Environment perception is a crucial ability for robot's interaction into an environment. One of the first steps in this direction is the combined problem of simultaneous localization and mapping (SLAM). A new method, called G-SLAM, is proposed, where the map is considered as a set of scattered points in the continuous space followed by a probability that states the existence of an obstacle in the subsequent point in space. A probabilistic approach with particle filters for the robot's pose estimation and an adaptive recursive algorithm for the map's probability distribution estimation is presented. Key feature of the G-SLAM method is the adaptive repositioning of the scattered points and their convergence around obstacles. In this paper the goal is to estimate the best robot trajectory along with the probability distribution of the obstacles in space. For experimental purposes a four wheel rear drive car kinematic model is used and results derived from real case scenarios are discussed.

 The control problem for the serpentine motion of a planar snake under the assumption of a trajectory characterized by its curvature and heading is examined in this article. The time varying curvature and heading attributes of the trajectory result in a sinusoidal reference signal for the joint angles. An inner loop PD-controller is used for trajectory tracking by compensating the effects of the snake's dynamics, while an outer loop first-order controller is used for the formation of the reference joint angles by tracking the desired heading and velocity. Simulation studies on spiral curves are included to investigate the efficiency of the controller.

 Wall following consists in navigating at a constant distance and orientation from a certain surface to be inspected. This task is required in many applications of underwater vehicles (e.g. ship hull or hydraulic dam inspection). We propose a nonlinear state feedback, designed for a specific embedded acoustic sensing system. The proposed method and the associated sensor can work on very small low cost underwater vehicles at high sampling rates. Sensor experiments show the ability of a single beam transducer to detect both distance and orientation of a wall, while simulation results show how this sensing system can be successfully integrated in a nonlinear state feedback controller to perform autonomous wall following with an underwater vehicle, even with disturbances.

 This paper proposes a new algorithm for following a mobile robot target in an outdoor environment. This approach combines a fuzzy tracking control law and an inverse matching based algorithm for finding and following of the moving target. The inverse matching algorithm comprises local maps of a robot environment to identify the target and generate desire path toward it. The localization of the mobile robot is done by the triangulation method based on GPS measurements. In addition, previously developed software system for the mobile robot enables GPS guidance and obstacle avoidance which makes it suitable for creation of mobile convoys. Finally, the experimental verification is performed to confirm the effectiveness and practical value of the proposed approach.

 The coverage problem has been traditionally solved for a given number of robots with randomly generated positions. However, our recent work presented a solution to the parallel coverage problem that optimizes the number of robots starting at the same location. The motivations are: (i) the number of involved robots affects the total coverage cost, and (ii) it requires extra effort to place them at real-world locations. In this work we present a control algorithm for multiple robots with Kinect systems to implement the solution to the parallel coverage problem. Our algorithm utilizes a multi-robot formation. Robots need to localize themselves to know where they are within a map. To localize the robots and to reduce inter-communication, we introduce a technique to place only certain robots in a team. This work also presents an algorithm on how to manage dynamic changes of a group of formations in order to solve the coverage problem. This paper demonstrates the mission, which is to visit every desired position to cover an indoor environment, with a team of real robots and the Kinect system.