In this paper, we propose a noble line-recognition algorithm of driving parking for intelligent vehicles by using images obtained from a bird’s eye view system. To achieve safe driving and parking of unmanned vehicles, we need to obtain noiseless and effective images around vehicles. In addition, fast image processing is a fundamental requirement for the real time recognition of lanes and obstacles to ensure safety. In fact, the number of sensors equipped with conventional unmanned vehicles is reluctant to their commercialization. To solve this problem, we propose a noble method to detect straight lines and turning curves in the images obtained from a bird’s eye view system. For conventional vehicles equipped with this bird’s eye view system, straight lines and turning curves are detected by using a Hough and trigonometric function. Since parking lines have the form of a rectangle or parallelogram, detection of their vertexs makes it possible to determinate parking areas. In the case of a parking space without parking lines, the parking space is detected by using a stereo algorithm after calculating the area for parking. The experimental results using the proposed algorithm show that, without additional sensors, the lines and the surrounding environment are detected for unmanned driving and auto-parking.

This paper proposes an enhanced Simultaneous Localization and Mapping (SLAM) algorithm based on matching laser image and Extended Kalman Filter (EKF). In general, laser information is one of the most efficient data for localization of mobile robots and is more accurate than encoder data. For localization of a mobile robot, moving distance information of a robot is often obtained by encoders and distance information from the robot to landmarks is estimated by various sensors. Though encoder has high resolution, it is difficult to estimate current position of a robot precisely because of encoder error caused by slip and backlash of wheels. In this paper, the position and angle of the robot are estimated by comparing laser images obtained from laser scanner with high accuracy. In addition, Speeded Up Robust Features (SURF) is used for extracting feature points at previous laser image and current laser image by comparing feature points. As a result, the moving distance and heading angle are obtained based on information of available points. The experimental results using the proposed laser slam algorithm show effectiveness for the SLAM of robot.

This paper describes an advanced formation algorithm of clustered multiple robots for their navigation using flexible formation method for collision avoidance under static environment like narrow corridors. A group of clustered multiple robots finds the lowest path cost for navigation by changing its formation. The suggested flexible method of formation transforms the basic group of mobile robots into specific form when it is confronted by particular geographic feature. In addition, the proposed method suggests to choose a leader robot of the group for the obstacle avoidance and path planning. Firstly, the group of robots forms basic shapes such as triangle, square, pentagon and etc. depending on number of robots. Secondly, the closest to the target location robot is chosen as a leader robot. The chosen leader robot uses A* for reaching the goal location. The proposed approach improves autonomous formation characteristics and performance of all system.

This paper proposes a novel method based on error back propagation neural networks to fuse laser sensor data and ultrasonic sensor data for enhancing the accuracy of mapping. For navigation of single robot, the robot has to know its initial position and accurate environment information around it. However, due to the inherent properties of sensors, each sensor has its own advantages and drawbacks. In our system, the robot equipped with seven ultrasonic sensors and a laser sensor navigates to map two different corridor environments. The experimental results show the effectiveness of the heterogeneous sensor fusion using an error backpropagation algorithm for mapping.

In mobile robotics, ultrasonic sensors became one of the most popular devices for collision avoidance and navigation primarily due to data robustness, the easy availability of low-cost systems, their compact size, simple circuits, and their ease in interfacing with computers. However, ultrasonic sonic sensors are subject to noise which results in inaccuracy of mapping and localization of the robot. This paper introduces a new approach to enhance environmental maps based on ultrasonic range data using linear interpolation and Newton interpolation. The simulation and experimental results show that the proposed method improves of the accuracy of the map through better distance estimation between the mobile robot and obstacles.

This paper proposes a novel path finding approach of a mobile robot using RF strength in sensor network. In the experiments based on the proposed method, a mobile robot attempts to find its location, heading direction and the shortest path in the indoor environment. The experimental system consisting of mesh network shares node data and send them to base station. The triangulation and the proposed Grid method calculate the location and heading angle of the robot. In addition, the robot finds the shortest path by using the base station attached on it to receive data of environment around each node. Kalman filter reduces the straight line error when the robot estimates the strength of received signal. The experimental results show the effectiveness of the proposed algorithm.

In this paper, a novel rotor flux estimation method of an induction motor using support vector regression(SVR) is presented. Two well-known different flux models with respect to voltage and current are necessary to estimate the rotor flux of an induction motor. Training of SVR which the theory of the SVR algorithm leads to a quadratic programming(QP) problem. The proposed SVR rotor flux estimator guarantees the improvement of performance in the transient and steady state in spite of parameter variation circumstance. The validity and the usefulness of proposed algorithm are throughly verified through numerical simulation.