā)表于 2025-3-27 21:59:59 | 只看該作者
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.
38#
發(fā)表于 2025-3-28 04:52:51 | 只看該作者
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.
39#
發(fā)表于 2025-3-28 08:21:46 | 只看該作者
40#
發(fā)表于 2025-3-28 13:42:11 | 只看該作者
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