Distributed 3D TSDF Manifold Mapping for Multi-Robot Systems

International audienceThis paper presents a new method to perform collaborative real-time dense 3D mapping in a distributed way for a multi-robot system. This method associates a Truncated Signed Distance Function (TSDF) representation with a manifold structure. Each robot owns a private map which is composed of a collection of local TSDF sub-maps called patches that are locally consistent. This private map can be shared to build a public map collecting all the patches created by the robots of the fleet. In order to maintain consistency in the global map, a mechanism of patch alignment and fusion has been added. This work has been integrated in real-time into a mapping stack, which can be used for autonomous navigation in unknown and cluttered environment. Experimental results on a team of wheeled mobile robots are reported to demonstrate the practical interest of the proposed system, in particular for the exploration of unknown areas

Model-based prognosis of fatigue crack growth under variable amplitude loading

International audienceIn this paper, a model-based prognosis method using a particle filter that takes model uncertainty, measurement uncertainty and future loading uncertainty into account is proposed. A nonlinear analytical model of the degradation that depends on loading parameters is established, and then a particle filter is used to estimate and forecast these unknown inputs at the same time as the degradation state. Moreover, adding to this joint input-state estimation, a two-sided CUSUM algorithm is implemented to detect load variations. This would help the prognosis module to adapt to a change in the degradation state evolution, in order to correct the remaining useful life prediction. Real data from fatigue tests on fiber-reinforced metal matrix composite materials are used to demonstrate the efficiency of the proposed methodology for crack growth prognosis

Interval observer design for unknown input estimation of linear time-invariant discrete-time systems

International audienceIn this paper, the problem of joint state and unknown input estimation for linear time-invariant (LTI) discrete-time systems using interval observer is addressed. This problem has already been studied in the context of continuous-time systems. To the best of our knowledge, unknown input interval-based estimation for discrete-time systems has not been considered in the litterature. Assuming that the measurement noise and disturbances are bounded, lower and upper bounds are first computed for the unmeasured state and then for the unknown inputs. The results obtained with a numerical example highlight the efficiency of the method

Model-based prognosis using an explicit degradation model and Inverse FORM for uncertainty propagation

International audienceIn this paper, an analytical method issued from the field of reliability analysis is used for prognosis. The inverse first-order reliability method (Inverse FORM) is an uncertainty propagation method that can be adapted to remaining useful life (RUL) calculation. An extended Kalman filter (EKF) is first applied to estimate the current degradation state of the system, then the Inverse FORM allows to compute the probability density function (pdf) of the RUL. In the proposed Inverse FORM methodology, an analytical or numerical solution to the differential equation that describes the evolution of the system degradation is required to calculate the RUL model. In this work, the method is applied to a Paris fatigue crack growth model, and then compared to filter-based methods such as EKF and particle filter using performance evaluation metrics (precision, accuracy and timeliness). The main advantage of the Inverse FORM is its ability to compute the pdf of the RUL at a lower computational cost

Learning viewpoint planning in active recognition on a small sampling budget: a Kriging approach

International audienceThis paper focuses on viewpoint planning for 3D active object recognition. The objective is to design a planning policy into a Q-learning framework with a limited number of samples. Most existing stochastic techniques are therefore inapplicable. We propose to use Kriging and Bayesian Optimization coupled with Q-learning to obtain a computationally-efficient viewpoint-planning design, under a restrictive sampling budget. Experimental results on a representative database, including a comparison with classical approaches, show promising results for this strategy

Cooperative fault detection and isolation in a surveillance sensor network: a case study

International audienceThis work focuses on Fault Detection and Isolation (FDI) among sensors of a surveillance network. A review of the main characteristics of faults in sensor networks and the associated diagnosis techniques is first proposed. An extensive study has then been performed on the case study of the persistent monitoring of an area by a sensor network which provides binary measurements of the occurrence of events to be detected (intrusions). The performance of a reference FDI method with and without simultaneous intrusions has been quantified through Monte Carlo simulations. The combination of static and mobile sensors has also been considered and shows a significant performance improvement for the detection of faults and intrusions in this context

Worst-case global optimization of black-box functions through Kriging and relaxation

International audienceA new algorithm is proposed to deal with the worst-case optimization of black-box functions evaluated through costly computer simulations. The input variables of these computer experiments are assumed to be of two types. Control variables must be tuned while environmental variables have an undesirable effect, to which the design of the control variables should be robust. The algorithm to be proposed searches for a minimax solution, i.e., values of the control variables that minimize the maximum of the objective function with respect to the environmental variables. The problem is particularly difficult when the control and environmental variables live in continuous spaces. Combining a relaxation procedure with Kriging-based optimization makes it possible to deal with the continuity of the variables and the fact that no analytical expression of the objective function is available in most real-case problems. Numerical experiments are conducted to assess the accuracy and efficiency of the algorithm, both on analytical test functions with known results and on an engineering application

A new strategy for worst-case design from costly numerical simulations

International audienceWorst-case design is important whenever robustness to adverse environmental conditions should be ensured regardless of their probability. It leads to minimax optimization, which is most often considered assuming that a closed-form expression for the performance index is available. In this paper, we consider the important situation where this is not the case and where evaluation of the performance index is via costly numerical simulations. In this context, strategies to limit the number of these evaluations are of paramount importance. This paper describes one such strategy, which further improves the performance of an algorithm recently presented that combines the use of a relaxation procedure for minimax search and Kriging-based efficient global optimization. Test cases from the literature demonstrate the interest of the approach

Robust automatic tuning of diagnosis methods via an efficient use of costly simulations

International audienceThe robust tuning methodology developed in this paper aims at adjusting automatically the hyperparameters of fault-diagnosis procedures for complex case studies. The strategy should make an efficient use of computer simulations of these case studies, which will usually be computationally expensive. To this end, Kriging-based optimization is called upon. Robustness to environmental disturbances is achieved by continuous minimax optimization, and handled through an iterative relaxation procedure. This strategy is applied to the automatic tuning of a model-based fault-diagnosis scheme for a realistic aerospace application

Navigation 3D d'un UAV avec évitement d'obstacles à l'aide des fonctions de Lyapunov barrières

International audienceWe address the safe-navigation problem for aerial robots in the presence of mobile obstacles. Our approach relies on an original dynamic model defined in a cylindrical-coordinate space. It is assumed that the environment contains moving obstacles, that are encoded as state constraints so that they are embedded in the control design: the controller is constructed so as to generate a force field which, in turn, is derived from a potential with negative gradient in the vicinity of stable equilibria and positive gradient in the vicinity of obstacles. In particular, we combine the so-called Barrier Lyapunov Functions (BLF) method with the backstepping technique to obtain a smooth time-invariant controller. It is guaranteed that the robot reaches its destination from any initial condition in the valid workspace (that is, the environment stripped of the obstacles' safety neighborhoods) while avoiding collisions. Furthermore, the performance of our control approach is illustrated via simulations and experiments on a quadrotor benchmark.Nous abordons le problème de la navigation sécurisée pour des robots aériens en présence d'obstacles mobiles. Notre approche repose sur un modèle dynamique original défini dans un espace de coordonnées cylindriques. Il est supposé que l'environnement contient des obstacles mobiles, qui sont définis en tant que contraintes d'état, de manière à être intégrés dans la conception de la commande : le contrôleur est construit de manière à générer un champ de force qui, à son tour, est dérivé d'un potentiel à gradient négatif au voisinage des équilibres stables et de gradient positif au voisinage des obstacles. En particulier, nous combinons la méthode dite des fonctions de Lyapunov barrières (BLF) avec la technique du backstepping pour obtenir une commande lisse et invariante dans le temps. Il est garanti que le robot atteigne sa destination à partir de n’importe quelle condition initiale dans l’espace de travail valide (c'est-à-dire, l'espace de travail sans les zones de sécurité des obstacles) tout en évitant les collisions. De plus, la performance de notre approche de contrôle est illustrée via des simulations et des expériences sur des quadrotors