Joelle Al Hage

Fault tolerant collaborative localization for multi-robot system

Multi-robot system is used in some unreachable or dangerous area in order to replace the human operators. In such environments the integrity of localization should be assured by adding a sensor fault diagnosis step. In this paper, we present a method able, in addition of localizing a group of robots, to detect and exclude the faulty sensors from the team. The estimator is the informational form of the Kalman Filter (KF) namely Information Filter (IF). The developed residual test is based on the divergence between the predicted and the corrected estimation of the IF, calculated in term of the Kullback-Leibler divergence (KLD). The main contributions of this paper: - developing a method able simultaneously to localize a group of robots and to detect the faulty sensors - using the IF and the KLD as a residual test - Application of the proposed framework to a real environment with real robots.

Fault detection and exclusion method for a tightly coupled localization system

Integrity monitoring for a positioning method permit us to guarantee a high integrity localization which is needed for an autonomous navigation system. Different approaches for localization integrity monitoring have been developed. In this paper, we propose a Fault Detection and Exclusion (FDE) method based on information metrics since it provides tools that allow designing residual test that increase the integrity of localization. A residual test based on the Kullback-Leibler divergence (KLD) is elaborated. It is integrated in a FDE architecture applied to localization using a tightly coupled multi-sensor (GPS and odometer) data fusion method.

Fault tolerant multi-sensor fusion based on the information gain

In the last decade, multi-robot systems are used in several applications like for example, the army, the intervention areas presenting danger to human life, the management of natural disasters, the environmental monitoring, exploration and agriculture. The integrity of localization of the robots must be ensured in order to achieve their mission in the best conditions. Robots are equipped with proprioceptive (encoders, gyroscope) and exteroceptive sensors (Kinect). However, these sensors could be affected by various faults types that can be assimilated to erroneous measurements, bias, outliers, drifts,... In absence of a sensor fault diagnosis step, the integrity and the continuity of the localization are affected. In this work, we present a muti-sensors fusion approach with Fault Detection and Exclusion (FDE) based on the information theory. In this context, we are interested by the information gain given by an observation which may be relevant when dealing with the fault tolerance aspect. Moreover, threshold optimization based on the quantity of information given by a decision on the true hypothesis is highlighted.

Collaborative Localization for Multi-Robot System with Fault Detection and Exclusion Based on the Kullback-Leibler Divergence

Multi-robot system attracted attention in various applications in order to replace the human operators. To achieve the intended goal, one of the main challenges of this system is to ensure the integrity of localization by adding a sensor fault diagnosis step to the localization task. In this paper, we present a framework able, in addition of localizing a group of robots, to detect and exclude the faulty sensors from the group with an optimized thresholding method. The estimator has the informational form of the Kalman Filter (KF) namely Information Filter (IF). A residual test based on the Kullback-Leibler divergence (KLD) between the predicted and the corrected distributions of the IF is developed. It is generated from two tests: the first acts on the means and the second deals with the covariance matrices. Thresholding using entropy based criterion and Receiver Operating Characteristics (ROC) curve are discussed. Finally, the validation of this framework is studied on real experimental data from a group of robots.

Fault tolerant multi-sensor fusion for multi-robot collaborative localization

In the last decades, the multi-robot system has been widely investigated in mission that cannot be achieved by using a single robot or in area presenting danger to human life. Each robot needs to have an accurate position estimation of itself and of the others in the team. In this paper, we present a framework for localizing a group of robots with sensors Fault Detection and Exclusion (FDE) step. The Collaborative Localization (CL) is formulated using the Information Filter (IF) estimator which is the informational form of the Kalman Filter (KF). Residual tests calculated in term of divergence between the priori and posteriori distributions of the IF are developed in order to perform the FDE step. These residuals are based on the Kullback-Leibler Divergence (KLD) and they are generated from two tests: One acts on the means, and the other acts on the covariance matrices of the probability data distributions. Optimal thresholding method using entropy criterion is discussed and developed. Finally, the validation of this framework is studied on real experimental data from a group of robots.