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    author = {Innocente, Mauro S. and Grasso, Paolo},
    month = {5},
    pages = {80--101},
    volume = {34},
    doi = {10.1016/j.jocs.2019.04.009},
    issn = {18777503},
    keywords = {autonomous unmanned aerial vehicles, fire spread modelling, particle swarm, self-organisation, swarm robotics}
}

@article{paper:Superadditive_effect_of_multi-robot_coordination_in_the_exploration_of_unknown_environments_via_stigmergy,
    title = {Superadditive effect of multi-robot coordination in the exploration of unknown environments via stigmergy},
    year = {2015},
    journal = {Neurocomputing},
    author = {Kuyucu, Tüze and Tanev, Ivan and Shimohara, Katsunori},
    pages = {83--90},
    volume = {148},
    publisher = {Elsevier},
    doi = {10.1016/j.neucom.2012.07.062},
    issn = {18728286},
    keywords = {Exploration, Multi-robot coordination, Pheromones, Stigmergy, Swarm robotics}
}

@article{paper:Survey_of_Flocking_Algorithms_in_Multi-agent_Systems,
    title = {Survey of Flocking Algorithms in Multi-agent Systems},
    year = {2013},
    journal = {International Journal of Computer},
    author = {Barve, Aabha and Nene, Mj},
    number = {6},
    pages = {110--117},
    volume = {10},
    keywords = {flocking algorithms, formation control, leader-follower flocking, multi-agent systems, multi-robot systems, networks, wireless sensor}
}

@article{paper:Survey_on_Coverage_Path_Planning_with_Unmanned_Aerial_Vehicles,
    title = {Survey on coverage path planning with unmanned aerial vehicles},
    year = {2019},
    journal = {Drones},
    author = {Cabreira, Tauã M. and Brisolara, Lisane B. and Ferreira Paulo, R.},
    number = {1},
    pages = {1--38},
    volume = {3},
    isbn = {5553328438},
    doi = {10.3390/drones3010004},
    issn = {2504446X},
    keywords = {Approximate cellular decomposition, Coverage path planning, Exact cellular decomposition, Terrain coverage, Unmanned aerial vehicles}
}

@article{paper:Swarm_coordination_of_mini-UAVs_for_target_search_using_imperfect_sensors,
    title = {Swarm coordination of mini-UAVs for target search using imperfect sensors},
    year = {2018},
    journal = {Intelligent Decision Technologies},
    author = {Alfeo, Antonio L. and Cimino, Mario G.C.A. and De Francesco, Nicoletta and Lazzeri, Alessandro and Lega, Massimiliano and Vaglini, Gigliola},
    number = {2},
    pages = {149--162},
    volume = {12},
    doi = {10.3233/IDT-170317},
    issn = {18758843},
    keywords = {Swarm intelligence, flocking, mini-UAV, stigmergy, target search}
}

@book{book:Swarm_Intelligence,
    title = {Swarm Intelligence},
    year = {2001},
    author = {Eberhart, Russell C. and Shi, Yuhui and Kennedy, James},
    edition = {1st},
    publisher = {Morgan Kaufmann},
    isbn = {1558605959}
}

@article{paper:Swarm_Intelligence-Inspired_Autonomous_Flocking_Control_in_UAV_Networks,
    title = {Swarm Intelligence-Inspired Autonomous Flocking Control in UAV Networks},
    year = {2019},
    journal = {IEEE Access},
    author = {Dai, Fei and Chen, Ming and Wei, Xianglin and Wang, Huibin},
    pages = {61786--61796},
    volume = {7},
    doi = {10.1109/ACCESS.2019.2916004},
    issn = {21693536},
    keywords = {UAV network, energy, flocking control, swarm intelligent}
}


@Article{info12020051,
AUTHOR = {Godio, Simone and Primatesta, Stefano and Guglieri, Giorgio and Dovis, Fabio},
TITLE = {A Bioinspired Neural Network-Based Approach for Cooperative Coverage Planning of UAVs},
JOURNAL = {Information},
VOLUME = {12},
YEAR = {2021},
NUMBER = {2},
ARTICLE-NUMBER = {51},
URL = {https://www.mdpi.com/2078-2489/12/2/51},
ISSN = {2078-2489},
ABSTRACT = {This paper describes a bioinspired neural-network-based approach to solve a coverage planning problem for a fleet of unmanned aerial vehicles exploring critical areas. The main goal is to fully cover the map, maintaining a uniform distribution of the fleet on the map, and avoiding collisions between vehicles and other obstacles. This specific task is suitable for surveillance applications, where the uniform distribution of the fleet in the map permits them to reach any position on the map as fast as possible in emergency scenarios. To solve this problem, a bioinspired neural network structure is adopted. Specifically, the neural network consists of a grid of neurons, where each neuron has a local cost and has a local connection only with neighbor neurons. The cost of each neuron influences the cost of its neighbors, generating an attractive contribution to unvisited neurons. We introduce several controls and precautions to minimize the risk of collisions and optimize coverage planning. Then, preliminary simulations are performed in different scenarios by testing the algorithm in four maps and with fleets consisting of 3 to 10 vehicles. Results confirm the ability of the proposed approach to manage and coordinate the fleet providing the full coverage of the map in every tested scenario, avoiding collisions between vehicles, and uniformly distributing the fleet on the map.},
DOI = {10.3390/info12020051}
}

@INPROCEEDINGS{9476864,
  author={Sanna, Giovanni and Godio, Simone and Guglieri, Giorgio},
  booktitle={2021 International Conference on Unmanned Aircraft Systems (ICUAS)}, 
  title={Neural Network Based Algorithm for Multi-UAV Coverage Path Planning}, 
  year={2021},
  volume={},
  number={},
  pages={1210-1217},
  doi={10.1109/ICUAS51884.2021.9476864}
  }


@ARTICLE{9244647,
  author={Hu, Junyan and Niu, Hanlin and Carrasco, Joaquin and Lennox, Barry and Arvin, Farshad},
  journal={IEEE Transactions on Vehicular Technology}, 
  title={Voronoi-Based Multi-Robot Autonomous Exploration in Unknown Environments via Deep Reinforcement Learning}, 
  year={2020},
  volume={69},
  number={12},
  pages={14413-14423},
  doi={10.1109/TVT.2020.3034800}}
  

%% Journal article
@article{DBLP:journals/corr/abs-1911-10635,
	author    = {Kaiqing Zhang and	Zhuoran Yang and Tamer Basar},
	title     = {Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms},
	journal   = {CoRR},
	volume    = {abs/1911.10635},
	year      = {2019},
	url       = {http://arxiv.org/abs/1911.10635},
	eprinttype = {arXiv},
	eprint    = {1911.10635},
	timestamp = {Tue, 03 Dec 2019 14:15:54 +0100},
	biburl    = {https://dblp.org/rec/journals/corr/abs-1911-10635.bib},
	bibsource = {dblp computer science bibliography, https://dblp.org}
}


@article{DBLP:journals/corr/LoweWTHAM17,
	author    = {Ryan Lowe and Yi Wu and Aviv Tamar and Jean Harb and Pieter Abbeel and Igor Mordatch},
	title     = {Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments},
	journal   = {CoRR},
	volume    = {abs/1706.02275},
	year      = {2017},
	url       = {http://arxiv.org/abs/1706.02275},
	eprinttype = {arXiv},
	eprint    = {1706.02275},
	timestamp = {Mon, 13 Aug 2018 16:47:09 +0200},
	biburl    = {https://dblp.org/rec/journals/corr/LoweWTHAM17.bib},
	bibsource = {dblp computer science bibliography, https://dblp.org}
}


@article{8846699,  author={Qie, Han and Shi, Dianxi and Shen, Tianlong and Xu, Xinhai and Li, Yuan and Wang, Liujing},  journal={IEEE Access},   title={Joint Optimization of Multi-UAV Target Assignment and Path Planning Based on Multi-Agent Reinforcement Learning},   year={2019},  volume={7},  number={},  pages={146264-146272},  doi={10.1109/ACCESS.2019.2943253}}


@article{DBLP:journals/corr/abs-2003-10598,
	author    = {Hassam Ullah Sheikh and Ladislau Boloni},
	title     = {Multi-Agent Reinforcement Learning for Problems with Combined Individual and Team Reward},
	journal   = {CoRR},
	volume    = {abs/2003.10598},
	year      = {2020},
	url       = {https://arxiv.org/abs/2003.10598},
	eprinttype = {arXiv},
	eprint    = {2003.10598},
	timestamp = {Wed, 01 Apr 2020 17:39:11 +0200},
	biburl    = {https://dblp.org/rec/journals/corr/abs-2003-10598.bib},
	bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{buoys,
	author = {Kouzehgar, Maryam and Meghjani, Malika and Bouffanais, Roland},
	year = {2020},
	month = {12},
	pages = {},
	title = {Multi-Agent Reinforcement Learning for Dynamic Ocean Monitoring by a Swarm of Buoys}
}


@article{MPC_With_Convexation,
	author = {Greatwood, Colin and Richards, Arthur},
	year = {2019},
	month = {10},
	pages = {},
	title = {Reinforcement learning and model predictive control for robust embedded quadrotor guidance and control},
	volume = {43},
	journal = {Autonomous Robots},
	doi = {10.1007/s10514-019-09829-4}
}

@article{PreviousThesis,
author = {Gianpietro Battocletti and Riccardo Urban and Simone Godio and Giorgio Guglieri},
title = {RL-based Path Planning for Autonomous Aerial Vehicles in Unknown Environments},
booktitle = {AIAA AVIATION 2021 FORUM},
chapter = {},
pages = {},
doi = {10.2514/6.2021-3016},
URL = {https://arc.aiaa.org/doi/abs/10.2514/6.2021-3016},
eprint = {https://arc.aiaa.org/doi/pdf/10.2514/6.2021-3016}
}

@Article{s21072534,
	AUTHOR = {Doukhi, Oualid and Lee, Deok-Jin},
	TITLE = {Deep Reinforcement Learning for End-to-End Local Motion Planning of Autonomous Aerial Robots in Unknown Outdoor Environments: Real-Time Flight Experiments},
	JOURNAL = {Sensors},
	VOLUME = {21},
	YEAR = {2021},
	NUMBER = {7},
	ARTICLE-NUMBER = {2534},
	URL = {https://www.mdpi.com/1424-8220/21/7/2534},
	PubMedID = {33916624},
	ISSN = {1424-8220},
	ABSTRACT = {Autonomous navigation and collision avoidance missions represent a significant challenge for robotics systems as they generally operate in dynamic environments that require a high level of autonomy and flexible decision-making capabilities. This challenge becomes more applicable in micro aerial vehicles (MAVs) due to their limited size and computational power. This paper presents a novel approach for enabling a micro aerial vehicle system equipped with a laser range finder to autonomously navigate among obstacles and achieve a user-specified goal location in a GPS-denied environment, without the need for mapping or path planning. The proposed system uses an actor–critic-based reinforcement learning technique to train the aerial robot in a Gazebo simulator to perform a point-goal navigation task by directly mapping the noisy MAV’s state and laser scan measurements to continuous motion control. The obtained policy can perform collision-free flight in the real world while being trained entirely on a 3D simulator. Intensive simulations and real-time experiments were conducted and compared with a nonlinear model predictive control technique to show the generalization capabilities to new unseen environments, and robustness against localization noise. The obtained results demonstrate our system’s effectiveness in flying safely and reaching the desired points by planning smooth forward linear velocity and heading rates.},
	DOI = {10.3390/s21072534}
}




@article{ALFEO201934,
	title = {Enhancing biologically inspired swarm behavior: Metaheuristics to foster the optimization of UAVs coordination in target search},
	journal = {Computers and Operations Research},
	volume = {110},
	pages = {34-47},
	year = {2019},
	issn = {0305-0548},
	doi = {https://doi.org/10.1016/j.cor.2019.05.021},
	url = {https://www.sciencedirect.com/science/article/pii/S0305054819301340},
	author = {Antonio L. Alfeo and Mario G.C.A. Cimino and Gigliola Vaglini},
	keywords = {UAVs, Swarm intelligence, Stigmergy, Flocking, Differential evolution, Distributed targets search},
	abstract = {Recent miniaturization in Unmanned Aerial Vehicles (UAVs) technology encourages the use of many small UAVs for search missions in unknown environments, provided that the autonomous and adaptive coordination logic can be effective. In this research field, biologically inspired metaheuristics have been proposed to mimics swarms, flocks, and other coordination schemas. The design and management of such systems is a research challenge when considering (i) combination and optimization of multiple metaheuristics and (ii) enhancements of biologically inspired metaheuristic through technological advances. In this paper the swarm coordination of UAVs employed in target search is based on flocking and stigmergy, to provide robust formation control and dynamic environmental information sharing, respectively. The design of both metaheuristics takes into account UAVs equipment, and the coordination logic is adapted to the mission by means of a differential evolutionary algorithm. This algorithm optimizes the aggregated structural parameters of all metaheuristics to allow the most efficient coordination with respect to the mission environment. Some possible enhancements of stigmergy are studied by simulating target search tasks on synthetic and real-world scenarios.}
}

@article{StarcraftMARL, 
	title = {Grandmaster level in StarCraft II using multi-agent reinforcement learning.}, 
	journal = {Nature}, 
	volume = {575,7782}, 
	pages = {350-354}, 
	year = {2019}, 
	doi = {10.1038/s41586-019-1724-z}, 
	author = {Vinyals, Oriol et al.}, 
}

@ARTICLE{9187817,
	author={Wei, Xiaolong and Yang, Lifang and Cao, Gang and Lu, Tao and Wang, Bing},
	journal={IEEE Access}, 
	title={Recurrent MADDPG for Object Detection and Assignment in Combat Tasks}, 
	year={2020},
	volume={8},
	number={},
	pages={163334-163343},
	doi={10.1109/ACCESS.2020.3022638}}

@article{DBLP:journals/corr/abs-2104-06655,
	author    = {Yuan Pu and Shaochen Wang and Rui Yang and Xin Yao and Bin Li},
	title     = {Decomposed Soft Actor-Critic Method for Cooperative Multi-Agent Reinforcement Learning},
	journal   = {CoRR},
	volume    = {abs/2104.06655},
	year      = {2021},
	url       = {https://arxiv.org/abs/2104.06655},
	eprinttype = {arXiv},
	eprint    = {2104.06655},
	timestamp = {Mon, 19 Apr 2021 16:45:47 +0200},
	biburl    = {https://dblp.org/rec/journals/corr/abs-2104-06655.bib},
	bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{Adepegba2016MultiAgentAC,
	title={Multi-Agent Area Coverage Control Using Reinforcement Learning},
	author={Adekunle A. Adepegba and Md. Suruz Miah and Davide Spinello},
	booktitle={FLAIRS Conference},
	year={2016}
}

@article{coopdist,
	author = {Pham, Huy and La, Hung and Feil-Seifer, David and Nguyen, Luan},
	year = {2018},
	month = {03},
	pages = {},
	title = {Cooperative and Distributed Reinforcement Learning of Drones for Field Coverage}
}

@ARTICLE{9330612,
	author={Luis, Samuel Yanes and Reina, Daniel Gutiérrez and Marín, Sergio L. Toral},
	journal={IEEE Access}, 
	title={A Multiagent Deep Reinforcement Learning Approach for Path Planning in Autonomous Surface Vehicles: The Ypacaraí Lake Patrolling Case}, 
	year={2021},
	volume={9},
	number={},
	pages={17084-17099},
	doi={10.1109/ACCESS.2021.3053348}
}

@article{underwater,
	author = {Rahmati, Mehdi and Nadeem, Mohammad and Sadhu, Vidyasagar and Pompili, Dario},
	year = {2019},
	month = {11},
	pages = {},
	title = {UW-MARL: Multi-Agent Reinforcement Learning for Underwater Adaptive Sampling using Autonomous Vehicles}
}

@article{10.1115/1.2018-JUN-4,
	author = {Davoodi, Mohammadreza and Mohammadpour Velni, Javad and Li, Changying},
	title = "{Coverage Control with Multiple Ground Robots for Precision Agriculture}",
	journal = {Mechanical Engineering},
	volume = {140},
	number = {06},
	pages = {S4-S8},
	year = {2018},
	month = {06},
	abstract = "{In this work, coverage of agricultural ﬁelds using a team of autonomous unmanned ground robots with no human intervention is investigated. To this end, ﬁeld is ﬁrst represented by a topological mapandthenadistributedenergy-awaredeployment strategy is proposed to optimally distribute robots with the aim of persistent monitoring of speciﬁed regions of interest. When a robot participating in the coverage task approaches a low energy reserve, the team of robots collectively and cooperatively adjust the coverage formation to allow the agent to return to a designated base station, where it can recharge before rejoining the effort. Preliminary (simulation) results are provided to show the effectiveness and capabilities of the proposed coverage algorithm.}",
	issn = {0025-6501},
	doi = {10.1115/1.2018-JUN-4},
	url = {https://doi.org/10.1115/1.2018-JUN-4},
	eprint = {https://asmedigitalcollection.asme.org/memagazineselect/article-pdf/140/06/S4/6384102/me-2018-jun4.pdf},
}


@article{DBLP:journals/corr/SchulmanWDRK17,
	author    = {John Schulman and Filip Wolski and Prafulla Dhariwal and Alec Radford and Oleg Klimov},
	title     = {Proximal Policy Optimization Algorithms},
	journal   = {CoRR},
	volume    = {abs/1707.06347},
	year      = {2017},
	url       = {http://arxiv.org/abs/1707.06347},
	eprinttype = {arXiv},
	eprint    = {1707.06347},
	timestamp = {Mon, 13 Aug 2018 16:47:34 +0200},
	biburl    = {https://dblp.org/rec/journals/corr/SchulmanWDRK17.bib},
	bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{scikit-learn,
	title={Scikit-learn: Machine Learning in Python},
	author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.	and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P. and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
	journal={Journal of Machine Learning Research},
	volume={12},
	pages={2825--2830},
	year={2011}
}

@misc{tensorflow2015-whitepaper,
  doi = {10.48550/ARXIV.1603.04467},  
  url = {https://arxiv.org/abs/1603.04467},  
  author = {Abadi, Martín and Agarwal, Ashish and Barham, Paul and Brevdo, Eugene and Chen, Zhifeng and Citro, Craig and Corrado, Greg S. and Davis, Andy and Dean, Jeffrey and Devin, Matthieu and Ghemawat, Sanjay and Goodfellow, Ian and Harp, Andrew and Irving, Geoffrey and Isard, Michael and Jia, Yangqing and Jozefowicz, Rafal and Kaiser, Lukasz and Kudlur, Manjunath and Levenberg, Josh and Mane, Dan and Monga, Rajat and Moore, Sherry and Murray, Derek and Olah, Chris and Schuster, Mike and Shlens, Jonathon and Steiner, Benoit and Sutskever, Ilya and Talwar, Kunal and Tucker, Paul and Vanhoucke, Vincent and Vasudevan, Vijay and Viegas, Fernanda and Vinyals, Oriol and Warden, Pete and Wattenberg, Martin and Wicke, Martin and Yu, Yuan and Zheng, Xiaoqiang},  
  keywords = {Distributed, Parallel, and Cluster Computing (cs.DC), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences},  
  title = {TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems},  
  publisher = {arXiv},  
  year = {2016},  
  copyright = {arXiv.org perpetual, non-exclusive license}
}


@book{sutton2018reinforcement,
	title={Reinforcement learning: An introduction},
	author={Sutton, Richard S and Barto, Andrew G},
	year={2018},
	publisher={MIT press}
}

@misc{https://doi.org/10.48550/arxiv.2203.11318,
  doi = {10.48550/ARXIV.2203.11318},
  url = {https://arxiv.org/abs/2203.11318},
  author = {Pretorius, Ruan and van Zyl, Terence},
  keywords = {Portfolio Management (q-fin.PM), Machine Learning (cs.LG), FOS: Economics and business, FOS: Economics and business, FOS: Computer and information sciences, FOS: Computer and information sciences},
  title = {Deep Reinforcement Learning and Convex Mean-Variance Optimisation for Portfolio Management},
  publisher = {arXiv},
  year = {2022},
  copyright = {Creative Commons Attribution Share Alike 4.0 International}
}

@article{Li_2021,
	doi = {10.1088/1742-6596/2113/1/012030},
	url = {https://doi.org/10.1088/1742-6596/2113/1/012030},
	year = {2021},
	month = {nov},
	publisher = {IOP Publishing},
	volume = {2113},
	number = {1},
	pages = {012030},
	author = {Jing Li and Yanyang Liu and Xianguo Qing and Kai Xiao and Ying Zhang and Pengcheng Yang and Yue (Marco) Yang},
	title = {The application of Deep Reinforcement Learning in Coordinated Control of Nuclear Reactors},
	journal = {Journal of Physics: Conference Series},
	abstract = {The nuclear reactor control system plays a crucial role in the operation of nuclear power plants. The coordinated control of power control and steam generator level control has become one of the most important control problems in these systems. In this paper, we propose a mathematical model of the coordinated control system, and then transform it into a reinforcement learning model and develop a deep reinforcement learning control algorithm so-called DDPG algorithm to solve the problem. Through simulation experiments, our proposed algorithm has shown an extremely remarkable control performance.}
}

@INPROCEEDINGS{7989385,
  author={Gu, Shixiang and Holly, Ethan and Lillicrap, Timothy and Levine, Sergey},
  booktitle={2017 IEEE International Conference on Robotics and Automation (ICRA)}, 
  title={Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates}, 
  year={2017},
  volume={},
  number={},
  pages={3389-3396},
  doi={10.1109/ICRA.2017.7989385}}
  
@article{DBLP:journals/corr/abs-2005-13857,
  author    = {Hartmut Surmann and Christian Jestel and Robin Marchel and Franziska Musberg and Houssem Elhadj and Mahbube Ardani},
  title     = {Deep Reinforcement learning for real autonomous mobile robot navigation in indoor environments},
  journal   = {CoRR},
  volume    = {abs/2005.13857},
  year      = {2020},
  url       = {https://arxiv.org/abs/2005.13857},
  eprinttype = {arXiv},
  eprint    = {2005.13857},
  timestamp = {Wed, 03 Jun 2020 11:36:54 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2005-13857.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@InProceedings{10.2991/978-94-6239-102-4_136,
author={XIA, Chen and EL KAMEL, A.},
editor={Qi, Ershi and Shen, Jiang and Dou, Runliang},
title={A Reinforcement Learning Method of Obstacle Avoidance for Industrial Mobile Vehicles in Unknown Environments Using Neural Network},
booktitle={Proceedings of the 21st International Conference on Industrial Engineering and Engineering Management 2014},
year={2015},
publisher={Atlantis Press},
address={Paris},
pages={671--675},
abstract={This paper presents a reinforcement learning method for a mobile vehicle to navigate autonomously in an unknown environment. Q-learning algorithm is a model-free reinforcement learning technique and is applied to realize the robot self-learning ability. The state-action Q-values are traditionally stored in a Q table, which will decrease the learning speed when large storage memory is needed. The neural network has a strong ability to deal with large-scale state spaces. Therefore, the neural network is introduced to work with Q-learning to ensure the self-learning efficiency of avoiding obstacles for industrial vehicles in unpredictable environments. Experiment results show that an autonomous mobile vehicle using the proposed method can successfully navigate to the target place without colliding with obstacles, and hence prove the self-learning ability of navigation in an unknown environment.},
isbn={978-94-6239-102-4}
}

@article{doi:10.1177/1729881421992621,
author = {Matej Dobrevski and Danijel Skočaj},
title ={Deep reinforcement learning for map-less goal-driven robot navigation},
journal = {International Journal of Advanced Robotic Systems},
volume = {18},
number = {1},
pages = {1729881421992621},
year = {2021},
doi = {10.1177/1729881421992621},
URL = {https://doi.org/10.1177/1729881421992621},
eprint = {https://doi.org/10.1177/1729881421992621},
abstract = {Mobile robots that operate in real-world environments need to be able to safely navigate their surroundings. Obstacle avoidance and path planning are crucial capabilities for achieving autonomy of such systems. However, for new or dynamic environments, navigation methods that rely on an explicit map of the environment can be impractical or even impossible to use. We present a new local navigation method for steering the robot to global goals without relying on an explicit map of the environment. The proposed navigation model is trained in a deep reinforcement learning framework based on Advantage Actor–Critic method and is able to directly translate robot observations to movement commands. We evaluate and compare the proposed navigation method with standard map-based approaches on several navigation scenarios in simulation and demonstrate that our method is able to navigate the robot also without the map or when the map gets corrupted, while the standard approaches fail. We also show that our method can be directly transferred to a real robot.}
}


@article{ZHANG2021102668,
author = {Zhang, Juan and Campbell, James and Sweeney, Donald and Hupman, Andrea},
year = {2020},
month = {05},
pages = {},
title = {Energy Consumption Models for Delivery Drones: A Comparison and Assessment}
}

@Article{vehicles3030027,
	AUTHOR = {Karur, Karthik and Sharma, Nitin and Dharmatti, Chinmay and Siegel, Joshua E.},
	TITLE = {A Survey of Path Planning Algorithms for Mobile Robots},
	JOURNAL = {Vehicles},
	VOLUME = {3},
	YEAR = {2021},
	NUMBER = {3},
	PAGES = {448--468},
	URL = {https://www.mdpi.com/2624-8921/3/3/27},
	ISSN = {2624-8921},
	ABSTRACT = {Path planning algorithms are used by mobile robots, unmanned aerial vehicles, and autonomous cars in order to identify safe, efficient, collision-free, and least-cost travel paths from an origin to a destination. Choosing an appropriate path planning algorithm helps to ensure safe and effective point-to-point navigation, and the optimal algorithm depends on the robot geometry as well as the computing constraints, including static/holonomic and dynamic/non-holonomically-constrained systems, and requires a comprehensive understanding of contemporary solutions. The goal of this paper is to help novice practitioners gain an awareness of the classes of path planning algorithms used today and to understand their potential use cases—particularly within automated or unmanned systems. To that end, we provide broad, rather than deep, coverage of key and foundational algorithms, with popular algorithms and variants considered in the context of different robotic systems. The definitions, summaries, and comparisons are relevant to novice robotics engineers and embedded system developers seeking a primer of available algorithms.},
	DOI = {10.3390/vehicles3030027}
}

@article{doi:10.1177/1729881421992621,
	author = {Matej Dobrevski and Danijel Skočaj},
	title ={Deep reinforcement learning for map-less goal-driven robot navigation},
	journal = {International Journal of Advanced Robotic Systems},
	volume = {18},
	number = {1},
	pages = {1729881421992621},
	year = {2021},
	doi = {10.1177/1729881421992621},	
	URL = { https://doi.org/10.1177/1729881421992621},
	eprint = {https://doi.org/10.1177/1729881421992621},
	abstract = {Mobile robots that operate in real-world environments need to be able to safely navigate their surroundings. Obstacle avoidance and path planning are crucial capabilities for achieving autonomy of such systems. However, for new or dynamic environments, navigation methods that rely on an explicit map of the environment can be impractical or even impossible to use. We present a new local navigation method for steering the robot to global goals without relying on an explicit map of the environment. The proposed navigation model is trained in a deep reinforcement learning framework based on Advantage Actor–Critic method and is able to directly translate robot observations to movement commands. We evaluate and compare the proposed navigation method with standard map-based approaches on several navigation scenarios in simulation and demonstrate that our method is able to navigate the robot also without the map or when the map gets corrupted, while the standard approaches fail. We also show that our method can be directly transferred to a real robot. }
}

@article{DBLP:journals/corr/abs-2003-10598,
	author    = {Hassam Ullah Sheikh and Ladislau Boloni},
	title     = {Multi-Agent Reinforcement Learning for Problems with Combined Individual and Team Reward},
	journal   = {CoRR},
	volume    = {abs/2003.10598},
	year      = {2020},
	url       = {https://arxiv.org/abs/2003.10598},
	eprinttype = {arXiv},
	eprint    = {2003.10598},
	timestamp = {Wed, 01 Apr 2020 17:39:11 +0200},
	biburl    = {https://dblp.org/rec/journals/corr/abs-2003-10598.bib},
	bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/corr/abs-2012-11641,
	author    = {Maryam Kouzehgar and Malika Meghjani and Roland Bouffanais},
	title     = {Multi-Agent Reinforcement Learning for Dynamic Ocean Monitoring by	a Swarm of Buoys},
	journal   = {CoRR},
	volume    = {abs/2012.11641},
	year      = {2020},
	url       = {https://arxiv.org/abs/2012.11641},
	eprinttype = {arXiv},
	eprint    = {2012.11641},
	timestamp = {Mon, 04 Jan 2021 16:33:46 +0100},
	biburl    = {https://dblp.org/rec/journals/corr/abs-2012-11641.bib},
	bibsource = {dblp computer science bibliography, https://dblp.org}
}


@inproceedings{Adepegba2016MultiAgentAC,
	title={Multi-Agent Area Coverage Control Using Reinforcement Learning},
	author={Adekunle A. Adepegba and Md. Suruz Miah and Davide Spinello},
	booktitle={FLAIRS Conference},
	year={2016}
}

@article{DBLP:journals/corr/abs-1803-07250,
	author    = {Huy Xuan Pham and Hung Manh La and	David Feil-Seifer and Luan Van Nguyen},
	title     = {Cooperative and Distributed Reinforcement Learning of Drones for Field Coverage},
	journal   = {CoRR},
	volume    = {abs/1803.07250},
	year      = {2018},
	url       = {http://arxiv.org/abs/1803.07250},
	eprinttype = {arXiv},
	eprint    = {1803.07250},
	timestamp = {Mon, 13 Aug 2018 16:47:03 +0200},
	biburl    = {https://dblp.org/rec/journals/corr/abs-1803-07250.bib},
	bibsource = {dblp computer science bibliography, https://dblp.org}
}

@book{sutton2018reinforcement,
	title={Reinforcement learning: An introduction},
	author={Sutton, Richard S and Barto, Andrew G},
	year={2018},
	publisher={MIT press}
}

@article{doi:10.1061/(ASCE)AS.1943-5525.0001336,
author = {Alessandro Minervini  and Simone Godio  and Giorgio Guglieri  and Fabio Dovis  and Alfredo Bici },
title = {Development and Validation of a LQR-Based Quadcopter Control Dynamics Simulation Model},
journal = {Journal of Aerospace Engineering},
volume = {34},
number = {6},
pages = {04021095},
year = {2021},
doi = {10.1061/(ASCE)AS.1943-5525.0001336},

URL = {https://ascelibrary.org/doi/abs/10.1061/%28ASCE%29AS.1943-5525.0001336},
eprint = {https://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29AS.1943-5525.0001336}
,
    abstract = { The growing applications involving unmanned aerial vehicles (UAVs) are requiring more advanced control algorithms to improve rotary-wing UAVs’ performance. To preliminarily tune such advanced controllers, an experimental approach could take a long time and also be dangerous for the vehicle and the onboard hardware components. In this paper, a simulation model of a quadcopter is developed and validated by the comparison of simulation results and experimental data collected during flight tests. For this purpose, an open-source flight controller for quadcopter UAVs is developed and a linear quadratic regulator (LQR) controller is implemented as the control strategy. The input physical quantities are experimentally measured; hence, the LQR controller parameters are tuned on the simulation model. The same tuning is proposed on the developed flight controller with satisfactory results. Finally, flight data and simulation results are compared showing a reliable approximation of the experimental data by the model. Because numerous state-of-the-art simulation models are available, but accurately validated ones are not easy to find, the main purpose of this work is to provide a reliable tool to evaluate the performance for this UAV configuration. }
}




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	address={Boston, MA},
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	url={https://doi.org/10.1007/978-0-387-30164-8_363}
}

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  doi = {10.48550/ARXIV.1502.01852},
  
  url = {https://arxiv.org/abs/1502.01852},
  
  author = {He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
  
  keywords = {Computer Vision and Pattern Recognition (cs.CV), Artificial Intelligence (cs.AI), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences},
  
  title = {Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification},
  
  publisher = {arXiv},
  
  year = {2015},
  
  copyright = {arXiv.org perpetual, non-exclusive license}
}


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  year={2016}
}



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year = {2019},
month = {01},
pages = {4},
title = {Survey on Coverage Path Planning with Unmanned Aerial Vehicles},
volume = {3},
journal = {Drones},
doi = {10.3390/drones3010004}
}

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doi = {10.1016/j.robot.2013.09.004}
}


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title = {Survey on path and view planning for UAVs},
volume = {2},
journal = {Virtual Reality and Intelligent Hardware},
doi = {10.1016/j.vrih.2019.12.004}
}

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@mastersthesis{TM,
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  title        = {LA PIATTAFORMA ROS PER LO SVILUPPO DI APPLICAZIONI PER LA ROBOTICA: PANORAMICA E CASO DI STUDIO},
  school       = {Università di Bologna},
  year         = {2019}
}

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@MISC{master,
author = {Yanqing, W.},
title = {ROS.org documentation - master},
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year = {2018},
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@misc{GS_Min,
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}


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author = {Dr. Palik, Matyas and Nagy, Máté},
year = {2019},
month = {01},
pages = {155-166},
title = {Brief history of UAV development},
volume = {31},
journal = {Repüléstudományi Közlemények},
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}

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author = {Dekoulis, George},
year = {2018},
month = {06},
pages = {},
title = {Introductory Chapter: Drones},
isbn = {978-1-78923-284-4},
doi = {10.5772/intechopen.76943}
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@article{JANEFOX2022102089,
title = {Drones: Foreseeing a 'risky' business? Policing the challenge that flies above},
journal = {Technology in Society},
volume = {71},
pages = {102089},
year = {2022},
issn = {0160-791X},
doi = {https://doi.org/10.1016/j.techsoc.2022.102089},
url = {https://www.sciencedirect.com/science/article/pii/S0160791X22002305},
author = {Sarah {Jane Fox}},
keywords = {Drones, Warfare, Risk, Police/policing, Governance, Evolution/revolution},
abstract = {Drones are one of the most disruptive technologies of our time. They have the ability to do so much good, but history and the military legacy of airborne vehicles serves as an indicator of the potential risk to society that drones also present. This paper combines a law/policy, socio-legal approach that considers the history of technology that flies above society and invariably the linkage to warfare. Discussion is given to technological advancements alongside anticipatory measures and governance approaches that need to be factored in prior to unleashing certain technologies on society. This includes the means of policing drones alongside the police use of drones. Case studies are included to contextualise the use and misuse of drones as part of the conclusion. The findings are that drones are set to be a massive challenge and that there has been a lack of foresight, despite warnings, predictions and historical lessons which should have served as a premonitory indicator of the safety/security risks of drones. Ultimately, more protection is needed before the benefits of drones to society can be realised.}
}

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month = {11},
pages = {1-5},
title = {Preliminary Evaluation of Dog-Drone Technological Interfaces: Challenges and Opportunities},
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}

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author = {Pieri, David and Diaz, Jorge and Bland, Geoff and Fladeland, Matthew and Madrigal, Yetty and Corrales Corrales, Ernesto and Alegria, Oscar and Alan, Alfredo and Realmuto, Vincent and Miles, Ted and Abtahi, Ali},
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journal = {Geological Society of London Special Publications},
doi = {10.1144/SP380.13}
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author = {Juhasz, Ondrej and Lopez, Mark and Berrios, Marcos and Berger, Tom and Tischler, Mark},
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title = {Turbulence Modeling of a Small Quadrotor UAS Using System Identification from Flight Data}
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author = {Invernizzi, Davide and Panza, Simone and Lovera, Marco},
year = {2020},
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@misc{WebFAA,
  title = {FAA AEROSPACE FORECAST - Fisical Years 2022-2042},
  howpublished = {\url{https://www.faa.gov/sites/faa.gov/files/2022-06/FY2022_42_FAA_Aerospace_Forecast.pdf}},
  note = {Accessed: 2023-01-19}
}



