In this paper, a vision-based autonomous lane following system for a mobile robot is presented. To achieve the autonomous lane following, the lane images are obtained in real time using a camera fixed mounted on the robot. Lane position is presented based on lane boundary pixel extractor (LBPE) and Hough transform in the image coordinate frame, and transformed to the mobile robot coordinate frame using photogrammetric techniques. The visibility of the lane information is checked by the lane width constraints and its parallel property. Path planning of the mobile robot is performed based on mobile robot's position and orientation with respect to the lanes obtained from the vision information. Experimental results demonstrated the feasibility of the approach.

The objective of the work presented is to determine an accurate position and orientation for mobile robots based on information received from multiple sensors. The approach taken in this paper is to integrate the information from odometry with the inertial system using Unscented Kalman Filter (UKF). The UKF is the newest extension of widely used estimation method, Kalman Filter. The UKF is more accurate and simpler than the extended Kalman filter applied to nonlinear systems.

Monitoring of contact type is important to achieve tasks in which an end-effector holding an object makes contact with the constraint surface. The force exerted by the end-effector is usually controlled in a direction normal to the constraint surface, while the position of the end-effector is maintained in the tangential direction. In the paper, the position and force degrees of freedom are specified for constraint surfaces depending on the type of contact. Three types of contact (point, line, and plane) are considered. The type is determined based on the point of contact which is calculated from information received from a wrist force/torque sensor. Experimental results verify the technique for obtaining the type of contact. A reduced order position/force control method is then applied to effectively control the manipulator in orthogonal directions. An example is presented to demonstrate the technique.

The visual information obtained from a camera and an image processing unit are incorporated in an adaptive control algorithm to make a robotic manipulator grasp a moving object. Because of the inherent time delay caused by the image processing, the motion of the moving target is predicted in real time and is used in the online planning of the trajectory for the manipulator motion. Since the dynamics of the target are assumed to be unknown, the prediction is accomplished by means of an autoregressive discrete-time model. On the basis of the predicted motion of the object, the planner determines online at each control sampling instant the desired trajectory point (subgoal) for the controller. The subgoal point is tracked by controlling the end effector with self-tuner until grasping occurs. A simulation study and laboratory experiments are presented to demonstrate the performance of this visual feedback system.>

Consideration is given to grasping a moving target by adaptively controlling a robot manipulator. The adaptive controller is capable to compensate for the system modeling errors and unknown payloads without deterioration of its performance. Because of the time needed to extract information from the computer images, a time delay is inherent in the calculated position of the object. This can be circumvented by predicting the position of the object so that the end effector of the manipulator can grasp it. Since the dynamics of the object are assumed to be unknown, a prediction is performed based on an autoregressive (AR) model, which is determined using the information extracted from the computer images. The information is then utilized in the controller so that the manipulator will grasp the moving object. The details of the prediction scheme are given. Control of the manipulator is reviewed. A simulation study on a manipulator for grasping a moving target using vision information and a self-tuning controller is described. The practicality of the approach is demonstrated by the laboratory experiments presented.>

The task of raising a rectangular parallelopiped object on the plane surface to a standing position by a robot manipulator is considered. An initial contact between the object and the surface is assumed to be a point contact. To perform the task, the manipulator needs to be servoed to that the object position changes from a point contact to an edge (line) contact and then to a plane contact, using sensory feedback from tactile and force/torques sensors. After an initial contact, the object is rotated around the gripper approach vector till the object makes a line contact with the surface. The angle between the gripper-held object and the surface is then calculated from tactile sensory information. The position and orientation of the gripper are modified based on the current gripper position and orientation, and the angle calculated so as to place the object on the plane. A simple experiment is conducted to demonstrate the applicability of calculating the angle between the object and the surface from tactile sensory information.>

The force-position controller is designed for robotic manipulator in the Cartesian coordinate system. A time series model is used for the controller design. An adaptive controller with self-tuning is determined (i) by minimizing a well defined quadratic performance criterion (ii) by pole placement. Comparison is made between the performance of the two controller. Successful simulations of force-position control using both controllers with self-tuning are presented.