@conference {Li2020,
	title = {Robot-to-Robot Relative Pose Estimation based on Semidefinite Relaxation Optimization},
	booktitle = {2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
	year = {2020},
	pages = {4491{\textendash}4498},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Las Vegas, NV, USA},
	abstract = {In this paper, the 2D robot-to-robot relative pose (position and orientation) estimation problem based on ego-motion and noisy distance measurements is considered. We address this problem using an optimization-based method, which does not require complicated numerical analysis while yields no inferior relative localization (RL) results compared to existing approaches. In particular, we start from a state-of-the-art method named square distances weighted least square (SD-WLS), and reformulate it as a non-convex quadratically constrained quadratic programming (QCQP) problem. To handle its non-convex nature, a semidefinite programming (SDP) relaxation optimization-based method is proposed, and we prove that the relaxation is tight when measurements are free from noise or just corrupted by small noise. Further, to obtain the optimal solution of the relative pose estimation problem in the sense of maximum likelihood estimation (MLE), a theoretically optimal WLS method is developed to refine the estimate from the SDP optimization. Comprehensive simulations and well-designed experiments are presented for validating the tightness of the SDP relaxation, and the effectiveness of the proposed algorithm is highlighted by comparing it to the existing approaches.},
	keywords = {Maximum likelihood estimation, Noise measurement, Optimization, Particle measurements, Pose estimation, Quadratic programming, Two dimensional displays},
	isbn = {978-1-7281-6213-3},
	issn = {2153-0858},
	doi = {10.1109/IROS45743.2020.9341568},
	author = {Ming Li and Guanqi Liang and Haobo Luo and Huihuan Qian and Tin Lun Lam}
}
@article {111,
	title = {Observer-Based Decentralized Fault Detection and Isolation Strategy for Networked Multirobot Systems},
	journal = {Control Systems Technology, IEEE Transactions on},
	volume = {PP},
	year = {2015},
	pages = {1-1},
	abstract = {In this paper, we present a distributed fault {detection} and isolation (FDI) strategy for a team of networked robots that builds on a distributed controller-observer schema. Remarkably different from other works in literature, the proposed FDI approach makes each robot of the team able to detect and isolate faults occurring on other robots, even if they are not direct neighbors. By means of a local observer, each robot can estimate the overall state of the team and it can use such an estimate to compute its local control input to achieve global tasks. The same information used by the local observers is also used to compute residual vectors, whose aim is to allow the detection and the isolation of actuator faults occurring on any robot of the team. Adaptive thresholds are derived based on the dynamics of the residual vectors by considering the presence of nonzero initial observer estimation errors, and noise terms affecting state measurement and model dynamics. The approach is validated via both numerical simulations and experiments involving four Khepera III mobile robots.},
	keywords = {Distributed multirobot systems, Fault detection, fault detection and isolation (FDI), networked robots., Noise, Noise measurement, Observers, Robots, Uncertainty, Vectors},
	issn = {1063-6536},
	doi = {10.1109/TCST.2014.2377175},
	url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=\&arnumber=7004823},
	author = {Arrichiello, F. and Marino, A. and Pierri, F.}
}
