Nowadays, managers are looking for adequate strategies and advanced logistics management models in order to achieve their competitiveness and improve their overall performance. The current literature on supply network planning has not adequately incorporated sustainability factors, uncertainties, and the integration of various levels of planning (operational, tactical and strategic). In this paper, we address the integrated planning of logistics networks in an uncertain and sustainable environment. The problem is formulated as a two-stage stochastic programming which minimizes the logistics costs and evaluates a posteriori the CO2 emissions and the transport accident risk. This model is solved using the Sample Average Approximation (SAA) approach. Numerical experiments are performed using CPLEX to study the impact of different parameters on sustainability aspects.

In this paper, we deal with an original advanced driver assistance system (ADAS) based on the use of omnidirectional vision and an evidential fusion architecture. The panoramic perception solution permits us to address efficiently the problem of close vehicles detection but also the monitoring side traffic system. The fusion and integration of this sensorial data stream is assumed by a credibilist architecture based on the transferable belief model (TBM) of SMETS. This paradigm permits the filtering of false alarms efficiently by an optimal management of the uncertainties estimation.

This paper deals with the design of a robotic platform providing a grasping procedure to assist disabled persons. Like most of the vision-based systems, the camera used to guide the arm toward the object is embedded onto the gripper, which gives a very local view of the environment. We propose to use a catadioptric sensor in order to deal with situations where the object of interest is out of the embedded camera field of view. The mirror used has been especially designed for our robot, satisfying several constraints of the final application. The solution proposed respects one of the main matters of all systems designed for disabled persons, reduce the user strain. A single click is necessary to position the robotic arm toward the direction of the object. Clinical evaluations realized during one month in four medical centers with about 40 users confirm the validity of our system.

Firstly this article presents a multi-level architecture permitting the localization of a mobile platform and secondly an incremental construction of the environment's map. The environment will be modeled by an occupancy grid built with information provided by the stereovision system situated on the platform. The reliability of these data is introduced to the grid by the propagation of uncertainties managed thanks to the theory of the Transferable Belief Model.

This article concerns road safety and driving assistance. To solve this problem, we propose a data fusion architecture based on the Dempster-Shafer theory. This multilevel approach allows the management of complementary and redundant data which come from two perception systems: an omnidirectional vision sensor and a laser telemeter. The originality of this architecture is its ability to manage and propagate uncertainties from low level data until a high level information of danger given to the driver. The first part concerns the data sensor. The second part deals with the quantification of the uncertainties of the detected vehicles, followed by a determination of situations of danger and the evaluation of their level of dangerousness with the aim of supplying the driver with an indicator of global danger around the vehicle.

This article deals about uncertainty and imprecision treatment during the mobile robot localization process. The imprecision determination is based on the use of the interval formalism. The mobile robot is equipped with an exteroceptive sensor and odometers. The imprecise data given by these two sensors are fused by constraint propagation on intervals. At the end of the algorithm, we get 3D localization subpaving which is supposed to contain in a guaranteed way the robot’s position. Concerning the uncertainty, it is managed throw a propagation architecture based on the used of the Transferable Belief Model of Smets.

Robotic researches can contribute to the restoration of some functions lost by handicapped people. The over-cost generated by the additive potentialities must be affordable and related to the value of the usual product. In most cases, autonomous functions are direct transpositions of solutions applied in industrial robotics. If we consider that in addition with cost, security is a supplementary constraint of rehabilitation robotics, an important research effort is needed to propose technological components. The aim of this paper is to introduce a functionality within the context of the following plan: "technical achievement and psychological analysis of a master/slave robot assistance for the invalid person" (HTSC: human/technology complex system). This application allows a severely handicapped person to handle at the same time a movable platform and a Manus/spl reg/ arm. In this paper, we will present an "automatic tracking" functionality we developed in order to keep the Manus/spl reg/ arm automatically close to the patient.

In this article, a multi-sensor cooperation approach between an omnidirectional vision system and a panoramic range finder is presented. The aim is to localize dynamically a mobile robot. These two sensors provide some complementary and redundant data which enable to construct a robust sensorial model which integrates all important number of significant primitives. Based on this model, we treat the problem of maintaining a matching and propagating uncertainties on each matched primitive. Indeed, our localization method is based on the tracking of significant landmarks of the environment and integrates a multilevel uncertainty propagation stage based on the use of the Dempster-Shafer theory.

This paper deals with the localization and map building paradigm in an unknown indoor environment. We propose an exploration method based on the use of the sensorial data provided by an omnidirectional stereoscopic vision system. First, we link the problem of sensorial model construction with two omnidirectional images. We propose an approach based on the fusion of several criteria, which is realized according to Dempster-Shafer rules. We then deal with the matching problem of the stereo sensorial model with an environment map integrating all the previous primitive observations. We propose two matching approaches based on different selection criteria: the Hausdorff distance and the cumulated Cartesian distance. We also present our incremental map building paradigm based on the hypothesis of a non a priori knowledge. Finally, we deal with the problem of allowing a robot to localize itself and to construct concurrently a representation of its environment.

This paper deals with an absolute localization paradigm based on the cooperation of an omnidirectional vision system and a low cost panoramic range finder system. These two perception systems used independently by our laboratory provide some complementary data. This association permits us to build a robust sensorial model which integrates an important number of significant primitives. The obtained sensorial model allows one to realize an absolute localization of the mobile robot, in particular, configurations where it is not possible to determine the position with the use of one of the two sensors. First, we present our global perception system. Next, we deal with our sensorial model building approach, and finally we present an absolute-localization method which uses a Dempster-Shafer approach. We analyze the performance of our global absolute localization system on several robot's elementary moves, in an indoor environment.

In this paper we present a method of calibrating the omnidirectional sensor used in our laboratory and named SYCLOP ("Conic System for Localization and Perception"). In order to have better precision than that obtained in classical applications using this kind of sensor, we highlight the importance of calibration for the whole sensor. After this step we validate our model by a reconstruction of objects, and of an environment from two different methods, the first based on a commercial software 3DS Max/sup (R)/ and the second based on a simulator developed by us and taking parameters determined in the previous stage. The real and calculated pictures are compared, to estimate the error of reconstruction. The last part of this paper presents results obtained with the SYCLOP simulator and an experimental phase.

In this paper, an absolute localization paradigm based on the cooperation of an omnidirectional vision system composed of a conical mirror and a CCD camera and a low cost panoramic range finder system is reported. These two sensors, which have been used independently until now, provide some complementary data. This association enables us to build a robust sensorial model which integrates an important number of significant primitives. We can thus realize an absolute localization of the mobile robot in particular configurations, like symmetric environments, where it is not possible to determine the position with the use of only one of the two sensors. In a first part, we present our global perception system. In a second part, we describe our sensorial model building approach. Finally we present an absolute localization method which uses three matching criteria fused thanks to the combination rules of the Dempster-Shafer theory. The basic probability assignment got for each primitive matching enables to estimate the reliability of the localization. We test our global absolute localization system on several robots' elementary moves in an indoor and symmetric environment.

Presents a system of absolute localization based on the stereoscopic omnidirectional vision. To do it we use an original perception system which allows our omnidirectional vision sensor SYCLOP to move along a rail. The first part of our study deals with the problem of building the sensorial model with the help of the two stereoscopic omnidirectional images. To solve this problem we propose an approach based on the fusion of several criteria which will be made according to Dempster-Shafer rules. The second part is devoted to exploiting this sensorial model to localize the robot thanks to matching the sensorial primitives with the environment map. We analyze the performance of our global absolute localization system on several robot elementary moves, in different environments.

Presents results for state estimation based on noisy observations suffering from two different types of uncertainties. The first uncertainty is a stochastic process with given statistics. The second uncertainty is only known to be bounded, the exact underlying statistics are unknown. State estimation tasks of this kind typically arise in target localization, navigation, and sensor data fusion. A new estimator has been developed, that combines set theoretic and stochastic estimation in a rigorous manner. The estimator is efficient and, hence, well-suited for practical applications. It provides a continuous transition between the two classical estimation, concepts, because it converges to a set theoretic estimator, when the stochastic error goes to zero, and to a Kalman filter, when the bounded error vanishes. In the mixed noise case, the new estimator provides solution sets that are uncertain in a statistical sense.

This paper presents a system of absolute localization based on the cooperation of a stereoscopic omnidirectional vision system and a dead-reckoning system. To do it we use an original perception system which allows our omnidirectional vision sensor SYCLOP to move along a rail. We address the problem of building the sensorial model with the help of the two stereoscopic omnidirectional images. To solve this problem we propose an approach based on the fusion of several criteria which will be made according to Dempster-Shafer rules. We then exploit this sensorial model to localize the robot thanks to matching the sensorial primitives with the environment map. We use the dead-reckoning prediction to decrease the combinatory aspect of the matching algorithm. We analyze the performance of our global absolute localization system on several robots' elementary moves, in an indoor environment.

In this article we present a navigation system allowing a mobile robot to be localized in an indoor environment which is only partially known. This system integrates an environment map updating module allowing the mobile robot to estimate the position of new vertical landmarks along its path. An extended Kalman filter is used on the one hand to estimate the mobile robot position and on the other hand to extract observations which will be used to determine the positions of unlisted landmarks. The integration of new landmarks into the environment global map is managed from the covariance matrix associated with each unlisted landmark. We present the experimental results we have got with SARAH, our mobile robot.

In this paper we present a dynamic localization system which allows a mobile robot to evolve autonomously in a structured environment. Our system is based on the use of two sensors: an odometer and an omnidirectional vision system which gives a reference in connection with a set of natural beacons. Our navigation algorithm gives a reliable position estimation due to a systematic dynamic resetting. To merge the data obtained we use the extended Kalman filter. Our proposed method allows us to treat efficiently the noise problems linked to the primitive extraction, which contributes to the robustness of our system. Thus, we have developed a reliable and quick navigation system which can deals with the constraints of moving the robots in an industrial environment. We give the experimental results obtained from a mission realized in an a priori known environment.