@conference {2014f-ZelFraRob,
	title = {Rigidity Theory in SE(2) for Unscaled Relative Position Estimation using only Bearing},
	booktitle = {2014 European Control Conference},
	year = {2014},
	month = {06/2014},
	pages = {2703-2708},
	address = {Strasbourg, France},
	abstract = {This work considers the problem of estimating the unscaled relative positions of a multi-robot team in a common reference frame from bearing-only measurements. Each robot has access to a relative bearing measurement taken from the local body frame of the robot, and the robots have no knowledge of a common reference frame. An extension of rigidity theory is made for frameworks embedded in the special Euclidean group SE(2) = R^2  {\texttimes} S1. We introduce definitions describing rigidity for SE(2) frameworks and provide necessary and sufficient conditions for when such a framework is infinitesimally rigid in SE(2). We then introduce the directed bearing rigidity matrix and show that an SE(2) framework is infinitesimally rigid if and only if the rank of this matrix is equal to 2|V| - 4, where |V| is the number of agents in the ensemble. The directed bearing rigidity matrix and its properties are then used in the implementation and convergence proof of a distributed estimator to determine the unscaled relative positions in a common frame. Simulation results are given to support the
analysis.},
	keywords = {Localization of ground robots, Motion control of multiple robots, Rigidity mainenance},
	attachments = {https://homepages.laas.fr/afranchi/robotics/sites/default/files/2014f-ZelFraRob-preprint.pdf},
	author = {Daniel Zelazo and Antonio Franchi and Paolo Robuffo Giordano}
}
@article {2013g-FraOriSte,
	title = {Mutual Localization in Multi-Robot Systems using Anonymous Relative Measurements},
	journal = {The International Journal of Robotics Research},
	volume = {32},
	year = {2013},
	month = {09/2013},
	pages = {1303-1322},
	abstract = {We propose a decentralized method to perform mutual localization in multi-robot systems using anonymous relative measurements, i.e., measurements that do not include the identity of the measured robot. This is a challenging and practically relevant operating scenario that has received little attention in the literature. Our mutual localization algorithm includes two main components: a probabilistic multiple registration stage, which provides all data associations that are consistent with the relative robot measurements and the current belief, and a dynamic filtering stage, which incorporates odometric data into the estimation process. The design of the proposed method proceeds from a detailed formal analysis of the implications of anonymity on the mutual localization problem. Experimental results on a team of differential-drive robots illustrate the effectiveness of the approach, and in particular its robustness against false positives and negatives that may affect the robot measurement process. We also provide an experimental comparison that shows how the proposed method outperforms more classical approaches that may be designed building on existing techniques. The source code of the proposed method is available within the MLAM ROS stack.},
	keywords = {Localization of ground robots},
	attachments = {https://homepages.laas.fr/afranchi/robotics/sites/default/files/2013g-FraOriSte.pdf , https://homepages.laas.fr/afranchi/robotics/sites/default/files/2013g-FraOriSte.mp4},
	author = {Antonio Franchi and Giuseppe Oriolo and Paolo Stegagno}
}
@mastersthesis {60,
	title = {Decentralized Methods for Cooperative Task  Execution in Multi-robot Systems},
	year = {2009},
	month = {12/2009},
	school = {"La Sapienza" University of Rome},
	type = {PhD},
	keywords = {Coverage, Decentralized control, Distributed algorithms, Estimation, Exploration, Formation control, Localization of ground robots, Middleware for robotics, Motion control of multiple robots, Multi-robot systems, Patrolling / Surveillance, Pursuit-evasion / Clearing},
	attachments = {https://homepages.laas.fr/afranchi/robotics/sites/default/files/2009-Franchi_PhDThesis.pdf},
	author = {Antonio Franchi}
}
