@article {paolanti2017modelling,
	title = {Modelling and Forecasting Customer Navigation in Intelligent Retail Environments},
	journal = {Journal of Intelligent \& Robotic Systems},
	year = {2017},
	month = {10},
	pages = {1{\textendash}16},
	abstract = {<p>Understanding shopper behaviour is one of the keys to success for retailers. In particular, it is necessary that managers know which retail attributes are important to which shoppers and their main goal is to improve the consumer shopping experience. In this work, we present sCREEN (Consumer REtail ExperieNce), an intelligent mechatronic system for indoor navigation assistance in retail environments that minimizes the need for active tagging and does not require metrics maps. The tracking system is based on Ultra-wideband technology. The digital devices are installed in the shopping carts and baskets and sCREEN allows modelling and forecasting customer navigation in retail environments. This paper contributes the design of an intelligent mechatronic system with the use of a novel Hidden Markov Models (HMMs) for the representation of shoppers{\textquoteright} shelf/category attraction and usual retail scenarios such as product out of stock or changes on store layout. Observations are viewed as a perceived intelligent system performance. By forecasting consumers next shelf/category attraction, the system can present the item location information to the consumer, including a walking route map to a location of the product in the retail store, and/or the number of an aisle in which the product is located. Effective and efficient design processes for mechatronic systems are a prerequisite for competitiveness in an intelligent retail environment. Experiments are performed in a real retail environment that is a German supermarket, during business hours. A dataset, with consumers trajectories, timestamps and the corresponding ground truth for training as well as evaluating the HMM, have been built and made publicly available. The results in terms of Precision, Recall and F1-score demonstrate the effectiveness and suitability of our approach, with a precision value that exceeds the 76\% in all test cases.</p>
},
	issn = {0921-0296},
	doi = {10.1007/s10846-017-0674-7},
	url = {https://doi.org/10.1007/s10846-017-0674-7},
	author = {Marina Paolanti and Daniele Liciotti and R. Pietrini and Adriano Mancini and Emanuele Frontoni}
}
@conference {mancini2016multi,
	title = {A multi/hyper-spectral imaging system for land use/land cover using unmanned aerial systems},
	booktitle = {Unmanned Aircraft Systems (ICUAS), 2016 International Conference on},
	year = {2016},
	pages = {1148{\textendash}1155},
	publisher = {IEEE},
	organization = {IEEE},
	author = {Adriano Mancini and Emanuele Frontoni and Primo Zingaretti}
}
@article {Fulvio2016273,
	title = {Multi-Point Stereovision System for Contactless Dimensional Measurements},
	journal = {Journal of Intelligent and Robotic Systems: Theory and Applications},
	volume = {81},
	number = {2},
	year = {2016},
	note = {cited By 0},
	pages = {273-284},
	abstract = {<p>The paper focuses on the design of a fast and reliable multi-point vision-based measurement system able to be flexible, low cost and accurate for quality control in industrial robotics applications. The paper discusses a new method for integrating visual quality control in highly dynamic manufacturing lines, where products are added or removed from production. The structure of the vision-based measurement system is described. In particular, the stereo system is created by the movement of a single camera mounted on a six-axis manipulator. The visual software is structured in three phases: single camera and stereo calibration, addition of the visual inspection tasks for the object to be measured, stereo measurement. The feasibility of the proposed solution has been tested in a real industrial application with strong requirements on robot speed. The comparison of experimental results on a target with a simple and well-defined shape has shown that the proposed solution provides better results, in terms of accuracy and measurement speed, when compared to commercial libraries and RGBD vision systems. Besides, the robot motion policy adopted by the solution proposed here guarantees a continuous movement without the need for stop\&amp;go phases. {\textcopyright} 2015, Springer Science+Business Media Dordrecht.</p>
},
	doi = {10.1007/s10846-015-0249-4},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84955089994\&partnerID=40\&md5=9c4abfb9d88e7dff178bbd704c850bbb},
	author = {Fulvio, G.D. and Emanuele Frontoni and Adriano Mancini and Primo Zingaretti}
}
@article {Benini2012507,
	title = {A modular framework for fast prototyping of cooperative unmanned aerial vehicle},
	journal = {Journal of Intelligent and Robotic Systems: Theory and Applications},
	volume = {65},
	number = {1-4},
	year = {2012},
	note = {cited By 4},
	pages = {507-520},
	abstract = {Unmanned Aerial Vehicles (UAVs) are gaining increasing interest thanks to their flexibility and versatility. However, these systems are very complex and a good simulation platform is needed. In this paper, a new Framework for simulation and fast prototyping of UAV control laws is presented. The Framework exploits the high realism of the simulations carried out in a three-dimensional virtual environment with the easiness of use of development systems such as Matlab{\textregistered} for fast prototyping of control systems. Then a novel method that exploits the benefits of Model Predictive Control (MPC) for cooperative scenarios is introduced. The obtained results show good performances of MPC in solving the formation problem of unmanned aerial vehicles; finding an optimal solution and taking into account different constraints. The developed framework allows also to easily change from simulated agent to real one. {\textcopyright} 2011 Springer Science+Business Media B.V.},
	doi = {10.1007/s10846-011-9577-1},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84855437595\&partnerID=40\&md5=e426704537bd52bc6397b9152f01e978},
	author = {Benini, A. and Adriano Mancini and Minutolo, R. and Sauro Longhi and Montanari, M.b}
}
