Volume 23, Issue 2 - Special Issue: The 2013 IEEE International Conference on Networking, Sensing and Control (ICNSC) / April 10–12, 2013, Paris, France

In this article, we present our work to provide a navigation and localization system on a constrained humanoid platform, the NAO robot, without modifying the robot sensors. First, we tried to implement a simple and light version of classic monocular Simultaneous Localization and Mapping (SLAM) algorithms, while adapting to the CPU and camera quality, which turned out to be insufficient on the platform for the moment. From our work on keypoints tracking, we identified that some keypoints can be still accurately tracked at little cost, and used them to build a visual compass. This compass was then used to correct the robot walk because it makes it possible to control the robot orientation accurately.

One of the important issues in mobile robots is finding the position of robots in space. This is normally achieved by using a sensor to locate the position of the robot. However, relying on more than one sensor and then using multisenor data fusion algorithms tends to be more reliable than just using a reading from a single sensor. If these sensors provide inconsistent data, catastrophic fusion may occur, and thus the estimated position of the robot obtained will be less accurate than if an individual sensor is used. This article uses an approach that relies on combining modified Bayesian fusion algorithm with Kalman filtering to estimate the position of a mobile robot. Two case studies are presented to prove the efficiency of the proposed approach in estimating the position of a mobile robot. Both scenarios show that combining fusion with filtering provides an accurate estimate of the location of the robot by handling the problem of uncertainty and inconsistency of the data provided by the sensors.

The present article deals with an observer design for nonlinear vehicle lateral dynamics. The contributions of the article concern the nonconsideration of any force model and the consideration that the longitudinal velocity is time varying, which is more realistic than the assumption that it is constant. The vehicle model is then represented by an exact Takagi–Sugeno (TS) model via the sector nonlinearity transformation. A proportional multiple integral (PMI) observer based on the TS model is designed to estimate simultaneously the state vector and the unknown input (lateral forces and road curvature). The convergence conditions of the estimation error are expressed under LMI formulation using the Lyapunov theory, which guaranties a bounded error. Simulations are carried out for comparison between the conventional PI observer, the enhanced PI observer, and the PMI observer. Finally, experimental results are provided to illustrate the performances of the proposed PMI observer.

Since the use of systems of satellite positioning such as the global positioning system (GPS), applications have tried to locate vehicles on maps representing the environment with their attributes. For one decade, this has led to both localization and navigation services for users. Recently, new researches have begun in order to extend the functionalities of the existing systems and thus to develop new applications using these technologies in the design of driver assistance systems. These new systems will indeed allow us to anticipate road departures or prevent overspeed turn approaches. Nevertheless, to deploy such new functionalities, it is imperative to ensure the association of vehicle position with one of the roadmap segments. In this article, we propose a new approach based on the belief theory taking into account the imperfections of available data in order to ensure the positioning and tracking of a vehicle on a roadmap and to manage conflicts and ambiguities using a multi-hypotheses decision.

In a context of global change, inland navigation transport has gained interest with economic and environmental benefits. The development of this means of conveyance requires the improvement of its management rules to deal with the increase of navigation (schedules and frequency) and the potential impact of global change. To achieve this aim, it is first necessary to have a better knowledge about the dynamics of inland navigation networks and their interaction with the environment. Second, the potential effects of global change have to be anticipated. This article focuses on the modeling of inland navigation reaches. An inland navigation network is a large-scale distributed system composed of several interconnected reaches. These reaches are characterized by non-linearities, time delays, and generally no significant slope. To deal with these particularities, a gray-box model is proposed. It consists in determining the delays according to the physical characteristics of the system. The parameters of the model are identified with measured data. The gray-box model is used to reproduce the dynamics of the Cuinchy–Fontinettes reach located in the north of France.

In this article, a new adaptive fuzzy wavelet neural network (AFWNN) model is proposed for nonlinear function approximation problems. The AFWNN model is based on the traditional Takagi-Sugeno-Kang fuzzy system. Specifically, this model replaces the membership functions of fuzzy rules with wavelet basis functions, which are known to have time and frequency localization properties, i.e., they can approximate patterns both in the time and frequency domains. The structure of the AFWNN model is derived from that of the adaptive neuro-fuzzy inference system (ANFIS). However, the AFWNN improves over the ANFIS by replacing Gaussian functions in the hidden layer with wavelet basis functions. The AFWNN model is trained using a gradient-based optimization algorithm. The AFWNN is then tested on three function approximation problems of time series prediction. For certain types of nonlinear time series, for instance fractal processes, the AFWNN is found to be substantially more accurate than alternative methods.

Mining time series data is of great significance in various areas. To efficiently find representative patterns in these data, this article focuses on the definition of a valid dissimilarity measure and the acceleration of partitioning clustering, a common group of techniques used to discover typical shapes of time series. Dissimilarity measure is a crucial component in clustering. It is required, by some particular applications, to be invariant to specific transformations. The rationale for using the angle between two time series to define a dissimilarity is analyzed. Moreover, our proposed measure satisfies the triangle inequality with specific restrictions. This property can be employed to accelerate clustering. An integrated algorithm is proposed. The experiments show that angle-based dissimilarity captures the essence of time series patterns that are invariant to amplitude scaling. In addition, the accelerated algorithm outperforms the standard one as redundancies are pruned. Our approach has been applied to discover typical patterns of information diffusion in an online social network. Analyses revealed the formation mechanisms of different patterns.