The Expanded Guide Circle (EGC) method has been originally proposed as the guidance navigation method for improving the efficiency of the remote operation using the sensory information. The previous algorithm is, however, concerned only for the omni-directional mobile robot, so it needs to suggest a suitable one for a mobile robot with non-holonomic constraints. The ego-kinematic transform is a method to map points of R2 into the ego-kinematic space which implicitly represents non-holonomic constraints for admissible paths. Thus, robots with non-holonomic constraints in the ego-kinematic space can be considered as “free-flying object”. In this paper, we propose an effective obstacle avoidance method for mobile robots with non-holonomic constraints by applying EGC method in the ego-kinematic space using the ego-kinematic transformation. This proposed method shows that it works better for non-holonomic mobile robots such as differential-drive robot than the original one. The simulation results show its effectiveness of performance.

In this paper, we propose a method for estimating the pose of the camera using a rectangle feature utilized for the visual SLAM. A warped rectangle feature as a quadrilateral in the image by the perspective transformation is reconstructed by the Coupled Line Camera algorithm. In order to fully reconstruct a rectangle in the real world coordinate, the distance between the features and the camera is needed. The distance in the real world coordinate can be measured by using a stereo camera. Using properties of the line camera, the physical size of the rectangle feature can be induced from the distance. The correspondence between the quadrilateral in the image and the rectangle in the real world coordinate can restore the relative pose between the camera and the feature through obtaining the homography. In order to evaluate the performance, we analyzed the result of proposed method with its reference pose in Gazebo robot simulator.

We propose a navigation algorithm using image-based visual servoing utilizing a fixed camera. We define the mobile robot navigation problem as an unconstrained optimization problem to minimize the image error between the goal position and the position of a mobile robot. The residual function which is the image error between the position of a mobile robot and the goal position is generally large for this navigation problem. So, this navigation problem can be considered as the nonlinear least squares problem for the large residual case. For large residual, we propose a method to find the second-order term using the secant approximation method. The performance was evaluated using the simulation.