(memory) added references to chap 1 and edited chap 1-3, remaining li…

…nks chap 3

memoria/bibliografia.bib

@@ -155,6 +155,85 @@ @article{logic-theorist
 	doi = {10.1177/154193120605000904}
 }
 
+@misc{christl2020visionbased,
+	title={Vision-Based Autonomous Drone Control using Supervised Learning in Simulation},
+	author={Max Christl},
+	year={2020},
+	eprint={2009.04298},
+	archivePrefix={arXiv},
+	primaryClass={cs.RO}
+}
+
+@article{wang2019unsupervised,
+	title = {Unsupervised Anomaly Detection with Compact Deep Features for Wind Turbine Blade Images Taken by a Drone},
+	author = {Wang, Y. and Yoshihashi, R. and Kawakami, R. and others},
+	journal = {IPSJ Transactions on Computer Vision and Applications},
+	volume = {11},
+	number = {3},
+	pages = {1-10},
+	year = {2019},
+	doi = {10.1186/s41074-019-0056-0},
+	url = {https://doi.org/10.1186/s41074-019-0056-0},
+	received = {14 March 2019},
+	accepted = {30 April 2019},
+	published = {04 June 2019}
+}
+
+@Article{electronics10090999,
+	AUTHOR = {Azar, Ahmad Taher and Koubaa, Anis and Ali Mohamed, Nada and Ibrahim, Habiba A. and Ibrahim, Zahra Fathy and Kazim, Muhammad and Ammar, Adel and Benjdira, Bilel and Khamis, Alaa M. and Hameed, Ibrahim A. and Casalino, Gabriella},
+	TITLE = {Drone Deep Reinforcement Learning: A Review},
+	JOURNAL = {Electronics},
+	VOLUME = {10},
+	YEAR = {2021},
+	NUMBER = {9},
+	ARTICLE-NUMBER = {999},
+	URL = {https://www.mdpi.com/2079-9292/10/9/999},
+	ISSN = {2079-9292},
+	ABSTRACT = {Unmanned Aerial Vehicles (UAVs) are increasingly being used in many challenging and diversified applications. These applications belong to the civilian and the military fields. To name a few; infrastructure inspection, traffic patrolling, remote sensing, mapping, surveillance, rescuing humans and animals, environment monitoring, and Intelligence, Surveillance, Target Acquisition, and Reconnaissance (ISTAR) operations. However, the use of UAVs in these applications needs a substantial level of autonomy. In other words, UAVs should have the ability to accomplish planned missions in unexpected situations without requiring human intervention. To ensure this level of autonomy, many artificial intelligence algorithms were designed. These algorithms targeted the guidance, navigation, and control (GNC) of UAVs. In this paper, we described the state of the art of one subset of these algorithms: the deep reinforcement learning (DRL) techniques. We made a detailed description of them, and we deduced the current limitations in this area. We noted that most of these DRL methods were designed to ensure stable and smooth UAV navigation by training computer-simulated environments. We realized that further research efforts are needed to address the challenges that restrain their deployment in real-life scenarios.},
+	DOI = {10.3390/electronics10090999}
+}
+
+@INPROCEEDINGS{9636053,
+	author={Song, Yunlong and Steinweg, Mats and Kaufmann, Elia and Scaramuzza, Davide},
+	booktitle={2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
+	title={Autonomous Drone Racing with Deep Reinforcement Learning},
+	year={2021},
+	volume={},
+	number={},
+	pages={1205-1212},
+	doi={10.1109/IROS51168.2021.9636053}
+}
+
+@article{CHEN2022939,
+	title = {Deep Q-learning for same-day delivery with vehicles and drones},
+	journal = {European Journal of Operational Research},
+	volume = {298},
+	number = {3},
+	pages = {939-952},
+	year = {2022},
+	issn = {0377-2217},
+	doi = {https://doi.org/10.1016/j.ejor.2021.06.021},
+	url = {https://www.sciencedirect.com/science/article/pii/S0377221721005361},
+	author = {Xinwei Chen and Marlin W. Ulmer and Barrett W. Thomas},
+	keywords = {Transportation, Same-day delivery, Reinforcement learning, Dynamic vehicle routing},
+	abstract = {In this paper, we consider same-day delivery with vehicles and drones. Customers make delivery requests over the course of the day, and the dispatcher dynamically dispatches vehicles and drones to deliver the goods to customers before their delivery deadline. Vehicles can deliver multiple packages in one route but travel relatively slowly due to the urban traffic. Drones travel faster, but they have limited capacity and require charging or battery swaps. To exploit the different strengths of the fleets, we propose a deep Q-learning approach. Our method learns the value of assigning a new customer to either drones or vehicles as well as the option to not offer service at all. In a systematic computational analysis, we show the superiority of our policy compared to benchmark policies and the effectiveness of our deep Q-learning approach. We also show that the combination of state and action features is very valuable and that our policy can maintain effectiveness when demand data and the fleet size change moderately.}
+}
+
+@Article{s23010188,
+	AUTHOR = {Kabiri, Meisam and Cimarelli, Claudio and Bavle, Hriday and Sanchez-Lopez, Jose Luis and Voos, Holger},
+	TITLE = {A Review of Radio Frequency Based Localisation for Aerial and Ground Robots with 5G Future Perspectives},
+	JOURNAL = {Sensors},
+	VOLUME = {23},
+	YEAR = {2023},
+	NUMBER = {1},
+	ARTICLE-NUMBER = {188},
+	URL = {https://www.mdpi.com/1424-8220/23/1/188},
+	PubMedID = {36616782},
+	ISSN = {1424-8220},
+	ABSTRACT = {Efficient localisation plays a vital role in many modern applications of Unmanned Ground Vehicles (UGV) and Unmanned Aerial Vehicles (UAVs), which contributes to improved control, safety, power economy, etc. The ubiquitous 5G NR (New Radio) cellular network will provide new opportunities to enhance the localisation of UAVs and UGVs. In this paper, we review radio frequency (RF)-based approaches to localisation. We review the RF features that can be utilized for localisation and investigate the current methods suitable for Unmanned Vehicles under two general categories: range-based and fingerprinting. The existing state-of-the-art literature on RF-based localisation for both UAVs and UGVs is examined, and the envisioned 5G NR for localisation enhancement, and the future research direction are explored.},
+	DOI = {10.3390/s23010188}
+}
+
 @Electronic{history-ai,
 	Title = {\textit{The History of Artificial Intelligence}},
 	howpublished= {\url{https://sitn.hms.harvard.edu/flash/2017/history-artificial-intelligence/}},
@@ -418,4 +497,4 @@ @Electronic{poly-info
 	howpublished= {\url{https://www.geeksforgeeks.org/how-to-check-if-a-given-point-lies-inside-a-polygon/}},
 	author = {geeksforgeeks},
 	year = {2023}
-}
+}