Abstract-In this paper, we consider the problem of robot tracking and navigation toward a moving goal. The goal's maneuvers are not a priori known to the robot. Thus, off-line strategies are not effective. To model the robot and the goal, we use geometric rules combined with kinematics equations expressed in a polar representation. The intent of the strategy is to keep the robot between a reference point, called the observer, and the goal. We prove under certain assumptions that the robot navigating using this strategy reaches the moving goal successfully. In the presence of obstacles, the method is combined with an obstacle avoidance algorithm. The robot then moves in two modes, the navigation mode and the obstacle avoidance mode. Simulation of various scenarios highlights the efficiency of the method and provides an instructive comparison between the paths obtained for different reference points.Index Terms-Line of sight guidance law, relative kinematics equations, robotic navigation toward a moving goal, tracking.

Abstract-This paper deals with the problem of modeling and controlling a robotic convoy. Guidance laws techniques are used to provide a mathematical formulation of the problem. The guidance laws used for this purpose are the velocity pursuit, the deviated pursuit, and the proportional navigation. The velocity pursuit equations model the robot's path under various sensors based control laws. A systematic study of the tracking problem based on this technique is undertaken. These guidance laws are applied to derive decentralized control laws for the angular and linear velocities. For the angular velocity, the control law is directly derived from the guidance laws after considering the relative kinematics equations between successive robots. The second control law maintains the distance between successive robots constant by controlling the linear velocity. This control law is derived by considering the kinematics equations between successive robots under the considered guidance law. Properties of the method are discussed and proven. Simulation results confirm the validity of our approach, as well as the validity of the properties of the method.

This paper deals with a method for robot navigation towards a moving goal. The goal maneuvers are not a priori known to the robot. Our method is based on the use of the kinematics equations of the robot and the goal combined with geometrical rules. First a kinematics model for the tracking problem is derived and two strategies are suggested for robot navigation, namely the velocity pursuit guidance law and the deviated pursuit guidance law. It turns out that in both cases, the robot's angular velocity is equal to the line of sight angle rate. Important properties of the navigation strategies are discussed and proven. In the presence of obstacles, two navigation modes are used: the tracking mode, which has a global aspect and the obstacle avoidance mode, which has a local aspect. In the obstacle avoidance mode, a polar diagram combining information about obstacles and directions corresponding to the pursuit is constructed. An extensive simulation study is carried out, where the efficiency of both strategies is illustrated for different scenarios.

Twitter popularity has increasingly grown in the last few years making influence on the social, political and business aspects of life. Therefore, sentiment analysis research has put special focus on Twitter. Tweet data have many peculiarities relevant to the use of informal language, slogans, and special characters. Furthermore, training machine learning classifiers from tweets data often faces the data sparsity problem primarily due to the large variety of Tweets expressed in only 140-character. In this work, we evaluate the performance of various classifiers commonly used in sentiment analysis to show their effectiveness in sentiment mining of Twitter data under different experimental setups. For the purpose of the study the Stanford Testing Sentiment dataset STS is used. Results of our analysis show that multinomial Naïve Bayes outperforms other classifiers in Twitter sentiment analysis and is less affected by data sparsity.

In this paper, we present a method for robot navigation toward a moving object with unknown maneuvers. Our strategy is based on the integration of the robot and the target kinematics equations with geometric rules. The tracking problem is modeled in polar coordinates using a two-dimensional system of differential equations. The control law is then derived based on this model. Our approach consists of a rendezvous course, which means that the robot reaches the moving goal without following its path. In the presence of obstacles, two navigation modes are integrated, namely the tracking and the obstacle-avoidance modes. To confirm our theoretical results, the navigation strategy is illustrated using an extensive simulation for different scenarios.