This thesis describes a vision-based method for ROV¡¦s underwater pipeline recognition task. In this research, we tried to overcome the poor image quality of the underwater circumstance and the condition when seaweed is in the scene. Edge information and line feature of the pipeline are used in this method. Edge image is obtained after preprocessing to extract line feature. In this thesis we focused on the recognition of pipeline, trying to provide useful navigation information for further development of the ROV¡¦s control system.

In this thesis, an image-based uncalibrated visual servo is proposed for image tracking tasks in highly disturbed environment, such as a remotely operated vehicle performing observing or investigation objects under the influence of undersea current. For the conditions that the target model and the camera parameters are unknown, the control framework applies the scale invariant feature transform (SIFT) to extract image features. Furthermore, a robust adaptive control law is implemented to overcome the effect caused by camera calibration parameters. Then by using three different types of camera¡¦s motion: pan, tilt, and zoom to maintain the target always at the central position on the image plane.

The study and application of machine vision in early years mostly focus on a single camera. However, the trend of research on multiple cameras has been developed recently. Due to highly complicated correlation among multiple images, the arrangement of multiple cameras was restricted to the encirclement layout for acquiring more than one views of a target object. Furthermore, it has been well known that the special architecture of insect compound eyes contributes outstanding capability for precise and efficient observation of moving objects. If this technique can be transferred to the domain of engineering applications, significant improvement on visual tracking of moving objects will be greatly expected. This thesis builds a visual servo system with trinocular cameras by mimicking the configuration of compound eye of insects for tracking an object moving in 2D space. The arrangement of the trinocular cameras is divergent, and this system can function properly without the information of distance between the object and the cameras.

Optical flow, caused by relative motion of the object and the viewer, is the distribution of apparent velocities of brightness pattern in an image. The advantage of the optical-flow-based visual servo method is that feature of the object does not need to be defined or known in advance. This research plans to build an image servo technique to deal with the problem of 3D relative motion of the viewer and the environment. The images are treated as input and output signals of the control system and are fed back to extract the relative velocity information between contiguous image patterns. Then the video camera will automatically follow the motion to maintain the target image unchanged.

The brightness variance, caused by relative velocity of the camera and environment in a sequence of images, is called optical flow. The advantage of the optical-flow-based visual servo method is that feature of the object dose not need to be known in advance. Therefore, it can be applied for positioning and tracking implement tasks. The purpose of this thesis is to implement the image servo technique and the sliding-mode control method to track an unknown image pattern in three dimensional motion. The goal of tracking is to maintain identical image captured by the camera based on the relative movement calculated from the optical flow.

A monocular visual servo system for a target with variable shape has been developed in this paper. It consists of two parts: an image-processing unit and a servo control unit. For the image-processing unit, the motion between the target and image center is determined by a template match approach. The image is grabbed by the camera equipped on a Pan-Tilt robot and the robot is controlled to track the target by maintaining the target on the image center. However, the template needs to be updated when the target deforms. For the servo control unit, the movement is estimated by the Kalman filter technique to enhance the tracking performance of the visual servo system.

This thesis presents a robust real-time active tracking system with a pan-tilt camera. The proposed visual servo framework is able to track a deformed object and maintain the target always inside the field of view. For the image processing, an efficient template matching and searching method using the mean-shift theory is developed. The robustness is achieved by appending the ratio histogram, a kernel function, and the template update to the framework when the target is deformed. Then the pan-tilt unit turns towards the target and keeps the target inside the field of view of the camera by feeding back the position information to a Kalman filter. Experimental results show that the presented scheme works successfully when the target is vague or concealed or deformed. The visual tracking task can also be accomplished even when a similar object crosses over the target. In addition, the refreshing rate can be up to 60 frames per second.

The optical flow has been one of the common approaches for image tracking. Its advantage is that no prior knowledge for image features is required. Since movement information can be obtained based on brightness data only, this method is suitable for tracking tasks of unknown objects. Besides, insects are always masters in chasing and catching preys in the natural world due to their unique compound eye structure. If the edge of the compound eye can be applied to tracking of moving objects, it is highly expected that the tracking performance will be greatly improved. Conventional images are built on a Cartesian reference system, which is quite different from the hexagonal framework for the compound eye of insects. This thesis explores the distinction of the hexagonal image framework by incorporating the hexagonal concept into the optical flow technology. Consequently, the reason behind why the compound eye is good at tracking moving objects can be revealed. According to simulation results for test images with different features, the hexagonal optical flow method appears to be superior to the traditional optical flow method in the Cartesian reference system.

This thesis presents a visual servo control scheme for a ball-and-plate system with a maze. The maze built on the plate forms obstacles for the ball and increases variety and complexity of its environment. The ball-and-plate system is a two degrees-of-freedom robotic wrist with an acrylic plate attached as the end effector. By using image processing techniques, the ball¡¦s position is acquired from the visual feedback, which was implemented with a webcam and a personal computer. A fuzzy controller, which provides dexterity of the robotic wrist, is designed to decide the slope angles of the plate to guide the ball to a designated target spot. Using the method of distance transform, the path planning based on the current position of the ball is conducted to find the shortest path toward the target spot. Besides, a relaxed path, appears to be more suitable for actual applications, is provided by the obstacle¡¦s expansion approach. Experimental results show that the presented control framework successfully leads the ball to pass through the maze and arrive at target spot. The visual servo control scheme works effectively in both stabilization and tracking control. Based on this preliminary achievement, further improvement and deeper exploration on related research topics can be carried on in the future.

No / In this paper, we investigate trajectory tracking in a multi-input nonlinear system, where there is little knowledge of the system parameters and the form of the nonlinear function. An identification-based iterative learning control (ILC) scheme to repetitively estimate the linearity in a neighborhood of a desired trajectory is presented. Based on this estimation, the original nonlinear system can track the desired trajectory perfectly by the aid of a regional training scheme. Just like in adaptive control, a singularity exists in ILC when the input coupling matrix is estimated. Singularity avoidance is discussed. A new parameter modification procedure for ILC is presented such that the determinant of the estimate of the input coupling matrix is uniformly bounded from below. Compared with the scheme used for adaptive control of a MIMO system, the proposed scheme reduces the computation load greatly. It is used in a robotic visual system for manipulator trajectory tracking without any information about the camera-robot relationship. The estimated image Jacobian is updated repetitively and then its inverse is used to calculate the manipulator velocity without any singularity.